Art Machines (2019)

Richard  Allen

This paper is an attempt to formalise definitions of different types of performance common to computer-generated (CG) characters in feature films, and to create a taxonomy of modes of performance among these characters. Terms such as a “virtual actors” (vactors), “synthespians”, and “digital doubles” are frequently, but incorrectly, used interchangeably due to a lack of established definitions. What is the relationship of these terms to each other, and how should they be understood from both technological and performance-based perspectives? By articulating clear definitions for these terms, it is possible to theorise a variety of performance types specific to CG characters in feature films. These categories provide an expanded understanding of how performance is created among CG characters, and draws into question whether classic examples of synthespians are really synthespians at all.

From a small community of pioneering artists who experimented with artificial intelligence (AI) in the 1970s, AI art has expanded, gained visibility, and attained socio-cultural relevance since the second half of the 2010s. Its topics, methodologies, presentational formats, and implications are closely related to a range of disciplines engaged in the research and application of AI. In this paper, I present a comprehensive framework for the critical exploration of AI art. It comprises the context of AI art, its prominent poetic features, major issues, and possible directions. I address the poetic, expressive, and ethical layers of AI art practices within the context of contemporary art, AI research, and related disciplines. I focus on the works that exemplify poetic complexity and manifest the epistemic or political ambiguities indicative of a broader milieu of contemporary culture, AI science, technology, economy, and society. By comparing, acknowledging, and contextualizing both their accomplishments and shortcomings, I outline the prospective strategies to advance the field. The aim of this framework is to expand the existing critical discourse of AI art with new perspectives which can be used to examine the creative attributes of emerging practices and to assess their cultural significance and socio-political impact. It contributes to rethinking and redefining the art/science/technology critique in the age when the arts, together with science and technology, are becoming increasingly responsible for changing ecologies, shaping cultural values, and political normalization.

In this paper, the implementation of a robotic painting system using a sponge and the watercolor painting technique is presented. A collection of tools for calibration and sponge support operations was designed and built. A contour-filling algorithm was developed, which defines the sponge positions and orientations in order to color the contour of a generic image. Finally, the proposed robotic system was employed to realize a painting combining etching and watercolor techniques. To the best of our knowledge, this is the first example of robotic painting that uses the watercolor technique and a sponge as the painting media.

PROCEEDINGS Art Machines: International Symposium on Computational Media Art Proceedings Editor: Richard William Allen Co-Editors: Olli Tapio Leino, Malina Siu, Sureshika Piyasena Cover Design: Trilingua Copyright 2018 ©All rights reserved by the Individual Authors, School of Creative Media, City University of Hong Kong. No part of this publication may be reproduced, stored in a retrieval system, transmitted in any form or by any means, without prior written permission of the Individual Authors and Conference Director of Art Machines: International Symposium on Computational Media Art. Individual authors of papers and abstracts are solely responsible for all materials submitted for the publication. The publisher and the editors do not warrant or assume any legal responsibilities for the publication’s content. All reﬂec those of opinions expressed in the book are of the authors and do not reﬂect the publisher and the editors. Published by: School of Creative Media, City University of Hong Kong 81 Tat Chee Avenue, Kowloon Tong, Hong Kong Printed in Hong Kong ISBN: 978-962-442-421-8 Presented by: Sponsored by: Art Machines: International Symposium on Computational Media Art Organizing Committee Conference Director Richard William Allen School of Creative Media City University of Hong Kong Conference Co-Directors Machine Learning and Art Plenaries and Panels Hector Rodriguez School of Creative Media City University of Hong Kong Tomas Laurenzo School of Creative Media City University of Hong Kong Open Call Conference: Scholarly Abstracts Damien Charrieras School of Creative Media City University of Hong Kong Olli Tapio Leino School of Creative Media City University of Hong Kong Open Call Conference: Artistic Project Abstracts Harald Kraemer School of Creative Media City University of Hong Kong Tobias Klein School of Creative Media City University of Hong Kong “Algorithmic Art: Shuffling Space and Time” Exhibition Curator Linda Lai School of Creative Media City University of Hong Kong Conference Committee Maurice Benayoun School of Creative Media City University of Hong Kong Lam Miu Ling School of Creative Media City University of Hong Kong Fion Ng School of Creative Media City University of Hong Kong Malina Siu School of Creative Media City University of Hong Kong Conference Coordinators Jae Cheung School of Creative Media City University of Hong Kong Choi Hoi Ling School of Creative Media City University of Hong Kong PhD Student-led Salon Co-Curators Ashley Wong School of Creative Media City University of Hong Kong Mariana Perez-Bobadilla School of Creative Media City University of Hong Kong Preface and Acknowledgements These are the official proceeding of Art Machines, the 1st International Symposium on Computational Media Art, which was held in Hong Kong from 4th -7th January, 2019, and organized and hosted by the School of Creative Media, City University of Hong Kong. The conference title, Art Machines, refers to the conference theme that focused upon Machine Learning and Art. The conference consisted of four plenary sessions organized around the theme of Machine Learning and Art, a keynote symposium on Robotics and Art, two keynote addresses, open call panels, and a student-run salon. It was accompanied by a sophisticated, high-level exhibition, Algorithmic Art: Shuffling Space and Time, which contextualized contemporary computer-based art in relationship to the history of this practice in the region, and was curated by Dr. Linda Lai in Hong Kong City Hall. Plenary panels and keynotes were solicited by invitation and the breakout panels were solicited by open call. Open call papers were solicited both on the conference theme of Machine Learning and Art and on broader themes pertaining to computational media art in general. In the end, over 50 percent of the accepted papers directly addressed the main conference theme. The overall acceptance rate was 60%. Contributions were invited under four categories. Full papers were subject to double-blind peer review. Conference abstracts, solicited under two categories: artistic abstracts and scholarly abstracts, were reviewed by the organizing committee. Finally, poster presentations were invited, but since few were accepted, these were folded into the conference abstracts. This volume contains the accepted full papers (6), together with the abstracts of the scholarly papers (29) and artistic papers (27) that were presented at the conference. This distinction between scholarly and artistic abstracts is not an absolute one, but reflects the difference between papers which consisted primarily of an artist making an analytical presentation of his or her work and a scholarly inquiry in the field. The conference organizing committee consisted of nine faculty from School of Creative Media who divided different responsibilities between them: Dr. Linda Lai directed the exhibition Algorithmic Art. Dr. Hector Rodriguez and Dr. Tomas Laurenzo, who early in 2018 organized a successful conference on Machine Learning and Art in Cordoba, Spain called Ars Incognita, came up with the conference theme and took responsibility for selecting the participants in the the plenaries and panels on Machine Learning and Art. Dr. Harald Kraemer and Mr. Tobias Klein reviewed the artistic abstracts. Dr. Olli Tapio Leino and Dr. Damien Charrieras reviewed the scholarly abstracts and Dr. Leino also oversaw the review process of the full papers. Prof. Maurice Benayoun assisted with the organization of the student salon. Dr. Miu Ling Lam helped secure financial support from the Croucher Foundation and some of the plenary contributions. I want to thank them all for their hard work in making this conference possible. I also want to thank the leaders of the student salon Ashley Wong and Mariana Perez-Bobadilla. I would like to offer a special acknowledgement to Dr. Leino for his wise counsel and support throughout. The expertise he brought to organizing of this conference from his outstanding leadership of ISEA 2016 was invaluable, including providing the template for this volume. Art Machines and its accompanying exhibition, Algorithmic Art, would not have been possible without the support of a number of key organizations and individuals. I offer grateful thanks to our financial donors: City University of Hong Kong; The Innovation and Technology Fund, Hong Kong; The Leisure and Cultural Services Department (LSCD), Hong Kong; The U.S Consulate General in Hong Kong & Macau; The Croucher Foundation; and The Cultural and Sports Committee, City University of Hong Kong. My special thanks to Dr. Louis Ng, Deputy Director, LCSD, Prof. Alex Jen, Provost, CityU, and Prof. Horace Ip, Vice President, CityU. This volume was prepared before the conference and given to every delegate. Thanks to all of you who responded to our call and participated in this conference and thanks, too, to the various session chairs and moderators. Finally, I want to thank Fion Ng who helped us raise money and co-ordinate Algorithmic Art, and give special thanks to Ms. Malina Siu. From day one, Malina took charge of the whole process, and together with our team, Ms. Jae Cheung Oi Lun and Dr. Sureshika Piyasena, put in a lot of hard work to ensure that presentation of this volume was of the highest standard. Richard William Allen Conference Director, Art Machines: ISCMA 2019 Dean, School of Creative Media Chair Professor of Film and Media Art City University of Hong Kong vii Contents Preface and Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I Full Papers (peer-reviewed) vii 1 1 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong . . . . . . . . . . . . . . . . . . . 2 2 Artistic Intelligence. Ray LC (Luo) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 CG-Art: Demystifying the Anthropocentric Bias of Artistic Creativity. Leonardo Arriagada . . . . . . . . . 20 4 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Sofian Audry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5 How does a Machine Judge Photos?. Wasim Ahmad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 6 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 II Scholarly Abstracts 57 7 The Present Tense of Virtual Space. Andrew Burrell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 8 Computational Photography. Yeon-Kyoung Lim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 9 import <execute> [as <command>]. Korsten, De Jong . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 10 The (un)predictability of Text-Based Processing in Machine Learning Art. Winnie Soon . . . . . . . . . . . 64 11 The Viewer Under Surveillance from the Interactive Artwork. Raivo Kelomees . . . . . . . . . . . . . . . . 66 12 The Demiurge, or a Manifestation of Carbo-Silico Evolution. Jaden Hastings . . . . . . . . . . . . . . . . . 69 13 Art Chasing Liability: Digital Sharecropping and Conscientious Law-Breaking. Monica Lee Steinberg . . . 72 14 Audiovisual Experiments with Evolutionary Games, and the Evolution of a Work-in-progress. Stefano Kalonaris 74 15 Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence. Joo-Wha Hong, Nathaniel Ming Curran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 16 Introducing Machine Learning in the Creative Communities: A Case Study Workshop. Matteo Loglio, Serena Cangiano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 17 Storytelling for Virtual Reality Film: Structure, Genre, Immersive and Interactive Narrative. Ka Lok Sobel Chan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 18 Generation of a Multi-pictorial Script. Haytham Nawar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 19 Speculation and Acceleration: Financialization, Art & The Blockchain. Ashley Lee Wong . . . . . . . . . . 85 20 Aesthetic Coding: Exploring Computational Culture Beyond Creative Coding. Winnie Soon, Shelly Knotts . 87 21 Distributed Cognition in Ecological / Digital Art. Scott Rettberg . . . . . . . . . . . . . . . . . . . . . . . . 89 22 Playing with the Sound. Wing On Tse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 23 Art and Automation: The Role of the Artist in an Automated Future. Lodewijk Heylen . . . . . . . . . . . . 94 24 Atom, Bit, Coin, Transactional Art Between Sublimation and Reification. Maurice Benayoun, Tobias Klein 96 25 Facial (Re)Cognition: Windows and Mirrors, and Screens. Megan Olinger . . . . . . . . . . . . . . . . . . 99 26 Are Photographers Superfluous? The Autonomous Camera. Elke Reinhuber . . . . . . . . . . . . . . . . . 101 27 How Machines See the World: Understanding Image Labelling. Carloalberto Treccani . . . . . . . . . . . . 104 28 The Struggle Between Text and Reader Control in Chinese Calligraphy Machines. Yue-Jin Ho . . . . . . . . 106 29 Bacterial Mechanisms: Material Speculation on Posthuman Cognition. Mariana Pérez Bobadilla . . . . . . 108 30 Lying Sophia and Mocking Alexa – An Exhibition on AI and Art. Iris Xinru Long . . . . . . . . . . . . . . 110 31 Art of Our Times: A Temporal Position to Art and Change. Tanya Toft Ag . . . . . . . . . . . . . . . . . . 112 32 Do Machines Produce Art? No. (A Systems-Theoretic Answer). Michael Straeubig . . . . . . . . . . . . . 114 33 The Janus-Face of Facial Recognition Software. Romi Mikulinsky . . . . . . . . . . . . . . . . . . . . . . . 116 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 ix Contents 34 A Pixel-Free Display Using Squid’s Chromatophores. Juppo Yokokawa, Haruki Muta, Ryo Adachi, Hiroshi Ito, Kazuhiro Jo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 35 VR and AI: The Interface for Human and Non-Human Agents. Lukasz Mirocha . . . . . . . . . . . . . . . 120 III Artistic project abstracts 36 37 38 39 SHAPES of the Future: When Art Machines Pass the Turing Test. Terry Trickett . . . . . . . . Opinions – Body Movements and Sound. Yanbin Song . . . . . . . . . . . . . . . . . . . . . . Constellation – Call Your Personalized Constellation. Nan Zhao . . . . . . . . . . . . . . . . . The Dancer in the Machine. Simon Biggs, Sue Hawksley, Samya Bagchi, Mark D. McDonnell 123 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 127 130 132 40 I’m evolving into a box. The Paradoxical Condition in AI. Wei-Yu Chen . . . . . . . . . . . . . . . . . . . . 135 41 Volumetric Black. Triton Mobley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 AIBO – Artificially Intelligent Brain Opera – An Artistic Work-in-Progress Rapid Prototype. Ellen Pearlman Artificial Digitality. Kuldeep Gohel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specimens of the Globe: Generative Sculpture in the Age of Anthropocene. Gyung Jin Shin . . . . . . . . . Machine Learning for Performative Spaces. Alex Davies, Brad Miller, Boris Bagattini . . . . . . . . . . . Penelope. Alejandro Albornoz, Roderick Coover, Scott Rettberg . . . . . . . . . . . . . . . . . . . . . . Hypomnesia, Game of Memory. Wanqi Li, Jian Guan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Up-Close Experiences with Robots. Louis-Philippe Demers . . . . . . . . . . . . . . . . . . . . . . . . . . Membrane or How to Produce Algorithmic Fiction. Ursula Damm, Peter Serocka . . . . . . . . . . . . . . The Fresnel Video Lens. Steve Boyer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MAC Check. Scott Fitzgerald . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Visualizing Algorithms: Mistakes, Bias, Interpretability. Catherine Griffiths . . . . . . . . . . . . . . . . . Multimedia Art: The Synthesis of Machine-generated Poetry and Virtual Landscapes. Suzana Ilić, Martina Jole Moro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Microbial Sonorities. Carlos Castellanos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The 360° Video Secret Detours as Case Study to Convey Experiences through Immersive Media and the Method of Presentation. Elke Reinhuber, Benjamin Seide, Ross Williams . . . . . . . . . . . . . . . . . . . . . . . Parallax Relax: Expanded Stereoscopy. Max Hattler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Electronic Curator or How to Ride Your CycleGAN. Eyal Gruss, Eran Hadas . . . . . . . . . . . . . . Das Fremde Robot Installation. Michael Spranger, Stéphane Noel . . . . . . . . . . . . . . . . . . . . . . Repopulating the City: Introducing Urban Electronic Wildlife. Guillaume Slizewicz, Greg Nijs . . . . . . . Anonymous Conjecture. Fangqing He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adversarial Ornament Attack. Michal Jurgielewicz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 142 144 145 147 149 151 154 156 158 160 162 164 167 169 170 172 174 176 178 62 The Time Machine: a Multiscreen Generative Video Artwork. Daniel Buzzo . . . . . . . . . . . . . . . . . 181 IV Review Board x Proceedings of Art Machines: International Symposium on Computational Media Art 2019 183 Part I Full Papers (peer-reviewed) 1 2.5D Computational Image Stippling Kin-Ming Wong Tien-Tsin Wong artixels mwkm@artixels.com The Chinese University of Hong Kong ttwong@cse.cuhk.edu.hk (a) Input pair (image + depth). Fig. 1. (b) Regular stippling. (c) Stippling with depth of field using our method. 2.5D computational image stippling examples (10,240 points) Abstract We present a novel 2.5D1 image stippling process that renders the photographic depth-offield effect direct as an integral feature without any need of image filtering computation. Our approach relies on an additional depth image to produce the effect. The proposed method is based on a recent physically based blue noise sampling technique, which allows sampling naturally from spatial data, such as a 3D point cloud. The separation of the image data and its spatial information under our proposed 2.5D setting enables additional creative possibilities of image stippling art. Our approach can also produce an animated sequence that mimics the rack focus effect with good temporal coherence. 1. Introduction Image stippling has a long history, dating back to the 16th century as a printmaking technique introduced by Giulio Campagnola [1] for reproducing smooth tones, shading and image details. This image-making technique uses only strong tone dots as the sole pictorial elements, and it demands an extremely skilful spatial arrangement. After centuries, stippling is still ubiquitous because of its unique aesthetics, the transparency of the process, and its simplicity as an art form. Computational image stippling connects tightly to blue noise adaptive sampling techniques. Deussen and Isenberg [2] offer an excellent comprehensive review of its development. The term blue noise was formally defined and characterized by Ulichney [3] in his dithering research work. Figure 1b shows an example of how the structureless blue noise points reproduce pleasantly the underlying image tone with subtly varying yet uniform distribution. Early research work in computer graphics related to blue noise and image stippling was driven by the need for tone reproduction improvement for early digital printing and display devices. Floyd and Steinberg [4] proposed the error diffusion technique, which stands as one of the best examples of how dithering improves tone reproduction. In the rendering research community, Dippé and Wold [5] proposed the use of Poisson disk sampling in rendering with reference to work on the study of spatial pattern of photo-receptors by Yellott [6]. 1 2.5D image processing refers to techniques which take advantage of the perpixel distance from camera information, i.e. depth information 2 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong Cook [7] further popularized the effectiveness of Poisson disk sampling, which is effectively a quality blue noise sampling point set. Stippling-focused research work proposed by Deussen [8] relies on the relaxation technique proposed by Lloyd [9] to produce quality stipple drawings. To enable a more interactive experience, Secord [10] introduced a precomputed stipple tile-based approach, along with the weighted Voronoi method. Ostromoukhov et al. [11] and Kopf et al. [12] proposed improved tile-based acceleration techniques for better interactive image stippling. More modern blue noise research work by Balzer et al. [13], namely the Capacity Constrained Voronoi Tessellation (CCVT) technique, is considered the state-of-the-art blue noise sampling method. CCVT serves as an important model, which inspired additional work. One such work was proposed by De Goes et al. [14], which formulated the capacity constrained model into an optimal transport problem, now commonly known as the BNOT method. The kernel density model proposed by Fattal [15] also set a new standard for blue noise sampling quality. There are computational image stippling methods that are designed to improve the quality or variety of image stippling art from different perspectives. Pang et al. [16] proposed an approach that emphasizes reproduction of the structural details. Kim et al. [17] proposed an example-based stippling method that enables the use of sampled stippling patterns. Wei [18] introduced multi-class sampling, which enables more sophisticated stippling possibilities, and Li et al. [19] proposed an anisotropic technique, which substitutes dots with adaptive thin directional pictorial elements. Li and Mould [20] proposed a structure aware stippling method, which allows user-defined priority of stipple emphasis. For the depth-of-field effect, there is no shortage of bitmap image filtering-based techniques [21, 22, 23], which render the photographic effect using an additional depth image. To the best of our knowledge, there has been no attempt to introduce photographic effects to the image stippling process as an integral feature without any pre-processing of the input image. Our proposed 2.5D image stippling method renders the depth-of-field effect as a computation-free feature. We rely on the physically based blue noise sampling technique proposed by Wong and Wong [24] as the core of our approach. This sampling technique models the sample points as electrically charged particles, which self-organize by movement to reach an equilibrium. We apply an intuitive extension to this blue noise sampling method so that 2.5D image data can be adaptively sampled. This dynamics-based approach also allows us to produce an animated rack focus effect by changing the focus distance during simulation; the animated result shows stable temporal coherence. In section 2, we give a brief overview of the blue noise sampling technique used in our method and how it inspired our work. Section 3 describes the details of our extension for 2.5D image data sampling. In section 4, we demonstrate and evaluate the depth of field enabled stippling results from an artistic point of view. And in section 5, we discuss a few creative stippling applications based on our method. 2. Physically based Blue Noise Sampling In this section, we review the blue noise sampling technique proposed by Wong and Wong [24], which serves as the foundation of our 2.5 image stippling method. This sampling method proposed a very intuitive approach, which models the sampling points as a system of electrically charged particles, with each carrying an identical charge. These like-charged particles repel each other, and the system undergoes selforganization by movement until it reaches an equilibrium state by maintaining a uniform equidistant neighbourhood around each particle. The particles' positions are then computed by integrating the equations of motion using a customized Velocity Verlet numerical integrator [25, 24], described in the original article. The whole idea is not totally innovative. It was first suggested by Hanson [26] and later by Schmaltz [27], but using a pure 2D electric field. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 3 Part I. Full Papers (peer-reviewed) 2.1 Uniform Sampling Given a system of N particles constrained on an (a) qs = 0.05. (a) Uniform point set with qs = 0.25. (b) Power spectrum. Fig. 2. Uniform sampling using the physically based blue noise sampling method. [24] imaginary 2D plane, the total electrostatic force exerted on a particle pi based on Coulomb's inverse-square law is governed by the following equation (eq. 1): 𝑁𝑁 𝐹𝐹𝑖𝑖 = 𝑞𝑞𝑠𝑠2 ∑ 1 𝑗𝑗≠𝑖𝑖 ‖𝑟𝑟𝑖𝑖 − 𝑟𝑟𝑗𝑗 ‖ 2 𝑒𝑒̂𝑗𝑗,𝑖𝑖 where 𝑞𝑞𝑠𝑠 is the amount of charge carried by each particle, 𝑟𝑟𝑖𝑖 and 𝑟𝑟𝑗𝑗 are the positions of particles 𝑝𝑝𝑖𝑖 and 𝑝𝑝𝑗𝑗 , respectively, and 𝑒𝑒̂𝑗𝑗,𝑖𝑖 is a unit vector pointing from 𝑟𝑟𝑗𝑗 to 𝑟𝑟𝑖𝑖 , which represents the direction of force. The process is simulated in a periodic domain, and the particles self-organize to reach an equilibrium state. Figure 2 shows a uniform point set generated using this physically based technique. This point set exhibits highquality blue noise characteristics and is reflected by its power spectrum, as shown in Figure 2b. 2.2 Adaptive Sampling What inspired our 2.5D image stippling approach is the adaptive sampling model proposed by this sampling method. To adaptively sample a varying density function, such as a bitmap image, the sampling method creates an additional imaginary 2D plane, named the density plane. On this new density plane, a regular grid of M non-moving attractively charged particles is created; each particle's charge is determined by the corresponding pixel that it represents. The amount of charge 𝑞𝑞𝑘𝑘 carried by a given particle 4 (b) qs = 0.35. Fig. 3. Impact of sampling particle’s charge 𝑞𝑞𝑠𝑠 on adaptive sampling. 𝑝𝑝𝑘𝑘 on the density plane is defined as follows (eq. 2): 𝑞𝑞𝑘𝑘 = −𝐴𝐴(1.0 − 𝐼𝐼𝑘𝑘 ) where 𝐼𝐼𝑘𝑘 is the pixel's intensity value that the particle 𝑝𝑝𝑘𝑘 represents, and 𝐴𝐴 is a positive valued coefficient determined by the total charge of the particles on the sampling plane. This relationship guarantees a total balance of potential. The force exerted on a particle 𝑝𝑝𝑖𝑖 on the sampling plane by the charges on the density plane is governed by the following equation (eq. 3): 𝑀𝑀 𝐺𝐺𝑖𝑖 = 𝑞𝑞𝑠𝑠2 ∑ 𝑘𝑘=1 𝑞𝑞𝑘𝑘 𝑒𝑒̂ ‖𝑟𝑟𝑖𝑖 − 𝑟𝑟𝑘𝑘 ‖2 𝑘𝑘,𝑖𝑖 The total force experienced by a particle 𝑝𝑝𝑖𝑖 can be expressed as the sum of equations (1) and (3). We carefully examined the stipple images produced by this blue noise sampling method, and we noticed that the amount of charge 𝑞𝑞𝑠𝑠 carried by the sampling particles has an important impact on the overall image quality. Figure 3 shows a pair of stipple images produced using different values of 𝑞𝑞𝑠𝑠 . A higher value of 𝑞𝑞𝑠𝑠 produces an impression of better contrast. We believe it is a logical consequence that the larger force between sampling particles produces more space in the areas of low density (or brighter area), so it boosts the overall contrast. It is not Proceedings of Art Machines: International Symposium on Computational Media Art 2019 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong (a) Small inter-plane (b) Large inter-plane distance. distance. Fig. 4. Adaptive Sampling with different amount of interplane distance. hard to see that Figure 3b offers better contrast than Figure 3a. For a lower a value of 𝑞𝑞𝑠𝑠 , we note that the points are obviously less structured, and they seem to be more sensitive to subtle local image structures too. In our experience, a higher value of 𝑞𝑞𝑠𝑠 accelerates the convergence if it is a necessary factor to consider. The density plane is by design placed tightly and parallel to the sampling plane to control the local density of the sampling particles. Wong and Wong [24] briefly demonstrated the impact of this inter-plane distance to the adaptive sampling results, and they named it a parameter for sharpness control. Figure 4 shows the effects of this parameter. It has an intuitive physical meaning here because according to Coulomb's inverse-square law, attractive force should be weakened and less localized when the distance between the sampling and the density planes increases, resulting in a stipple image that gives a blurred impression, as shown in Figure 4b. Although the force applied by the density plane, as expressed in Equation (3), assumes a planar arrangement of the particles, the model itself does permit a 3D configuration, as mentioned in Wong and Wong [24]. Our method exploits this 3D configuration possibility as the foundation of our depth-of-field effect integrated stippling technique. 3. 2.5D Image Stippling By extending the idea of using a 2D density plane for adaptive sampling, we propose substituting the planar setup of density particles with a height-field alike configuration. In our new model, each density particle has its own depth from the sampling plane defined by an additional depth image. We also introduce a new parameter , which defines the focus distance, from the so the density particles at a distance sampling plane give an in-focus impression in the stipple result. To achieve this visual effect, we displace the whole density field towards the sampling plane by , so the in-focus density particles exert a strong attraction to the sampling particles. Based on this new proposal, we adapt Equation (3) to accommodate the changes. The force exerted by this new configuration is now governed by the following equation (eq. 4): 𝑀𝑀 𝐺𝐺 𝑖𝑖 = 𝑞𝑞𝑠𝑠2 ∑ 𝑘𝑘=1 𝑞𝑞𝑘𝑘 ‖𝑟𝑟𝑖𝑖 − 𝑟𝑟 𝑘𝑘 ‖2 𝑒𝑒̂𝑘𝑘,𝑖𝑖 where 𝑟𝑟 𝑘𝑘 = 𝑟𝑟𝑘𝑘 − (0,0, ) is the new position of density particle 𝑝𝑝𝑘𝑘 , 𝑒𝑒̂𝑘𝑘,𝑖𝑖 is a unit vector pointing from 𝑟𝑟 𝑘𝑘 to 𝑟𝑟𝑖𝑖 , and maintains a minimum distance between particles to avoid instability. To control the amount of depth of field, the depth component of all density particles can be globally scaled to achieve the desired degree of field depth. We use the same numerical integrator described in Wong and Wong [24]; the algorithm is outlined in Algorithm 1. Using OpenGL compute shaders, we implemented a simple GPU application based on our method. Figure 5 shows an example of how our method is used to create stipple images from the same input with different focus distances. The average computation time of this example is 326ms per iteration, using an nVIDIA Geforce GT 650M mobile GPU. ______________________________________ Algorithm 1 Numerical Integrator 1. Position Update: ( )= ( ) 2. Acceleration Update: Compute ( ) using ( 3. Velocity Update: ( 4. Repeat )= 1 ( , ( ) ( , ( ) 1 ( ) 2) ) ( ( ) Proceedings of Art Machines: International Symposium on Computational Media Art 2019 ( )) ) 5 Part I. Full Papers (peer-reviewed) (a) Input pair (image + depth). Fig. 5. (b) Focus on the front, depth = 0.25. (c) Focus on the back, depth = 0.55. Image stippling examples with depth-of-field effect using our method; both used 𝑞𝑞𝑠𝑠 = 0.3 and 150 iterations to converge. where is a user-defined damping factor of a range of [0,1), which improves convergence. We find that a value of 0.95 works best in most scenarios. defines the maximum per time-step displacement of each particle, which we keep constantly at 0.002, using a normalized coordinate system in our periodic simulation setting. 4. Evaluation In this section, we evaluate the visual quality and image characteristics of our rendered output. In the PDF version of this paper, all stipple images are embedded in vector form for better visual examination. 4.1 Pre-filtered Depth of Field The depth-of-field effect is traditionally achieved by applying adaptive filtering to a bitmap image, based on a depth map. We evaluate the qualitative difference between our results using the traditional approach from an artistic point of view instead of a technical one because our method is not designed to parallel or match the filtering result of the bitmap imagebased technique. We used commercial software [28] to obtain a pre-filtered bitmap, which is made to match the degree of depth of field in Figure 5b. Figure 6b shows a regular stippling result of the prefiltered depth-of-field input using our method; it is not hard to observe that the stipple image using pre-filtered input maintains better contrast 6 (a) Pre-filtered input. Fig. 6. (b) qs = 0.3. Stipple image of the pre-filtered depth of field image. and a stronger photographic impression. Our depth-of-field result in Figure 5b, however, has a stronger illustrational and handcrafted quality. As our approach does not intend to accurately simulate the bitmap image filtering process, we believe that our result has a unique look with its own aesthetic qualities. 4.2 Degree of Depth of Field Our model allows different degrees of depth of field by globally scaling the depth component of the input depth map. Figure 8 shows two stippling results rendered with different depth scaling factors, while all other settings remain identical. The one with shallow depth of field, Proceedings of Art Machines: International Symposium on Computational Media Art 2019 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong Fig. 7. (a) Medium depth of field. (a) Particle charge qs = 0.1. (b) Shallow depth of field. (b) Particle charge qs = 0.5. Different degrees of depth of field image. shown in Figure 7b, demonstrates stronger tone and local contrast on the dark in-focus areas. We believe this is a consequence of the relatively stronger attraction force and denser in-focus neighbourhood. 4.3 Tone and Feature Reproduction Characteristics As mentioned above, the sampling particle's charge has an impact on the overall image contrast. This is an inherent property of the sampling method [24], but we take a deeper look at how this parameter 𝑞𝑞𝑠𝑠 affects the overall image quality. We use a pair of stipple images with the same depth of field settings using a lower number of sample points (5,120 points) to illustrate our observations more clearly. Fig. 8. Effects of particle charge. Figure 8a is produced using a smaller particle charge. It is not hard to observe that the stipple points on this image are far less structured than the ones in Figure 8b. The stipple points rely on various subtle and continuously varying density distributions to reveal the underlying image. This characteristic helps to maintain the subtle local tonal changes, and the whole image possesses a more organic quality from an artistic point of view. In contrast, the stipple points in Figure 8b are more structurally organized; this is especially clear on the silhouettes and other sharp features. The overall image has more technical clarity, and better overall image contrast. We believe this setting is good for instructional or graphical illustration purposes. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 7 Part I. Full Papers (peer-reviewed) (a) Mixed inputs. Fig. 9. (b) Stipple output. Mixed input for stylized stippling. (a) Mixed inputs. (b) Stipple output. Fig. 10. Mixed input for graphic design. 5. Creative Possibilities In this section, we explore various creative possibilities with our proposed method, ranging from general manipulation to photographic processing and animated sequence output. 5.1 Mixed Input as Masked Processing As our method relies on a separate given depth image, users can always use a depth map that is not necessarily related to the image as a means to achieve other creative effects. Figures 9 and 10 show two creative uses of mixing an unrelated depth map to an image map to create a masked stippling. 5.2 Image Processing To render the depth-of-field effect for bitmap images, image features more distant from the focus require more processing because of a larger filter kernel to process, but this does not 8 (a) Input pair. (b) Stipple output. inputs. Fig. 11. Tilt-shift alike image filtering. apply to our stippling method. For general bitmap image processing based on convolution, we may loosely relate the filter kernel radius in bitmap image processing with the depth component of a density particle in our method. As an example of this connection, we follow how bitmap image processing creates a tilt-shift effect to a given image; this is usually achieved by applying a blurring process with a global radially increasing filter kernel radius. We reproduce it with a depth map which mimics the approach. Figure 11 shows the input pair and the result. We believe this analogy between the kernel radius and the density particle's depth would serve as a good research direction for exploring systematic processing techniques for stipple images, or more precisely, point-based images. 5.3 Temporal Coherence of Stipple Image Sequence We include with this paper a short video as supplemental material to demonstrate how our dynamics-based stippling method can be used to generate an animated sequence of stipple images that mimics the rack-focus effect. It can be used direct as the initialization point set for the next stipple computation. As long as the focus distance shifts slowly, the convergence of the new stipple image can happen in one or just a few time-steps in our experience. More importantly, the two consecutive stipple images often demonstrate good temporal Proceedings of Art Machines: International Symposium on Computational Media Art 2019 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong coherence. This is the advantage of the global, dynamics-based blue noise method proposed by Wong and Wong. [24] This temporal coherence is often hard to achieve with the sequential method or algorithms which rely on randomization. Theoretically, this temporal coherence characteristic should also apply to animated video clip input, provided there is no vigorous change in image content, but this potential was not explored in the original paper. (a) Regular stipple image. Fig. 12. Per-iteration time performance on GT650M. 6. Performance We implemented a simple graphics processing unit (GPU) application using OpenGL compute shaders without any specialized algorithmic acceleration. Stippling computation time depends only on the number of sample points and the input image size; the degree of depth of field has no impact on our performance. For a stippling of 10,240 points and an input bitmap of size 256 256, each iteration takes less than 150ms on a modest Geforce GT650M notebook GPU. Our compute shader parallelizes in a per sample point fashion, and the OpenGL compute shader allows us to maximize the use of local memory to minimize the GPU global memory bottleneck. A summary of timing information is provided in Figure 12, showing how computation time increases with the number of sample points under different input bitmap sizes. Although we believe our method should run impressively on more modern GPUs, to compute stippling with several hundred (b) Our stipple result with depth of field. Fig. 13. Inconsistency of perceived brightness. thousand sample points at an interactive rate, an algorithmic level acceleration is definitely necessary. The physically based blue noise sampling method [24] we use is practically an N-Body simulation, so any algorithmic acceleration for an N-Body simulation should work for our method too. The multi-level summation method proposed by Hardy et al. [29] and the non-equidistant fast Fourier transform-based acceleration method by Gwosdek et al. [30] are both applicable to our method. In addition, the electric field of the density particles can be theoretically precomputed as a high resolution look-up table for runtime interpolation. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 9 Part I. Full Papers (peer-reviewed) 7. Discussion We have presented a novel 2.5D image stippling method which is able to render certain photographic effects for free. Based on a global blue noise sampling technique, our method generates an animated sequence with effects with good temporal coherence. However, we are aware that our method cannot maintain the consistency of the overall image brightness across stipples. Figure 13 shows a pair of images; Figure 13a is a regular stipple image, and in Figure 13b the depth-offield effect was applied. There is an obvious tone difference between them, which can be explained by the concentration of attraction force. To provide overall brightness consistency, we believe that an algorithm to adjust the number of sample points has to be in place. This could be considered for future research. References 1. G. Flocco, La giovinezza di Giulio Campagnola’in L’Arte, vol. xvii (1915). 2. Oliver Deussen and Tobias Isenberg, “Halftoning and stippling,” in Image and VideoBased Artistic Stylisation (Springer), 45–61. 3. Robert A. Ulichney, “Dithering with blue noise,” Proc. IEEE 76, no. 1 (1988): 56–79. 4. Robert W. Floyd, “An adaptive algorithm for spatial gray-scale,” Proc. Soc. Inf. Disp., 17 (1976): 75–77. 5. Mark A.Z. Dippé and Erling Henry Wold, “Antialiasing through stochastic sampling,” ACM Siggraph Computer Graphics 19, no. 3 (1985): 69–78. 6. John I. Yellott, “Spectral consequences of photoreceptor sampling in the rhesus retina,” Science 221, 4608 (1983). 7. Robert L. 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Hua Li and David Mould, “Structurepreserving stippling by priority-based error diffusion,” Canadian Human-Computer Communications Society Proceedings of Graphics Interface (2011): 127–134. 20. Hongwei Li, Li-Yi Wei, Pedro V Sander, and Chi-Wing Fu, “Anisotropic blue noise sampling,” ACM Transactions on Graphics (TOG) 29 (2010): 167. 21. Joe Demers, “Depth of field: A survey of techniques,” GPU Gems 1, 375 (2004), U390. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 2.5D Computational Image Stippling. Kin-Ming Wong, Tien-Tsin Wong 22. Jhonny Göransson and Andreas Karlsson, “Practical post-process depth of field,” GPU Gems 3 (2007), 583–606. 23. Martin Kraus and Magnus Strengert, “Depth-of-Field Rendering by Pyramidal Image Processing,” Computer Graphics Forum, 26 (2007): 645–654. 24. Kin-Ming Wong and Tien-Tsin Wong, “Blue Noise Sampling using an N-Body based Simulation Method,” The Visual Computer, Proceedings of Computer Graphics International 33, 6-8 (2017): 823-832. 25. William C. Swope, Hans C. Andersen, Peter H. Berens, and Kent R. Wilson, “A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters,” The Journal of Chemical Physics 76, 1 (1982): 637–649. 26. Kenneth M. Hanson, “Halftoning and Quasi-Monte Carlo,” Los Alamos National Library (2005), 430–442. 27. Christian Schmaltz, Pascal Gwosdek, Andrés Bruhn, and Joachim Weickert, “Electrostatic halftoning,” Computer Graphics Forum 29 (2010): 2313–2327. 28. Victor Ostromoukhov, Charles Donohue, and Pierre-Marc Jodoin, “Fast hierarchical importance sampling with blue noise properties,” ACM Transactions on Graphics (TOG) 23 (2004): 488–495. 29. David J. Hardy, John E. Stone, and Klaus Schulten, “Multilevel summation of electrostatic potentials using graphics processing units,” Parallel computing 35, 3 (2009):164–177. 30. Pascal Gwosdek, Christian Schmaltz, Joachim Weickert, and Tanja Teuber, “Fast electrostatic halftoning,” Journal of real-time image processing 9, 2 (2014): 379–392. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 11 Artistic Intelligence Ray LC [Luo] Parsons School of Design Brain and Mind Research Institute, Cornell Medical College rayLC@newschool.edu [rayLC.org] Abstract Machine Learning (ML) has been applied in the financial, medical and educational fields to make, for example, smart stock predictors, hospital robots, and virtual assistants. But their use in one of the most human of endeavours, creative expression, has been relatively unexplored. Current applications of ML in artistic endeavours employ mostly artificial agents to extend human capabilities to realms where access to extensive data provides opportunities for associations previously unexploited by human artists. These examples take the human point of view first and merely expand human ability, by generating novel musical combinations based on a simple palette of tones, analyzing image content to pick out styles that serve as training for further image transformations, or joining poetic text based on phonetic similarities, for example. While these applications rely on ML as a data-mining agent in unexplored domains, they fail to exceed the limits of human expectations of what they can do. There’s another arena in which ML enables artistic expression: using Artificial Intelligence (AI) in unexpected ways in everything we interact with. Imagine, for example, talking to a human whose responses are generated by Google Assistant, or interacting with a robot who secretly wants to make you take medication. I propose using ML to give novel behaviours to objects we interact with, allowing these behaviours to vary using predefined parameters for training, which are unknown to users. Applying ML to unexpected forms of interactions changes what we think machines are capable of, creating situations where AI goes beyond human expectations of what machine intelligence means to us, making objects oddly, Artistically Intelligent. 12 Fig 0. Artistic Intelligence by Ray LC: sculptures imbued with machine learning for creative expression. Source: Ray LC. Introduction Technology is taking over much of our daily lives. Instead of memorizing epic poems passed down through generations, as in Homer’s era, humans invented books to record them. Now instead of using physical paper as media, we record information digitally and no longer need books. We went from talking, singing and memorizing, to recording, archiving and searching when we need something. These new tools have become integrated with human capabilities and made us more powerful, with the experience and findings of all previous generations available at our fingertips. If previously human capabilities, like way-finding, calculating and memorizing, can be overtaken by GPS, computer programs and the internet, what other fundamentally human abilities will be overtaken by the tools humans create? The most unique thing about humans is our ability to express ourselves by creating. Animals and plants can transform their environments the way we do, but they have limited means of making tools to do their work, and they are even more limited in the way in which they create works of imagination. Studies have found cells in the monkey cortex that react to the use of tools, [1] but non-human primates are limited in Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artistic Intelligence. Ray LC (Luo) what they can do in open-ended cognitive tasks, such as the inability to compose a picture. [2] Humans, on the other hand, can create entire worlds in their minds, invent hypothetical scenarios and stories, and evaluate them, and think of futures that may not correspond to reality. We use ideas imaginatively much as we use tools, talking about the hypothetical future based on “what if” questions. [3] Can this fundamentally human ability one day be transferred to tools we invent? Will we make Artificial Intelligence (AI) that creates with us, or even more capably, creates for us? Can we make an AI for Artistic Intelligence? Our uniquely human creative potential comes not from particular domains, like painting or theatre, for many cultures exhibit creativity without having venues in which to express them. Instead, creativity can be defined in terms of the ability to shape and improve ideas adaptively in changing environments, [4] a task suitable for Machine Learning (ML) once the goal state of adaptation has been established. Tasks with a simple goal state, like winning a chess game have comparatively simple ML solutions, because algorithms can simply search for more and more effective ways of searching for a solution to winning the game. In creative endeavours, the goal state is less obvious to humans, so we are unable to create machines that do the task for us, just by virtue of the ambiguity of what that task is actually trying to do. A sculptor may create a sculpture as much for its likeness to someone in her life (a welldefined goal) as for a need to expose societal prejudices (a goal much harder to define digitally). Hence, creative expression has so far not been taken over by ML algorithms, because it’s not clear what the algorithms should aim to achieve. One approach is to use ML to achieve what human artists achieve by learning (copying) the process of artifact creation. In this scheme, any future “invention” by machines is coded for by the creator, and ML is only a tool for templatebased creation. In contrast, another approach is to make ML agents part of a human ecosystem of creative works, exploiting our assumptions about what machines that have humanoid behaviours can or should do, giving voice to the machine’s own Artistic Intelligence. Background The first approach of using ML to mimic human creativity started with computer programs used to make “novel” images. Harold Cohen’s AARON robot was programmed by its creator to make abstract drawings based on predefined styles. Over the years AARON’s output looked a lot like Cohen’s own evolving style, leading to the question of what would happen after Cohen’s death. Would AARON stop learning, and if so, was it ever really creative, or simply following patterns? Cohen’s contention is that art does not require constant creativity, but rather devising rules to follow and allowing the pattern of rules to take over. [5] If this is the case, AARON is only a translator from patterns to artefacts, with some randomness added. Fig 1. AARON: a robot used by artist Harold Cohen to make abstract images autonomously using a routine programmed to mimic Cohen’s own style. Source: technologyreview.com. Other examples of ML art based on emulating human styles and customizations include ventures in digital image processing, like the Pikazo app, which combines an image and a style embodied by a painter in the history of art or an uploaded texture to make a novel image combination. The role of ML in the app is to perform the combination process in a seamless manner, using image recognition algorithms. However, there’s no creativity for the AI in this approach. Images from the Pikazo website show clear filter-like manipulation of images using the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 13 Part I. Full Papers (peer-reviewed) styles of various artists. Project Magenta dispenses with idea of machine creativity and instead focuses on algorithms that augment what human creators can do. For example, in Beat Blender, beat rhythms for music can be generated by drawing a path through a spatialtemporal state space of beats, allowing the musician to make creative content using an intuitive feel for beats in time and patterns in space. Project Magenta assumes that ML is used to heighten what humans can do by creating novel interfaces and creative combinations of basic palettes enabled by artists, not by having the algorithm generate ideas. Similar efforts in the textual domain have been undertaken to create machine-generated novels, such as Allison Parrish’s Our Arrival. Fig 2. Pikazo: an app that creates new images based on a preselected style and an image to be modified. Source: pikazoapp.com While the majority of ML art projects use ML to drive creativity, another segment of artists have focused on what AI will do to the creative process by focusing on understanding the machine. In particular, they aim to understand what is it about machine data mining that undermines how people, as creatives, can interact with the world. For example, ML systems like Deep Mask and Tensor Flow enable online systems to categorize people into stereotypical forms and to use their private data to make inferences about their lives. [6] With machine surveillance fast becoming part of our 14 future, artists like Merijin Bolink are wondering how best to understand machines in order to coexist with them. In his “Google’s Eyes” project, he used Google’s Goggles app to iteratively identify a sculptural object. First he created a ceramic tire, which when interpreted by Goggles, returned a list of items, which included a jawbone. Then Bolink made a plaster copy of the jawbone and had Goggles interpret it, which it identified as a hand. The complete 20-object series are placed together as a representation of how machines interpret human art, showing how the human creative potential may be subverted by machine recognition. While some artists like Bolink fear the rise of ML in the creative process, others herald it as the next phase of our evolution. In an early treatise on machine creativity, Roger Schank suggests that creativity can be defined as innovative problem solving, and that looking for “near misses” allows machines to hone in on these miss patterns and come up with creative modifications. [7] In a similar vein, arguments have been made that human creative power can be supplemented by machine interfaces, which have access to a larger scope of data, which can serve as raw material for powerful creative acts. [8] A counter argument is that more data is not necessarily useful, for great artists have often been constrained in expressing their point of view, which makes their work particularly expressive given their limited scope. This can create powerful emotion in those who have had a similar experience. Perhaps artistic genius comes from a combination of ML-like exploration and human-like constraints, much like the way trans-humanism puts machines and humans together. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artistic Intelligence. Ray LC (Luo) Fig 3. Google’s Eye project: Each object is iteratively shown to the Google app Goggles, which gives suggestions related to what it sees using image recognition. Each suggestion then becomes the next object. Source: fastcodesign.com Fig 4. Example of AI in general domains. The machine is intended to be programmed for one area (sales specialist) but shocks the audience with human-level knowledge in another field (influence, data collection). In the artistic domain, AI does something unexpected based on preconceptions. All the works discussed so far have applied ML to enable or enrich creative processes. A different approach to human-machine creativity interaction is to realize that our reaction to machines and what they are supposed to be capable of in human terms can be used to imbue them with intelligence and perceived emotionality and creativity. To allow machines to go beyond human creative potential, we have to go beyond just what machines are capable of, and instead, think about what is it in humans that makes us think that this is what machines can do. Creativity is about remaking processes, not artefacts. What makes this process unique is that by using what humans believe about machines to subvert our preconceived notions, we are making both humans and machines more creative. We are more creative because we can make tools that transcend boundaries to allow them to work closely with us. Machines are more creative because to the audience, they are doing more than what stereotypical machines do. There’s a natural consequence to the approach of using ML to transform what we think machines should do, which is that our fears about machines posing as humans or knowing our every move will manifest itself as uncertainty as to which part of the machine’s response is from the machine and which is from its programmer. This point is akin to going to a website that offers interactive chats with a “sales specialist.” After asking her a few questions, you get the feeling that she is not from your country and that perhaps she is contracted from a foreign country, because her replies are accurate but she uses unusual phrases. As the order proceeds you realize that she has the uncanny ability to know exactly what you have been searching for and knows your online identity and buying history from the past several months. Is she a person or a machine? Does it matter? Predictable AI does not produce a creative machine. Truly creative AI machines will possess an aura of mystery, which neither the programmer nor the machine can explain. Using ML to subvert what we think about ML puts us in a world where machines and humans are equals in their ability to influence: one is better at data; the other is better at language; one is better at analysis; the other is better at emotional response. If the AI machine is unexpected, it seems creative. Process To demonstrate the power of ML for creating smart objects capable of unexpected interactions with people, I created a set of sculpture pieces that incorporate digital technology using ML to predict and control, and occasionally, to surprise. Sculpture has the connotation of being inactive, because it usually remains inside a museum or in a fixed public space. What’s more, it is usually considered serious and high-brow due to its association with classical works of art and intellectualism. I chose sculpture as the domain of experimentation because I wanted to Proceedings of Art Machines: International Symposium on Computational Media Art 2019 15 Part I. Full Papers (peer-reviewed) challenge these two stereotypes about sculpture by creating pieces that interact instead of being sedentary, and that exhibit quirky and unexpected behaviour instead of being profound and unexciting. To begin, I observed that ML algorithms start from the premise of using observable states coupled with desired outcomes to predict future observations, using a learning algorithm to update the network to make the predictions more accurate. [9] I asked if ML agents are really making predictions based on observations, how would a humanoid version that behaves similarly be interpreted by humans. I made a hand sculpture that rotates either left or right using an embedded servo motor. The gesture is meant to convey the act of “looking” by the sculpture and prompts the audience to respond with the same gesture. When a person moves close to the hand sculpture, the sculpture uses an ultrasonic sensor to detect the person’s presence, and turns to face right or left randomly. However, the distance between the left and right sides with respect to the sensor is different, so ML can train it to determine whether the person is on its left or right and to train itself to adapt to the sequence of human hand movements. Using this data, the sculpture learns to predict whether the next hand motion from the human will be to the left or right of it, and will move there in anticipation. The predictions become more and more accurate over time as data is accumulated to drive the ML. The algorithm takes the average of recently detected locations and forms a maximum likelihood estimate of where the hand will be next, which is a form of time series prediction (see https://recfreq.wordpress.com/ portfolio/ai-artistic-intelligence/). 16 Fig 5. Hand sculpture that predicts where the interaction with it will stem from. The servo for rotation is controlled using a microcontroller that detects user distance using an ultrasonic sensor. The learning algorithm predicts future user positions by keeping track of the averaged time series of previous responses. Source: Ray LC. In user tests, I found that it was difficult to have people continue to interact with the sculpture to see the effect of the training. The ultrasonic distance sensor is also occasionally finicky, making data filtering necessary to maintain the accuracy of the sensor data for prediction. Moreover, the sculpture direction can be randomly correct early on, because there are only two possible states, so the error rate is only 50% even without learning. This can mask the progress that the sculpture makes over time. However, if observers are committed to watching the development over time, they can see the learning undertaken by the ML agent. Audiences also find the statue engaging, because plaster hands don’t usually move. The statues with interactive components were considered “cute” by some observers. Many were also surprised by its ability to move, and those who had the patience to observe its learning found the adaptability of the statue to be evocative. The canonical view of an immobile sculpture was replaced by an interactive element, which I will continue to explore in other modalities. Thus, I have shown that a motorized sculptural piece capable of learning about its audience can use ML to enrich its interaction and evoke positive unexpected responses, contrary to its stuffy classical stereotype. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artistic Intelligence. Ray LC (Luo) Fig 6. A “Star Trek” signalling hand sculpture, fortified by a raspberry pi running the Google Speech API. It interprets user voice input and replies, distorting the original meaning. The user is prompted to press the red button and say anything with the word “sculpture” in it. The sculpture adapts the words accordingly. Source: Ray LC. Next, I wanted to take the unsuspected sculptural agency idea one step further by making a talking sculpture that appeared to have some capabilities of creative speech production. I used the ML in the Google Cloud Speech API executed on a raspberry pi as a starting point to create my own style of machine speech interface. The audience is prompted to press a button and say something involving or about “sculpture.” A computerized voice reply comes back from the sculpture, which is a plaster mould of a hand doing the Star Trek Vulcan “peace and prosper sign.” The Star Trek reference here is intentional, for it evokes future technology and thought in a traditional sculptural form. The peace and prosperity metaphor also subtly prompts the audience to talk to the sculpture as if it is a character in a movie with agency, and evokes the sensibilities of smart devices that serve human needs and work cooperatively without conflict, much as the vulcans in Star Trek operate. Using speech recognition and custom routines based on the Google Speech API, which uses ML to recognize words, I trained the statue to answer not only repeating what the user says, but saying it as if it has agency (see video). Fig 7. A head sculpture that uses computer vision to see where the user is, and replies using digital code embodied as an LED matrix that sweeps across the mouth of the sculpture, representing machine communication. For example, whenever the user says “sculpture,” the sculpture replies with a different noun, which first appears to be referencing the user. But as the interaction proceeds, it also changes the pronunciation and verbs, and the user notices that the sculpture is using the previous noun to refer to itself, not the user. The statue is seen to have made a creative transformation in the user’s view, not by the way it has changed its interaction style, but in the way in which the audience discovers what is algorithmically already there. In user tests, the only instruction I gave was to tell the users to say anything they wanted referencing “sculpture,” but what occurred is that the users learned more and more about the rules of engagement undertaken by the statue. One user said that she thought the statue was subservient and complimentary at first, but then over the course of the interaction, it became “sassier.” What changed was not the rules, but the potential for the ML agent to surprise (and annoy) users. The form of the hand gesture as a Star Trek symbol was key as well, for users say that they expected the statue to be “high-minded and calm,” but they actually had a contentious exchange, in which both user and statue claimed to be the superior agent. Interestingly, it’s not the ML part (voice recognition and understanding) that made the surprising results possible, but rather the human intervention that involved swapping the text. Thus, I created a speech-producing statue capable of surprising and evoking an emotional reaction from users. As a final exercise, I wanted to extend the idea of creative production further than simply unexpected interactions. I decided to focus on visual representations after having previously explored the physical and language arenas. Although inspired by the ML algorithms for image association used by Google and Pikazo, I wanted to situate the piece so that the sculpture is the agent behind the “deep dreaming” undertaken by ML agents. Unlike previous efforts, I wanted to create a physical interface that appears to be producing the creative output, so that it’s not a computer using user input to create modified dreams, but the sculpture itself which makes content based on who and where the user is. To evoke the perception of creativity, Proceedings of Art Machines: International Symposium on Computational Media Art 2019 17 Part I. Full Papers (peer-reviewed) I gave the machine a human face. Humans are distinguished by their ability to manipulate and communicate using language and by their ability to creatively express themselves. I put both of these agencies in a traditionally inanimate sculpture by putting an LED matrix behind the silicone-based sculpture. The wood-grainembedded silicone retains the form of a classical statue, but forms a mesh that has hidden within it the ability to express itself. The LED matrix appears to respond to human touch due to its proximity to the silicone layer. Using Arduino to control the matrix, I created custom animations that evoked visual creation from the mouth of the statue when the user’s face was detected by an attached camera. The animations depend on where the human face is. I wanted to make a connection between human speech and machine data processing. Whereas we can express our creativity by make speeches, writing novels, or creating worlds using language, for example, the machine analogue is not human language as we know it, but a machine code that we can only visualize across a layer that blurs communication. Just as we as 3D beings cannot contemplate life in 4D, we don’t understand machine creative processing and the ways it can express itself as a form different from human conception. As humans, we can only hope to visualize the data machine produce across a layer of uncertainty. Again, it’s not the ML aspects (computer vision, image recognition, etc.) that made the sculpture surprising, but the human intervention that appeared to reveal machine “thought” using the LED matrix. Fig 8. The head sculpture lights up when a face is detected, but also moves its pixels based on where the face is in space. In this example, the face of the person whose face was cast for the 18 sculpture is detected by the statue. Users found the silicone face and LED matrix frightening at first. The red matrix evokes a type of bloodiness associated with the mouth. They found the pattern of the matrix display mesmerizing, because it tends to change form when they put their fingers on different parts of the silicone. The computer vision interaction provides users with a feeling of agency, because the light comes on only when they are close to the sculpture, and appears to track their face, a type of digital productivity. Unlike traditional sculptures, my piece evokes creative potential that contrasts with the classical form. One user said that it reminded him of the way machines would speak to each other if they were to communicate, because it “doesn’t say the same thing twice.” The silicone layer masks the lit up digital LEDs, so the effect is a filtered view of what machines would do creatively if they were creative. In summary, I created a digital machine metaphor for human creativity, which can be experienced through a filter established by classical forms. Directions The tools we create are taking over our lives. From recording our memories onto physical pages to analyzing the consequences of business investments; from enabling communication over long distance to interpreting our speech and predicting our desires, digital machines enabled by ML are going from helping us to enabling us to thinking for us. Will the most unique characteristic of humans, that of creative expression, be the next bastion to fall? Experiments with machine creativity have centred on using ML to help or imitate the human creative process. This strategy, however, is based on an anthropomorphic view that the way humans express themselves is the basis for all types of creative works, including those of machines, much as the Turing Test inherently situates machines within the human space with no regard for how non-human processes work. [10] I proposed that machine artistic expression can emerge instead from exploiting what humans think of objects and devices, allowing ML to subvert traditional forms, coalescing into Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artistic Intelligence. Ray LC (Luo) a system of creative expression beyond simply generating data from modifying previous models. In this view, the context and situation of the use of ML is just as important as algorithms, enabling a world permeated by creative machines. Indeed, we may be making machine creative expression possible not by simply coding it into their algorithms, but rather by changing the way we think about machines and how they operate. In short, the more we know about our tools, the more we learn about ourselves and our own Artistic Intelligence. References 1. P. F. Ferrari, S. Rozzi, and L. Fogassi, “Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex,” Journal of Cognitive Neuroscience 17, no. 2 (2006). 2. M. Vancatova, “Creativity and innovative behavior in primates on the example of picturemaking activity of apes,” NFU Psychology 2, no. 2 (2008). 3. Anthony Dunne, and Fiona Raby, Speculative Everything (Boston: MIT Press, 2013). 4. C. D. Hondzel, and R. Hansen, “Associating creativity, context, and experiential learning,” Journal of Education Inquiry 6, no 2 (2015). 5. Martin Gayford, “Robot art raises questions about human creativity,” MIT Technology Review (2016). https://www.technology review.com/s/600762/robot-art-raises-questions -about-human-creativity/. 6. Trevor Paglen, “Invisible images (your pictures are looking at you),” L.A. Times, April 2014. https://thenewinquiry.com/invisibleimages-your-pictures-are-looking-at-you/. 7. Ray Kurzweil, The Age of Intelligent Machines (Boston: MIT Press, 1990). 8. Clive Thompson, Smarter Than You Think: How Technology is Changing Our Minds for the Better (London: Penguin Press, 2013). 9. D. E. Rumelhart., G. E. Hinton, and R. J. Williams. “Learning representations by backpropagating errors.” Nature. 1986: 323. 10. Benjamin Bratton, “Outing AI: Beyond the Turing Test,” New York Times Opinionator, February 2015. https://opinionator.blogs. nytimes.com/2015/02/23/outing-a-i-beyond-the -turing-test/ Proceedings of Art Machines: International Symposium on Computational Media Art 2019 19 CG-Art: Demystifying the Anthropocentric Bias of Artistic Creativity Leonardo Arriagada University of Chile leoarriagada@outlook.com Abstract This aesthetic discussion examines in a philosophical-scientific way the relationship between computation and artistic creativity. Currently, there is criticism of the idea that an algorithm can be artistically creative. There are few exponents of the idea that computergenerated art (CG-Art) meets the definition of creativity proposed by Margaret Boden (2011): “the ability to come up with ideas or artifacts that are new, surprising, and valuable.” Moreover, it has been pointed out that CG-Art is not fundamentally art, because art is considered a unique and exclusive human manifestation of our species. I propose that the denial of CG-Art as art has an anthropocentric bias. To demonstrate this, I use recent studies in cognitive science on artistic creativity to show that behind the denial of creative artistic capacity to machines lies a negationist mysticism of current scientific advances. 1. Introduction Artificial intelligence (AI) has developed exponentially since the beginning of the 21st century. Every day we are surprised by algorithms that allow machines to perform tasks previously considered impossible. We receive shopping recommendations on Amazon, and reminders of our agenda thanks to Google Assistant. Car company Tesla has invested millions in autonomous driving. Such examples continue to spread. In short, it seems that every time we exclude something from the domain of AI, researchers take it as a challenge to overcome. However, all the tasks mentioned above are perceived as mechanical, so they can be modeled mathematically to be executed by a computer. Our common sense can project the 20 development of AI in the distant future and imagine that it will be possible to execute any mechanical task by an application or computer program. But can a machine create art? Is artistic creation a mathematically modelable task? 2. Is Creativity a Limit or Goal for CG-Art? The scenario just described has led philosophers, cognitive researchers and programmers to wonder if a machine has the potential to create. The subject continues to be discussed, since it requires a certain level of mathematical modeling of what we understand by creativity. In this sense, Margaret Boden (2011, 29) proposed the following definition of creativity: "the ability to come up with ideas or artifacts that are new, surprising, and valuable". I don’t think that, in general, anyone would completely object to this definition. Thus, creativity in general must include novelty, surprise and value. Regarding the particularity of artistic creativity, I believe that it is precisely the "value" aspect of the definition that is the most controversial when analyzing creative algorithms. I will return to this later, and I will try to show that denying value to artistic creations overlooks two important facts: the mechanical evaluation of artistic work and robotic embodiment (both topics are addressed in section 4). Creative algorithms have undergone fruitful development thanks to models based on Artificial Neural Networks (ANN). In particular, a subtype of these models, Generative Adversarial Networks (GAN) allowed the computer program AlphaGo to defeat Lee Sedol, considered the best human Go player in the world. This competition was used to show that GAN effectively created movements that seem irrational to humans. Therefore, it is no Proceedings of Art Machines: International Symposium on Computational Media Art 2019 CG-Art: Demystifying the Anthropocentric Bias of Artistic Creativity. Leonardo Arriagada longer absurd to argue that algorithms can at least create Go plays that are novel and surprising. However, is this homologous to artistic creation? What about the aesthetic assessment of the creations of machines? The fundamental point here is that Boden (2011) defined a special type of art by joining the concepts "creativity" and "computing". Thus, CG-Art is understood as "the artwork results from some computer program being left to run by itself, with minimal or zero interference from a human being" (Boden 2011). There are numerous examples of algorithms that have been trained to produce aesthetically pleasing output for human evaluation. AARON, by Harlod Cohen, and EMI, by David Cope, are classic illustrations of this type of art. In both cases the programmers were dedicated only to improving the algorithms, leaving the creation up to the software itself. But the general opinion is that the creations of AARON and EMI are the authorship of Cohen and Cope, not of the software itself. I discuss this point in section 6. But first it is necessary to understand what is meant by art and why the lay public generally consider it distinct from mathematical modeling. 3. Is Mystical Inspiration the Only Explanation for Artistic Creativity? We have seen that the GAN can create a novel and surprising play. But a third characteristic is still missing to satisfy Boden's definition of creativity: "value". I do not analyze here the value of AlphaGo's creations. My subject of investigation is algorithms for creating art, so the value I refer to here is the aesthetic type. I think that if it is already controversial to say that AlphaGo creates plays, it is problematic to affirm that machines can deliver output of aesthetic value. In this regard, Aaron Hertzmann (2018) points out: "The concepts of art and inspiration are often spoken of in mystical terms, something special and primal beyond the realm of science and technology; it is as if only humans create art because only humans have "souls." Surely, there should be a more scientific explanation Hertzmann, despite rejecting the idea that a computer program can create art, forces us to question our concept of art. In fact, I agree that most artists are reluctant to believe CG-Art is art. But the arguments they use ultimately appeal to the mystical qualities of "talent" and "inspiration". It is not my goal to refute the mystical vision of art that many artists share. I understand that it is a matter of faith and therefore, impossible to refute. Of course, there cannot be a mathematical modeling of this concept of art either. Considering the above, I will dedicate myself to investigating aesthetic aspects of algorithms or machine creations. Even so, I point out that a very simple objection to the lack of a mystical connection in CG-Art is that it is different from human art. Thus, although human artists may choose to believe in mysticism, potential computer artists do not have to submit to this requirement. I consider it much more productive to study the aesthetic value of CG-Art without appealing to concepts such as "talent". 4. The Aesthetic Value of CG-Art. Two Approaches: Human and Mechanical Evaluation Next I will examine how CG-Art can effectively produce novel and surprising works. In effect, this can be achieved by random combinations. This is not a topic that I will delve into in this text. But I postulate that the most debatable characteristic of CG-Art is its aesthetic value. I will show that CG-Art does meet this requirement through two approaches, one focused on human evaluation and the other on machine evaluation. 4.1 Human Evaluation of the Aesthetic Value of CG-Art Recently, people’s perceptions of CG-Art were evaluated. In the article "Putting the Art in Artificial: Aesthetic responses to computergenerated art" (Chamberlain et al. 2017), the researchers studied how human observers respond to artworks generated by computers and by humans. The findings indicate a negative bias towards CG-Art. Predictably, the works in which the CG-Art expressed plastically representational features were qualified as more Proceedings of Art Machines: International Symposium on Computational Media Art 2019 21 Part I. Full Papers (peer-reviewed) artificial than abstract works. In the same way, the observers valued imitations of brush strokes and small imperfections in CG-Art works more highly. Chamberlain et al. (2017) verified that this negative prejudice towards the aesthetic value of the CG-Art diminishes when the observer can see the production of the work. This led them to suggest that increasing the anthropomorphic characteristics of a robot could tend to eliminate hostility towards CG-Art. Indeed, it seems that a human observer expects to see artists working on their artwork. The "black box" model, in which only the printed output of an algorithm can be seen, moves away from the current human vision of artistic creation. The simple fact of seeing a robotic arm painting on a canvas increases the observer's empathy. Chamberlain et al. (2017) postulate that this may be the result of the activation of mirror neurons in the human brain. If the "black box" model in which CG-Art works is a handicap for its aesthetic value, it is interesting to think what would happen if we can overcome it. Unfortunately, we still do not have the technology to create a robot of the complexity that the Westworld series (2016) invites us to imagine. However, we can overcome this handicap by presenting human works and CG-Art without telling the observer which one is which. It is precisely this aspect that is investigated in the article "CAN: Creative Adversarial Networks Generating "Art" by Learning about Styles and Deviating from Style Norms" (Elgammal et al. 2017). Through a GAN modification, the researchers developed Creative Adversarial Networks (CAN). Basically, the algorithm was optimized so that it was not dedicated just to emulating human art styles, but it was really creative. This point is developed in section 4.2. The findings in this study showed that humans assign a higher score to the CG-Art created by CAN, surpassing a sample of Abstract Expressionism at premier art show Art Basel in 2016. The participants were asked to assign a score of 1 to 5 for the qualitative indicators of intentionality, visual structure, communication and inspiration. In each of the items the CG-Art was given a higher score than human art. 22 In conclusion, I postulate that in a blind test CG-Art has aesthetic value for humans. However, artworks by human artists receive constant aesthetic appreciation. So far we have discussed only the external valuation of observers. Can an algorithm aesthetically evaluate its own art? 4.2 Mechanical Evaluation of the Aesthetic Value of CG-Art When Harold Cohen wrote the computer program AARON, which he designed to produce art autonomously, he filtered the output that seemed aesthetically valuable to him. Since then, algorithms based on artificial neural networks (ANN) have progressed considerably. As I mentioned earlier, the GAN subtype is the most widely used today. I explain below that in a GAN network (and of course in a CAN network), an aesthetic evaluation is performed by the same algorithm. First, we need to understand how a GAN network works. According to Elgammal et al. (2017, 5), "a Generative Adversarial Network (GAN) has two sub networks, a generator and a discriminator. The discriminator has access to a set of images (training images). The discriminator tries to discriminate between "real" images (from the training set) and "fake" images generated by the generator. The generator tries to generate images similar to the training set without seeing these images. The generator starts by generating random images and receives a signal from the discriminator if the discriminator finds them real or fake." This dual model has aesthetic assessment incorporated into it. In effect, when the "discriminator" is deceived by the "generator", the GAN reaches an aesthetic value similar to that of the original set of training images that was provided to it. It follows from this that a GAN does not create art, but rather emulates artistic styles. Fortunately, the CAN subtype has been created specifically to move away from imitation and achieve authentic creation by an algorithm. As explained by Elgammal et al. (2017, 13), in this modification of the GAN, the discriminator gives the generator two signals: a) the classification of art or non-art, and b) Proceedings of Art Machines: International Symposium on Computational Media Art 2019 CG-Art: Demystifying the Anthropocentric Bias of Artistic Creativity. Leonardo Arriagada correspondence to a specific artistic style. In this way, "the proposed CAN model generates images that can be characterized as novel and not emulating the art distribution, however aesthetically appealing." Therefore, the statement by Hertzmann (2018, 19) that "unlike human artists, these systems do not grow or evolve over time" does not seem justifiable. A CAN is capable of creating and evaluating on its own. I suggest that these capabilities allow us to affirm that a CAN does grow and evolve aesthetically. 5. CG-Art and Society The final criticism that Hartzmann (2018) makes of CG-Art is that art is social, and since computers are not "social agents", they cannot create art. I will discuss this briefly with two answers that I think are pertinent to analyze and develop in the future. First, Hartzmann seems to forget that he is talking about CG-Art. Although it seems indisputable that art created by humans is social, in the terms stated in Hartzmann (2018), this does not mean that CG-Art must be social. It is analyzing an algorithm. The way in which algorithms relate is not a subject widely studied even in sociology. In my opinion, we cannot conclude that a computer cannot create art because it is not a social entity. Obviously, an algorithm doesn’t have the same kind of experiences a human has. The point here is to see if you can create art, not if you can create human art. The latter is not possible at present. Perhaps in the future, with an algorithm implanted in an anthropomorphized body, social interactions can be achieved that will allow this condition to be fulfilled. But there is no aspiration for CG-Art to be considered human. Their ways of knowing and experiencing are different. CG-Art is fundamentally based on Big Data, which is actually the most social thing we have, since it shows patterns of social behavior. Therefore, it is not surprising that in blind tests CG-Art is valued aesthetically, since it is based on a small sample of Big Data. I postulate that if we are optimistic and wait for the Big Data used by the CG-Art to be extended, we will have aesthetic works never thought of by humans. 6. Collaboration. Authorship. Apprentice and Teacher. Codes and Laws. "To date, there is a rich body of computergenerated art, and in all cases, the work is credited to the human artist (s) behind the tools, such as the authors or users of the software – and this might never change. "(Hertzmann 2018, 2) A final objection to CG-Art is related to the authorship of the artworks. Many postulate that the real authors of the artworks of a machine are the programmers of their code. In my opinion, this is incorrect for two reasons. First, and as Hertzmann himself (2018) recognizes, human artistic work is social. Therefore, it involves many agents. It is not necessary for only one of them to be considered an artist, since the agents may fulfill different functions. Let me give an example to clarify this point. When a film is shot we have at least a director and actors collaborating artistically. Both functions hybridize and complement each other. We cannot say, for example, that the artwork "film" is the creation only of the director and not the actors. In effect, we say that the director fulfills the artistic function of "directing" and the actors of "acting". In both cases, art has been created and a film, which is an artwork in itself, has been created. CG-Art invites us to think about a new art form, much more current, which underlies the collective creation. It can be argued that the programmer and the algorithm are the artists. This is so, I postulate, because unlike working with a brush, the algorithm acts as a kind of creative agent, or a colleague. Second, I propose an analogy in which a creative algorithm is to its programmer as human apprentice is to a human master. If we assume that human art is social, then we can understand that there is no artist who has not had a teacher. This role of teacher can be exercised by an expert, who explicitly teaches, or by experiences lived by an artist, without being attributed to any human being in particular. In both cases, the artwork was nurtured by prior learning. CG-Art is also based on learning. Its teacher may be its programmer, another algorithm, a sample of artworks, and so forth. Learning from an agent does not prohibit CG-Art from creating Proceedings of Art Machines: International Symposium on Computational Media Art 2019 23 Part I. Full Papers (peer-reviewed) its own art. In the same way, human apprentices do not have to grant authorship of their work to their teachers. Finally, there are criticisms of CG-Art based on the fact that an algorithm is a code and therefore cannot create because it follows rigid rules. But everything that exists follows inviolable rules. For example, neither CG-Art nor a human artist can violate physical laws. That is a real limitation for both types of art. Also, we all have a code that we follow. The computations performed by a CAN are complex instructions written by programmers at first, but then by the same algorithm throughout their learning. In the case of humans, we all develop genetically according to our DNA, which is a code we are born with. No artists would feel limited by having to respect physical laws and being forced to develop according to their genetic code. These criticisms seem worth investigating and developing to clarify these points with the lay public. Elgammal, Amhed, Bingchen Liu, Mohamed Elhoseiny, & Marian Mazzone. “CAN: Creative Adversarial Networks Generating "Art" by Learning About Styles and Deviating from Style Norms.” arXiv:1706.07068v1, 2017. Hertzmann, Aaron. “Can Computers Create Art?” Arts, 2018. 7. Conclusion This article examined the relationship between computation and artistic creativity philosophically and scientifically. It argues that CG-Art is a new art form and that most of its criticisms are made from an anthropocentric viewpoint. CG-Art is not human art and is not intended to be. The works of CG-Art satisfy the criteria of novelty, surprise and aesthetic value. Moreover, in the face of blind tests, human observers consider CG-Art to be more creative than art created by humans. I therefore consider that greater analysis of CG-Art will allow us to broaden our aesthetic conception of what art is, as long as it is studied without prejudice. Bibliography Boden, Margaret Creativity and Art: Three Roads to Surprise. New York: Oxford University Press, 2011. Chamberlain, Rebecca; Caitlin R Mullin, & Johan Wagemas. “Putting the Art in Artificial: Aesthetic responses to computergenerated art.” Psychology of Aesthetics Creativity and the Arts, 2017. 24 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation Sofian Audry School of Computing and Information Science University of Maine, Orono, ME, USA1 sofian.audry@maine.edu Abstract Machine learning has traditionally focused on problem-solving and optimization. But contemporary conceptions of art usually describe art as non-purposeful and nonoptimizable. In this paper, I propose an alternative approach to using machine learning for artistic creation by using the training phase itself as a generative process of new aesthetic forms. Contextualizing my approach within media art history and the history of artificial intelligence, I describe a series of experiments performed using this approach using Long Short-Term Memory (LSTM) recurrent neural networks applied to text generation. Introduction Machine learning has recently become a popular approach for studying artistic creativity and creating new forms of art. Oftentimes, this requires framing the creative process as a problem to be solved using some form of optimization. For example, such approaches have been used to evolve new 3D creatures based on subjective preferences; [1,2] to generate music scores that “sound like” the dataset they have been trained on; [3,4] to transfer a painter's style onto another painting; [5] and even to generate images that often feel “more artistic” (at least to the layman) than those of contemporary painters. [6] Indeed, machine learning is designed to recognize regular patterns, and when employed for generative purposes, is attuned to reproducing things that already exist. Artists, in 1 contrast, seek to create the unexpected. Optimization is inherently dichotomic to artistic practice. Studies that try to tackle artistic production as an optimization problem are immediately faced with problems such as the existence of multiple maxima (e.g., there is no such thing as “the best movie” or “the best painting”); the possibly infinite and incommensurable domains in which artworks exist; and the fact that art is often precisely described as non-purposeful and nonoptimizable. [7,8] In this paper, I explore an approach to computational art that uses the optimization process of machine learning algorithms as a raw material. This technique unrolls the iterative steps in the training phase, thus revealing the temporal structure of the learning agent's behavior. I examine one particular set of experiments that was conducted using this technique, involving a deep learning model known as a long short-term memory (LSTM) recurrent neural network, trained on a text database. The creative artistic and technical approach is presented, as well as the outcomes. Finally, I discuss the implications of the work in the field of computational media art. Context Machine learning finds its origin in cybernetics, a disruptive science that impacted not only computer science and artificial intelligence, but also biology, neurology, sociology, anthropology, and economics. Furthermore, it had a profound impact on art in the 1960s, and This research was initiated and conducted as part of my postdoctoral studies at the Comparative Media Studies/Writing, Massachusetts Institute of Technology, Cambridge, MA, USA. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 25 Part I. Full Papers (peer-reviewed) foreshadowed the later development of new media art. One of the central concepts of cybernetics was that of systems or agents, some of which, using feedback from their environment, were able to adapt over time by trial and error. [9] This very basic concept of an agent iteratively and incrementally adapting to its environment by adjusting its own structure is at the core of deep learning, which is based on layers of densely interconnected agents, called neurons, which work together to achieve a greater, more complex level of agency at the global scope. In current deep learning applications, these millions of agents are force-fed gigabytes of data, resulting after several iterations in the foie gras of the deep learning revolution: fully optimized models often performing above human level. Since the 1950s, many artists have exploited the adaptive features of cybernetics systems and other learning agents, not by applying optimized models, but by exploding the learning process itself, often running it in real time. Consider, for example, Hungarian artist Nicolas Schöffer’s piece CYSP I, which was directly inspired by Norbert Wiener’s theory of control and communication. [10, p. 472] Or Karl Sims’ Galápagos (1997), in which visitors are asked to select their favorite artificial 3D creatures in a virtual environment, and where the selected creatures’ genetic code is then used to create the next generation using genetic algorithms. Performative Ecologies (2008— 2010), by architect Ruairi Glynn, is another example. Inspired by the work of Gordon Pask, especially his 1968 installation Colloquy of Mobiles, Glynn’s installation creates a conversational space in which dancing robots evolve in constant interaction with one another and with the public. Most of my own work over the past decade has focused on the design of computational artificial agents, and documenting the performance behavior of these agents in the real world. For example, in my series of site-specific interventions Absences (2008-2011), I created small, autonomous, ephemeral agents that acted within natural environments, such as forests and mountains. My robotics installation 26 Vessels (2010-2015), created in collaboration with Samuel St-Aubin and Stephen Kelly, involves a group of autonomous, water-dwelling robots that react collectively to their environment through an emerging group behavior. Through this earlier research I developed an interest in how self-organizing and adaptive processes impact both artistic practice and the viewer’s experience. Hence, in Vessels, a genetic algorithm procedure is used to allow robots to collectively converge to a common group behavior. A similar mechanism has been explored by Stephen Kelly in his work Open Ended Ensembles (2016), in which two agents use genetic programming (GP) to move along a fluorescent tube. Artist and media theorist Simon Penny calls these kinds of works “embodied cultural agents” or “agents as artworks” and integrates them within the larger framework of an “aesthetic of behavior”, a “new aesthetic field opened up by the possibility of cultural interaction with machine systems”. [11] These works are distinct from so-called generative art, which uses computer algorithms to produce stabilized morphologies, such as images and sound: their aesthetics are about the performance of a program as it unfolds in real-time in the world through a situated artificial body. In my past work, I developed an ontological framework of behaviors by looking at the distinctive way behavior morphologies unfold over time. [12] While existing taxonomies of cybernetics systems have focused mainly on their relational and structural aspects, I look at the temporal dimension of agent behaviors and its aesthetic potential. [13,9] In particular, I hypothesize that adaptive behaviors are distinguished from non-adaptive behaviors by their ability to change over time and therefore belong to a “second order” of behaviors – those whose behavior evolves over time. With that in mind, we can start considering how the shape of a behavior emerges from randomness (morphogenesis), transforms over time (metamorphosis), or remains stable (morphostasis). Using this framework, we can establish that most learning algorithms go through a phase of morphogenesis, during which their behavior Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Sofian Audry changes, until they eventually stabilize in a final stage of morphostasis. I posit that this process of transformation and stabilization is artistically relevant and can be harnessed as a creative method. Fig. 1: Schematization of the temporal evolution of an adaptive behavior. Distance along the vertical axis represents difference in the form of observable events produced by the agent. The graphic shows how second-order, adaptive behaviors iteratively change over time through a process of morphogenesis, until they stabilize into an optimal first-order behavior, thus entering the phase of morphostasis. Approach In this research, machine learning is used to generate new forms of behavior. Following cybernetician Gordon Pask, we define a behavior as a stable form of events caused by an agent, as perceived by an external observer. [14, p. 18] This work fits within the larger artistic discipline of agent-based art – what artist Simon Penny calls “behavior aesthetics”. These works engage the performance of one or many synthetic agents as they unfold temporally in the world through situated artificial bodies. [11, 398] Such works are distinct from so-called “generative art” or “algorithmic art”, which use algorithmic processes not as an end, but as a means to produce stabilized morphologies, such as images, sound, and text. [12] This study involves a series of artworks in which LSTM recurrent neural networks were trained on a single text corpus: a version of Emily Brontë's novel Wuthering Heights, adapted from the Gutenberg online library. 2 2 3 http://www.gutenberg.org/cache/epub/768/pg768.txt The source code used in this project is available here: https://github.com/sofian/readings Snapshots of the trained models were saved on disk at different steps in the learning process, resulting in a set of increasingly optimal models. These models were then used as part of a generative process to create a new text.3 The first artistic output of that approach, for the sleepers in that quiet earth, takes the form of an artbook printed as a series of 31 unique copies,4 each of which has 642,746 characters – the same length as the version of Wuthering Heights that was used for training the neural network. Each copy is generated by a deep learning agent, known as LSTM, trained on the book. LSTM recurrent neural networks are a kind of artificial neural network with recurrent connections, which can “learn” from sequences of data, such as words and characters. They are used in state-of-the-art language processing applications, such as speech recognition and automated translation. The result is a unique record of the agent as it reads the book and learns the probability distribution of characters, thus somehow becoming increasingly “familiar” with its syntax and style, while at the same time becoming more and more complex in its generative features. This unicity is important, because I see the work less as a trace of the agent's behavior than as a way to experience its behavior as if it were happening in real time. Like many other deep learning systems, LSTM agents are both predictive and generative. In most scientific applications, it is their predictive capabilities that people are interested in. For example, in machine translation, deep learning systems of the LSTM type are used to compare the probability of different candidate translations and keep the one that is more likely. Another unique feature of deep-learning systems is that unlike other AI approaches, they improve iteratively. Starting from nothing, as they become more and more exposed to data, they improve and become better at prediction, which also directly impacts their generative capabilities, if they have any. 4 The work is published at Bad Quarto. Editor: Nick Montfort. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 27 Part I. Full Papers (peer-reviewed) These two ideas – generation and adaptation – form the basis of for the sleepers in that quiet earth. My intention in this work was not so much to produce an accurate “optimal” system that could generate rich, human-level, grammarcorrect sentences. Instead, I sought to allow the hesitant, strenuous learning process of the system to reveal itself as it goes through all of its sub-optimal states of being. Another key conceptual dimension of the work resides in the ability of the agent to be both a reader and a writer. If we picture the text of Wuthering Heights as the “world” in which the agent dwells and tries to make sense of by “reading” sequences of characters, then as it becomes more familiar with its environment, it is also able to “write” new sequences, which can give an insight into the agent's understanding of its world. The performance trace of this agent is made concrete in the archetypal object of authorship: a book. I decided to distribute only a printed version of this book, not a digital version. This aspect of the work is crucial, as it lends a physical materiality to the agent and confers an identity beyond its abstract virtual existence. The artbook format contributes to the hybrid nature of the work, combining visual arts, electronic arts, and electronic literature. The second output of the project is a series of two sound-art pieces and one performance realized in collaboration with Erin Gee5. These works explore different modes of revoicing texts generated by the algorithm, using a technique known as Autonomous Sensory Meridian Response (ASMR), which involves the use of sonic “triggers”, such as gentle whispering, or fingers scratching or tapping, to induce tingling sensations and pleasurable auditory-tactile synaesthesia in the user. The phrases of the soone and to the sooe are variations on the incremental learning process used in for the sleepers in that quiet earth, but using a shorter text generated by a simpler model. Finally, the 5 6 28 https://eringee.net As a point of comparison, consider the difficulty of learning how to write a book in an language unknown to you, with the only information being a single book written in the language. work Machine Unlearning reverses the process as part of a live performance, in which Gee reads a generative text that starts with the fully trained neural network and slowly regresses to randomness. Preprocessing Wuthering Heights contains a few more than 600,000 characters, which is rather small compared to state-of-the-art language modelling datasets, which usually contain several million characters. 6 Starting with an open-access version of Wuthering Heights. [15] I slightly reduced the complexity of the learning task by reducing the number of different characters encountered, by (1) making all the letters lowercase (so that the agent does not need to distinguish between uppercase and lowercase letters); and (2) removing low-frequency characters such as parentheses, which appeared only a few times in the text and would only confuse the agent.7 Training To produce the work, an LSTM was trained on the complete text of Wuthering Heights 8 over many iterations. Snapshots of the agent's weights were saved at different steps in the learning process, from the beginning, where it is initialized randomly, to the end, after it has read the book 150 times. Learning was asymptotic, with many changes happening during the first steps of training. This resulted in the system appearing already “overly trained” after the first epoch. To compensate for this, I saved 200 snapshots during this first runthrough using mini-batches of different sizes (Fig. 2). 7 8 The preprocessed version of the text which was used as the training set is available here: https://github.com/sofian/readings/blob/master/data/ wuthering.txt Some basic preprocessing was done to the text, as I explain later. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Sofian Audry Fig. 2: Training loss (categorical cross-entropy) plotted against (a) the training epoch for the first 75 epochs, and (b) the saved model number up to the first 75 epochs. These graphs show how the process of saving models during the first epoch flattened the learning curve, allowing for more fine-grained evolutions during the generative step. Notice that the first 200 saved models happened during the first epoch alone. These 351 snapshots – one in the starting state, 200 during the first epoch9, and 150 (one per epoch) for the rest of the process – were then used in a generative fashion to produce each version of the work. Each snapshot was used to generate an approximately equal portion of the 642,746 characters in the book. The way the LSTM was trained helps understand its behavior during the generative phase. The network modelled the distribution of sequential text patterns by estimating the conditional probability of the next character xi given the past N characters hi = xi-N … xi-1: P(xi|hi) This probability distribution is represented by a function that produces one probability value for each possible character. For example, let us say that the N=10 previous characters seen by the agent are “wutherin”. After training, we would expect the agent to emit a high probability P(g|wutherin) for the letter g (wuthering), a lower probability P(’|wutherin) for a single quote (’) (wutherin’), and near-zero probability for every other character. The network can then be used to generate new sequences, simply by sampling randomly using the distribution and repeating the procedure. To get back to our previous example, after choosing the letter g, the agent would sample a new character, this time using the input “uthering” – in which case we would likely expect a high 9 probability of s, a white space (_), and other punctuation marks (.,?!). This kind of statistical approach, which looks at the previous N units in a sequence, is known as the Markovian process, which is very common in natural language processing. [16] One of its limitations is that it makes the assumption that the closest elements in the past are the most important for predicting the future, which is an imperfect premise to say the least, especially when it comes to language, where there are often very long-term dependencies. This explains to a large extent why the sentences generated by the agent, even in the later stages of training, are somehow detached from one another, as the neural network fails to grasp long-term dependencies between sentences. To model this probability distribution, I used an LSTM network with two layers of fully interconnected hidden units with 200 neurons each. Input streams were sent by chunks of 100 characters using a sliding window (N=100). Input characters were represented using embeddings, a technique in which each symbol is represented by a vector, which is itself trained. For example, in this work, I used embeddings of size 5, which means that each character is represented by 5 different values. These values can be seen as a representation of different characteristics of each character that can be useful for the system to make better predictions over sequences. For example, the first value might represent whether the letter is a vowel, and the second value whether it is a punctuation mark. [14] Generating After the training, I obtained a series of probability distributions at different stages of the evolution of the model, which were then used to generate each book. Let f(x|h, θ) be the output of the LSTM for character x, given the N past characters h and the set of weights θ. The probability distribution is represented by the LSTM using the following softmax function: In machine learning jargon, an epoch corresponds to one full iteration over the training dataset – in this case, the complete novel. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 29 Part I. Full Papers (peer-reviewed) where Vn=Vn(h,θ) denotes the set of n characters x with the largest value fθ (x|h). where V is the set of all possible characters (i.e., the vocabulary). Here the hyper-parameter ∈ [0,∞] is called the temperature and is typically set to 1. Raising the temperature spreads out the probabilities, making them more uniform, while lowering it makes the distribution peakier, thus making the agent even more likely to choose the letter with highest probability. Temperature Adjustment After some experiments, I noticed that the probability distributions in the early stages were “spread” too much across the characters (i.e., there were not too many differences between each probability) and that the agent would thus generate text that appeared “too random” for my taste. I therefore decided to slightly adjust the probability distribution to make it more “peaky” by decreasing the temperature – thus effectively heightening the probability of the most probable elements and decreasing the probability of the others. However, this approach seemed too “greedy” in later stages, in which the agent became complex enough to consider different sequences of construction and completion. Thus, as the agent’s training progressed, I adjusted the probability distribution to be more “spread-out” to encourage diversity (Fig. 3). Shortlist Still, since no character had zero probability, there were always cases in which the agent would accidentally generate a completely arbitrary character. To limit this phenomenon while allowing variety, I forced the agent to choose among only a shortlist of the n most probable characters. So the final probability distribution is as follows: Fig. 3: Evolution of temperature ( ) throughout the bookgeneration process. Transitions between Models Finally, to allow for smooth transitions between each block of text generated by each model, in the last part of each section, I interpolated the probability distributions of the current model and the next model to generate each character. This was parameterized by a transition factor ∈ [0, 1], representing the point of transition in each block at which I start interpolating. To generate for the sleepers in that quiet earth, we used ; therefore, the last 20% of each of the 351 blocks of text (each averaging 1833 characters) was obtained by linearly interpolating the current probability distribution and the one of the next trained model. Postprocessing The final production of the artbooks for the sleepers in that quiet earth involved an additional step. Through discussions with editor Nick Montfort, we implemented a few minor changes to convert the raw generated text into book format. For instance, we interpreted the appearance of the word “chapter” followed by roman letters in the generative text (eg. “chapter xix”)10 as an indication of a new chapter, which we therefore formatted differently with a page break and bold typeface. Results This section discusses the results of the generative process through an in-depth 10 Notice that these appear randomly. For example, “chapter xi” might appear before “chapter iii”. 30 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Sofian Audry examination of an unpublished version of for the sleepers in that quiet earth. In this section, I describe the progress of the agent as it runs through the reading in terms of time. Here “time” is understood in terms of character position and is represented by the symbol t. There are 642,746 individual characters in the original text. So for example, at time t=64,274 the agent is about 10% into the book, and at time t=321,373 it is halfway through. Morphogenesis The behavior of the writing agent throughout the learning process manifests itself in a number of different ways, corresponding to the state of the agent as it becomes more and more attuned to the “world” it lives in – that is, the text it is reading. As is traditionally done, the neural network was initialized with random weights, representing a neutral state. At this point, the agent had not been subjected to any observations and therefore, had no understanding of the world. Accordingly, in the first few pages of the book, the agent behaved completely randomly, as it had been initialized with random weights. The agent then proceeded to read the book one character at a time to build an internal representation of how character sequences are generated in Brontë’s novel – in other words, by building a model of the author’s style. In so doing, it learned more and more about the author’s style as it read, starting with building a comprehension of sequences at the character level and incrementally building from this to groups of two, three and four characters, forming syllables, then words, and finally complete sentences. Following is a case study of a particular unpublished “reading” of the book, and thus construction of an LSTM agent. Here is an excerpt of the first “sentence” generated by the agent: Excerpt at t=0 Early on in the training (after reading a few characters), the agent started to utter erratically some of the characters it had seen: Excerpt at t=40 Later on, when it had seen more, it became obsessed with white spaces and frequent characters such as the letter “e”. Excerpt at t=530 These fixations can be explained through the probabilistic approach governing the system. More frequent characters simply have a higher probability of appearing in the text. For example, imagine yourself pointing to a random character in a book and trying to guess what it is without any context; you would likely have a higher chance of making the right guess if you chose a white space than a character. After reading a few hundred characters, the letters produced by the neural net became more condensed, and we saw appearing some character duplicates. These were the early steps of the agent moving from merely counting the frequency of characters as a predictive measurement. After it read about 5% of the book, the letters became more condensed and the agent even started to tentatively concatenate frequent letters: Excerpt at t=33,490 The Glitch Surprisingly, not long after this point, the agent seemed to regress to an earlier stage and started behaving erratically for a while. This event Proceedings of Art Machines: International Symposium on Computational Media Art 2019 31 Part I. Full Papers (peer-reviewed) happened in only one specific case. I have not been able to replicate this or explain the reasons for this glitch, despite several attempts. Excerpt at t=59,410 Excerpt at t=43,090 My best explanation is that this was due to an early attempt by the neural network to make sense of double-quotes (“”), which is one of the hardest mechanisms to understand for a neural network, as it involves looking backwards to a previous point in the sequence – as opposed to learning about syllables, which involves looking back only one or two characters. This, as well as the presence of tentative sequences of double-quotes in the next few learning steps, give a hint in this direction – although I was not able to verify it with certainty. Importantly, whereas I ran several training procedures to produce the work, tuning the model and the training procedure, this “glitch” appeared in only one of these experiments. Even a slight modification in the training data, such as removing the chapter titles at one point, prevented the appearance of the glitch. Since I thought this was such a fascinating accident, I decided to work with the specific experiment that produced it. Morphemes and Proto-Words Not long after resolving the “glitch”, the agent eventually relaxed its generation of spaces. It seemed to have finally learned one of the most basic principles of English language: the separation of groups of letters using individual spaces. From this point on, it started to tentatively build morphemes of increased length, separated by a single space. Sequences were first limited to a series of one, two or three of the most frequent characters. 32 Soon the agent started combining more diverse groups of letters. Short words even started appearing. Excerpt at t=113,170 This was shortly followed by early attempts to build short sequences of words, some of which were even correct English, such as “in the”, “that is”, “the mind” and “the mister”. Excerpt at t=215,570 Punctuation and Sentences After reading about a third of the book, the agent started using punctuation. For example, here is the first use of commas: Excerpt at t=227,090 At about two thirds through the book, the agent could construct sentences of varying length, making syntactically appropriate use of periods, commas, and quotes. The sentences were mostly nonsensical and grammatically imperfect. Yet they seem to mirror some of the core aspects of the original text, including the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Sofian Audry use of the first person, an abundance of dialogue, and the construction of long sentences with many complementary clauses, a style that was common in 19th century English literature. Above all, it was the rhythmic qualities of the text produced by the artificial agent that bore the closest resemblance to Brontë’s style. Excerpt at t=448,530 For comparison, consider this excerpt from Chapter VIII of Wuthering Heights: I guess she is; yet she looks bravely,’ replied the girl, ‘and she talks as if she thought of living to see it grow a man. She’s out of her head for joy, it’s such a beauty! If I were her I’m certain I should not die: I should get better at the bare sight of it, in spite of Kenneth. I was fairly mad at him. Dame Archer brought the cherub down to master, in the house, and his face just began to light up, when the old croaker steps forward, and says he—“Earnshaw, it’s a blessing your wife has been spared to leave you this son. When she came, I felt convinced we shouldn’t keep her long; and now, I must tell you, the winter will probably finish her. Don’t take on, and fret about it too much: it can’t be helped. And besides, you should have known better than to choose such a rush of a lass!” [15] Improvements This is an excerpt after one epoch of training – that is, after the agent had read the book once. At this point the agent had learned to generate complete sentences, with a few glitches. Many of these sentences are still grammatically incorrect and somewhat random. It is as if the agent could only “see” two or three words in the past, with usually only short sequences of two or three words making logical sense together. Consider for example the progression in the following sentence generated after the first epoch: Excerpt at epoch 1 From this point forward, the neural network was trained for several epochs, having re-read the novel up to 150 times. Changes in the agent’s output become less perceptible over these later iterations. The first epoch allowed the agent to grow from pure randomness to building morphemes, words, and full sentences with punctuation. In the following iterations, the agent seemed to expand these basic building blocks by (1) polishing grammar, (2) expanding vocabulary, and (3) diversifying the length and structure of sentences, including producing dialogic constructs that are common in the original text. To get a sense of this evolution, here are some sample sentences from epochs 20, 80, and 150, which may give a sense of the transformation in the agent’s behavior. Excerpt at epoch 20 Excerpt at epoch 80 Excerpt at epoch 150 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 33 Part I. Full Papers (peer-reviewed) Machine Unlearning Proceeding incrementally using models of increasing accuracy is not the only way the suggested method can be used. In Machine Unlearning,11 artist Erin Gee performs using a voice technique known as Autonomous Sensory Meridian Response (ASMR). She reads a text which was generated using the inverted process presented above. Here we simply regress from a fully optimized system down to an untrained model. Following is an example of such a text, which was read by Gee during the work’s premiere in May 2018: The generative text read in Machine Unlearning (2018). Conclusion The computational artworks described in this paper span diverse approaches, such as electronic literature, generative art, and behavior aesthetics. They make use of deep learning recurrent neural networks, not so much as a way to generate novel and creative writing by taking advantage of the system’s ability to imitate human performance, but to reveal the learning process of the system. In other words, the approach explored in this study subverts the core purpose of artificial intelligence, whose aim is to reproduce or exceed human performance, in this case, by imitating the style of a well-known English author. Instead, it focuses on the behavior of the artificial agent as it tentatively tries to achieve its goals. Rather than focusing on the literary prowess such computational systems can achieve when they are fully optimized, these works offer a unique insight into the inner workings of a machine learning algorithm by turning the experience of reading and listening into an encounter with a learning agent. While these works are certainly different in many respects from canonical forms of agent-based artworks (such as those employing situated robotic systems), it shares with them a unique focus on using behavior as an artistic form on its own – in these cases, through experiencing the learning journey of an artificial deep learning agent. More research needs to be done to understand the relationship between the learning curve and the perception of behaviors, looking at how changes in the error rate correspond to observable changes in the agent’s behavior. Furthermore, while this study is limited to the specific domain of text generators, future works should focus on applying the approach to other domains, such as robotics, sound and images. Acknowledgements The author would like to thank the Fonds de Recherche du Québec – Société et Culture, the Massachusetts Institute of Technology, 11 https://eringee.net/voice-of-echo-ii-meta-marathondusseldorf 34 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Unrolling the Learning Curve: Aesthetics of Adaptive Behaviors with Deep Recurrent Nets for Text Generation. Soﬁan Audry NVIDIA Corporation, The Trope Tank, Bad Quarto, and Dr Nick Montfort for their support. References 1. Karl Sims, “Evolving Virtual Creatures.” ACM Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (1994): 15–22. https://doi.org/10.1145/192161.192167. 2. Stephen Todd and William Latham, Evolutionary Art and Computers. (Academic Press, 1992). 3. Gaëtan Hadjieres, François Pachet, and Frank Nielsen, “DeepBach: A Steerable Model for Bach Chorales Generation,” ArXiv:1612.01010 [Cs], December 3, 2016. http://arxiv.org/abs/1612.01010. 4. Douglas Eck and Juergen Schmidhuber, “A First Look at Music Composition Using LSTM Recurrent Neural Networks.” Technical report” Manno, Switzerland: Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale, March 15, 2002.http://dl.acm.org/citation.cfm?id=870511. 5. Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. “A Neural Algorithm of Artistic Style.” ArXiv:1508.06576 [Cs, q-Bio], August 26, 2015. http://arxiv.org/abs/1508. 06576. 6. Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone, “CAN: Creative Adversarial Networks, Generating ‘Art’ by Learning About Styles and Deviating from Style Norms,” ArXiv:1706.07068 [Cs], June 21, 2017. http://arxiv.org/abs/1706.07068. 7. Simon Penny, “Agents as Artworks and Agent Design as Artistic Practice,” in \ Human Cognition and Social Agent Technology edited by Kerstin Dautenhahn, Advances in Consciousness Research, 19 (2000): 395–414. https://benjamins.com/ catalog /aicr.19. 18pen. 8. Leonel Moura Pereira and Henrique Garcia, Man + Robots: Symbiotic Art (Villeurbanne: Institut d’art contemporain, 2004). 9. Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow, “Behavior, Purpose and Teleology,” Philosophy of Science 10, no. 1 (1943): 18–24. 10. Maria Fernández, Maria, “‘Life-like’: Historicizing Process and Responsiveness in Digital Art,” in The Art of Art History: A Critical Anthology, edited by Donald Preziosi, New ed., (Oxford History of Art. Oxford; New York: Oxford University Press, 2006), 477-487. 11. Simon Penny, “Embodied Cultural Agents: At the Intersection of Robotics, Cognitive Science and Interactive Art,” in AAAI Socially Intelligent Agents: Papers from the 1997 Fall Symposium, edited by Kerstin Dautenhahn (Menlo Park: AAAI Press, 1997), 103–105. 12. Sofian Audry, “Aesthetics of Adaptive Behaviors in Agent-Based Art,” Proceedings of the 22nd International Symposium on Electronic Art, 2–9. Hong Kong, 2016. http://iseaarchives.org/? page_id=36370. 13. Peter A. Cariani, “On the Design of Devices with Emergent Semantic Functions,” State University of New York at Binghamton, 1989. 14. Gordon Pask. An Approach to Cybernetics. (London: Hutchinson, 1968). 15. Emily Brontë. Wuthering Heights, 1996. http://www.gutenberg.org/ebooks/768. 16. Christopher D. Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing. 1st edition. (Cambridge, Mass: The MIT Press, 1999). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 35 How does a Machine Judge Photos? Comparing humans and algorithms Wasim Ahmad Syracuse University wahmad@syr.edu Abstract Machine-vision technology has progressed to the point where it can do much more than just identify what’s in a photo; it can tell what makes a photo good or bad. This study investigates how well the current technology used by a company recently acquired by computing giant Apple works by comparing software that uses this algorithmic approach to judge photos aesthetically to how professional photojournalists view the same photos. One-onone interviews revealed that while humans varied in their responses to a photo, they often provided more than just surface-level commentary, adding extra elements related to context and their experience. Their preconceived biases also coloured their aesthetic evaluation. Introduction Machine perception has come a long way. Computers have advanced from recognizing simple text, to voice, and now a new frontier: images. But the pace of image-recognition technology has not kept up with the easier media of text and voice. With so many pixels and so much information to digest, the technology required for a computer to fully understand the context and content of a photo is still a long way off. Still, the algorithms used today are becoming ubiquitous. Even services such as the lowly Flickr can now recognize basic items in photos, as can Google Photos. Apple’s iPhone has become very good at recognizing faces and organizing them into albums. Few have applied the technology to have it conjure up more meaning to an image than that. However, a French company, Regaind, has put 36 its algorithms to use to try to better understand photos that are being run through its software. The company’s software was good enough to catch the attention of Apple, which quietly purchased the company in September 2017. The service was shut down during negotiations. [1] In 2016, Regaind created a public demonstration of its software, aimed at photographers who wanted a critique of their photographs. The program was called Keegan, the photo coach (https://keegan.regaind.io/). The underlying premise of this website was to use the algorithms created by this company for use in its business dealings to identify objects and categorize photos for another purpose: to critique a photo so the photographer could improve upon it. When a photographer uploaded a photo to the site, Keegan provided both written feedback and a numerical dataset, which ranked the photo according to several different metrics. An example is shown in Appendix A. The Keegan website was retired on Feb. 10, 2017, but Regaind still offered the technology in a more business-oriented format, without the qualitative, human-sounding feedback, to paying customers. When the Keegan site was launched in 2016, it made waves in the photo industry. Whereas previously, photographers needed a knowledgeable human to obtain a critique of their photos in words, now a machine could provide the same service using algorithms. [2] The software could also output quantitative data about the photo, opening up a completely different avenue of study than that presented here. This topic is of particular interest because of the potential seismic shift for a particular genre of photography: photojournalism. Photo editors Proceedings of Art Machines: International Symposium on Computational Media Art 2019 How does a Machine Judge Photos?. Wasim Ahmad at a major event, such as the Olympics, can often receive upwards of 10 images per second from a working photographer, and going through them under ever-tightening deadlines is a difficult task. [3] If the technology existed to separate the good photos from the bad, the editors could work much faster. Of course, there is also a chance that the editors could be replaced. The acquisition of Regaind by Apple increases the salience of this study. It is the most recent, and possibly the only study that examines the efficacy of software that may power image recognition technology on every iPhone and iPad on the market. The software was shut down mid-study, when Apple entered into negotiations with the company’s founders, as hindsight revealed. It is not an exaggeration to say that not only this software, but image recognition technology in general will shape the future of image editing across multiple industries, so understanding the logic and process behind such software is crucial. Human editors need to make decisions about photos for public consumption, and users need to curate their own personal libraries, so this study attempts to understand how this artificial intelligence works by examining the responses to the latest software in the field. It is through gaining this understanding that the implications of this technology on the media industry will be realized. humans and machines in this area, though there are studies on human vs. human competitions: e.g., photos from professional photographers vs. those from citizen photojournalists. [6] There has even been a study of professionals vs. professionals, looking at which newspaper staff are more professional and whether this professionalism produced better photography. [7] This study throws a machine into the mix, Regaind’s Keegan, comparing its qualitative responses to photographs to insights from professional photojournalists obtained through in-depth interviews. The goal of the research is to determine how far along image recognition technology is, and to study whether in its present state, its perception can rival that of humans in journalistic fieldwork. The aim of this exercise is to see if software can achieve even a basic level of competency in identifying aesthetic qualities of a photo compared to photojournalists. With that in mind, the following research questions are examined in this study: Literature Review Previous research on this topic has mostly come from the realm of engineering. Some researchers have placed high consideration not only on how the aesthetic value of a photo affects machine perception, but also on how the technical aspects of a photo, such as compression and noise, affect a machine’s evaluation of a photo. [4] Other research has focused on what humans find memorable in photographs, and not surprisingly, photographs with human subjects tend to be more memorable than those without. Colour and “interestingness” were also factors affecting a photo’s memorability. [5] If a machine tracks the same way, it could have farreaching implications for the photo industry. In the communications realm, there has not been an analysis of the direct battle between This approach holds appeal for both the engineering world and the communications world, putting to practical use this imagerecognition technology and comparing it to human capability. Comparing human and machine results offers researchers an opportunity to further improve upon imagerecognition technology until there is parity, at least from an aesthetic perspective. This will move image recognition to the next frontier of deciding which photographs are important in context. This is a skill that for the foreseeable future will require the hand of skilled human editors no matter how good the machines get. RQ1: How close to a human response does a computer algorithm get when looking at the aesthetic qualities of a photograph? RQ2: What contributes to the difference between a computer’s interpretation of a photograph and a professional journalist’s? Method In this study, five photographs were run through Keegan, and its qualitative evaluations were Proceedings of Art Machines: International Symposium on Computational Media Art 2019 37 Part I. Full Papers (peer-reviewed) recorded. The photos were shot by the researcher or an associate and were not famous enough to have been published elsewhere. Although there are many famous photos that easily come to mind when considering photojournalism (many readers may have a ready image in mind, such as Nick Ut’s Vietnam War-era “Napalm Girl” photo or Richard Drew’s “Falling Man” photo from the 9/11 terror attacks on New York), there’s a risk that the participants in the study would bring their own preconceived notions of these photos to their interpretations of the aesthetic qualities. To avoid this, the photos used were taken by the researcher so that viewers would not have any history with them. This is similar to an approach used in a previous study to prevent prior memories of photographs from interfering with the study. [8] Ten current and former professional photojournalists and photo editors were chosen through purposive sampling for one-on-one, semi-structured in-depth interviews about the same five photos. They were asked first for their overall impression, and then asked to comment on items that Keegan frequently brought up, including composition and framing, background, exposure and lighting, colour, moment, blur, and a numerical rating. The participants all had a minimum of five years of experience, ranged in age from 27 to 64, and comprised five males and five females. For the in-person interviews, printed photos were used, and for phone interviews, e-mailed photos were used. Their interviews were recorded, transcribed and then inputted into NVivo for analysis. First cycle coding was done as magnitude coding. [9] The criteria outlined in the questions (composition, background, colour, exposure, moment and blur) were coded as positive or negative. Keegan was also included in this process. Coding the human responses revealed additional themes that were unexpected, and pattern coding was used to group these thoughts together to reveal more information. unexpected. However, what was unexpected was that in some cases, there were advantages to using the machine responses over human responses, the biggest being consistency. Results The human responses differed in several ways from those of the software. This was not Misunderstanding images Misunderstanding was a common theme. Even at the most basic level, the human participants 38 Evaluating context Context was one of the most dominant themes to come up. The participants consistently asked how a photo was going to be used. By contrast, a machine such as Keegan has no outward appearance of caring about context, but that doesn’t mean that context isn’t coded in. There’s just no way to tell if Keegan was programmed by landscape photographers or photojournalists. For instance, Jason, a photojournalist-turnedstudio photographer, had this to say about an extensively altered portrait photo of a young child dressed as Thor, a comic book superhero: “I like what they did in this photo. I’ve seen some of this work down in Texas; a couple of guys used Photoshop, and it had a really nice effect. It’s cute. It made me laugh. They definitely caught the moment.” Contrast that to how Leslie, a former photojournalist, acknowledges her bias about the same photo: “I will say [I rate this photo] a 5, because I just hate studio pictures … but that has nothing to do with it; it’s a great, fun photo of your child or someone’s child, so that’s good, and I think that, you know, my bias comes from being a professional photojournalist. If I were a portrait photographer, I might give it a 10.” Keegan’s programming seemed to be keyed in by portrait and studio photographers, because of all of the photos in the study, the child Thor was its favourite. He said this of it: “I’m interested, and I don’t want to look away; congratulations! Composed quite well. Very dynamic. Overall, pretty good shot! 8.7/10; you deserve it, champ. Everything is so perfectly framed that you get the framing ribbon! Now you’ve got the idea. Feel free to send me as many photos as you want. I’ll be glad to comment on them and give you my feedback. After 10 pictures, I will evaluate your level in terms of creativity and composition. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 How does a Machine Judge Photos?. Wasim Ahmad could figure out the intent of the photographer and recognize a distinctive feature, such as a silhouette, as a photographic choice rather than a mistake that required studio lighting to fix, as Keegan suggested. This tied in with experience. At times, Keegan failed to meet even the level of expertise of an entry-level photojournalist, although in some cases, neither provided the deep level of detail those with more experience provided. Keegan’s advice for studio lighting was centred on a photo at a fair of the “Zipper” ride, where studio lighting wasn’t needed or practical, and the silhouette was intentional. None of the photographers in the study made the same call as Keegan. Contradictions and bias The humans participants would sometimes contradict themselves about how they felt about an aspect of a photograph or they indicated bias knowing that a photo was shot with a cell phone. Professional photojournalists often frown upon cell phone photos. This was one area in which Keegan’s objectivity was an advantage. Keegan did not seem to differentiate or care what device was used to shoot a photo, and its results were consistent, as opposed to the human participants, who often contradicted themselves in the same sentence. For instance, Jason, the photojournalist-turned-studio photographer, had this to say about the composition of the Dominican Day Parade photo: “I like its composition; I think it’s a little loose.” These two statements don’t make sense in the same sentence without a contrast word. Keegan offered no such ambiguity. The prejudice of professional photographers is a widely known industry issue. Photographers often frown upon using anything other than professional cameras and instantly dismiss what they consider snapshots with point-and-shoot cameras or phones. This was also true of the photojournalists in this study. Jessie, a photojournalist, had this to say about the photo of kids in a bounce-house: “This is definitely, like, a snapshot of ‘Hey look, there’s my son’ or ‘I gotta get a photo of this kid’ type of photo.” That attitude coloured the rest of her critique of the photo. When asked to rate the photo, she wanted to go lower than the scale allowed and give it a 0. By contrast, since Keegan was programmed by a company, it tended to be more tactful. For example, it had this to say about the same relatively poor photograph: “Nice timing, but a bit blurry. This pick is just … ok. Don’t forget about the blur and background. A solid 5.7/10. Not bad, but I’m sure you can do better!” The human bias was related to experience. In many cases, the photojournalists in this study were blunt with their critiques because they were battle-hardened by field experience. The more experience a participant had, the more detailed their critique, with photographers who also had photo-editing experience providing the most detailed responses. Keegan, by contrast, offered mostly surface-level and similar critiques, likely owing to its limited database of pre-programmed responses to photos. Conclusions The machines aren’t there yet. But it’s not easy to say why. Some research points to technical issues with photos. Resolution and compression, for instance, could put software at a disadvantage, but the same could be said for humans. [10] Print quality or monitor quality was brought up in some cases. One limitation of the study was the photos themselves. Regaind shut down Keegan earlier than promised, so there was no opportunity to run more journalistic photos through it. The reason for this mysterious cut-off in communication became clear when Apple’s acquisition of the company was reported in the media. [11] The photos chosen were a more general set used for exploratory purposes, but they ended up being the main photos used for the study. Regardless, the photos provided some insight into how the program perceives images. Since this study began, new software and products have come out that utilize machine vision. Amazon, for instance, released a device that takes a photo of users and offers fashion advice. While the technology has significant implications for journalism, there’s a wide range of consumer-based applications to be explored, an area ripe for future study. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 39 Part I. Full Papers (peer-reviewed) References 1. Romain Dillet, “Apple quietly acquired computer vision startup Regaind,” TechCrunch, September 29, 2017. https://techcrunch.com /2017/09/29/apple-quietly- acquires-computervision-startup-regaind/. 2. Michael Zhang, “Keegan is an Online A.I. Photo Coach Who Critiques Your Photos,” Petapixel, October 8, 2016. https://petapixel. com/2016/10/08/keegan-online-photo- coachcritiques-photos/. 3. Jack Crager, “How Getty's Olympics Photos are Shot, Edited, and Sent into the World in Just Two Minutes,” Popular Photography, August 3, 2016. http://www.popphoto.com /how-olympicimages-reach-your-eyes-in-two-minutes-flat. 4. Xiaoou Tang, Wei Luo, Xiogang Wang, “Content-based Photo Quality Assessment,” IEEE Transactions on Multimedia, 15, no. 8 (2013):1930-1943 5. Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva, “What Makes a Photograph Memorable?,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, no. 7 (2014): 1469-1482. 6. Tara Buehner Mortensen and Ana Keshelashvili, “If Everyone with a Camera Can Do This, Then What? Professional Photojournalists' Sense of Professional Threat in the Face of Citizen Photojournalism,” Visual Communication Quarterly, 20, no.3 (2013): 144-158. 7. Thomas Coldwell, “Professionalization and performance among newspaper photographers,” International Communication Gazette, 20, no. 2 (1974) :73-81. 8. Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, and Aude Oliva; What Makes a Photograph Memorable? 9. Johnny Saldaña, The coding manual for qualitative researchers, 3rd Edition (Los Angeles: SAGE 2016). 10. Xiaoou Tang, Wei Luo, Xiogang Wang, “Content-based Photo Quality Assessment.” 11. Romain Dillet, “Apple quietly acquired computer vision startup Regained.” TechCrunch, September 29th 2017. https:// techcrunch.com/2017/09/29/apple-quietlyacquires-computer-vision-startup-regaind/. 40 Appendix A Following is a sample of the output obtained from running a photo through Keegan the photo coach. As you can see, it offers a few sentences of critique for each photo inputted by the user, followed by a detailed analysis of several attributes of the photo. Appendix B These are the five photos used in the study, in the same order presented to the participants. The three phone interview participants viewed these on their computer screens at the highest resolution available for each photo, depending on the camera used. The seven in-person interview participants viewed them as 8.5 x 11” print-outs on Canon Lustre photo paper, printed on a pigment ink-based printer, the Canon Pro10. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 How does a Machine Judge Photos?. Wasim Ahmad Proceedings of Art Machines: International Symposium on Computational Media Art 2019 41 Ornament and Transformation - the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art Harald Kraemer School of Creative Media, City University of Hong Kong H.Kraemer@cityu.edu.hk Abstract In the late 1960s, there was a revival of ornamental visual language under the term 'Neue Ornamentik'. Inspired by the Chinese game Tangram, the idea of "geometrical ornaments" by Wassily Kandinsky, the writings of Max Bense, and Josef Frank's design of wallpaper and fabrics in the late 1960’s, Austrian artist Robert Lettner (1943–2012) developed an interest in ornament and ornamental structure. Since he didn’t understand ornament as a symmetric repetition of motifs, but more as a strategy to visualize complex structures, he first developed an analogue, and later, together with Walter Worlitschek and Philipp Stadler, a digital visual language, which resulted in a series of more than 250 digital paintings from 1995 to 2012. Based on the structural-systemic approach of ornament, there are three principles in the digital paintings of Robert Lettner: (1) the principle of the serial sequence, (2) the modular principle, which supports the idea of replacing single elements within a closed system, and (3) the principle of the algorithmic image composition, in which the computer defines the visual outcome. This text provides an introduction to Robert Lettner and a comprehensive overview of the results of a research project to build an archive and database. The project was started in 2013 and completed in 2018. We honour his work as a lesser-known pioneer of computational art, or rather algorithmic art. It is my hope that this essay will establish a basis for future research that will relate Lettner’s digital paintings to the works of pioneers of computer art. 42 From Analogue Drawings to … In the mid-1960s, about half a century after the end of historicism, engagement with the ornament experienced a revival, known as 'Neue Ornamentik' ('New Ornamentation'). Klaus Hoffmann claimed that this revival "is pointing out the denunciation of the ornament and discovers a new consciousness of ornamentality." [1] Vienna-based artist Robert Lettner indulged himself in the passion for ornament during those years. He received the British Council Scholarship for his studies in 1972-73 and moved to London. During his scholarshipfunded studies, he deepened his interest in ornament by visiting the Victoria & Albert Museum to study its rich collection of works by William Morris and the Arts & Crafts Movement, and created drawings that show his engagement with the repetition structure of iron fences, which he drew in an abstract form as an array of lines. [2] The drawings he made near the subway station of Royal Oak in London in 1973 were attempts to combine the experience with the spatial environment, together with the extensive character of ornament. [3] Two other ink drawings from 1972 show strongly rasterized and extremely fragmented structures (see Fig. 1 and 2). [4] Upon closer observation, it becomes clear that Lettner created his drawing in three stages. After doing a rough pencil layout he redrew the lines in ink with a Rotring pen, and then he filled in the gaps, creating a dense structure. He used this timeconsuming procedure to create drawings of chestnut blossoms in 1975 and more than 70 ink drawings of plants from 2008 to 2012. [5] From research made in the framework of the exhibition In Dialogue with the Chinese Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer Landscape in 2017 in Hong Kong, we conclude that Lettner combined two drawing techniques from the classic Manual of the Mustard Seed Garden (芥子園畫傳) and adapted them to his ink drawings. [6][7] Following this manual of Chinese painting from the early Qing Dynasty, Lettner first made a pencil sketch and used the double-line method to outline the shape in ink. Fig. 1. Raster (grid), 1972, Robert Lettner, pencil, ink on paper, Robert Lettner Archive, Vienna. of the magical geometry, which he created between 1995 and 1998. When Lettner introduced his sketches to mathematician Herbert Fleischner, he learned that his analogue drawings dealt with a mathematical problem that engaged both mathematicians and pioneers in computer graphic development. The problem refers to the idea of Max Bense, which he elaborated in his four essays Aesthetica. Bense claimed that it is possible to calculate the aesthetic value of information through a mathematical formula. [9] Since the algorithmic-based software in the late 1960s was far from offering visually oriented solutions for complex problems, computer graphics served a merely decorative purpose, classified as OpArt, as many results show. The “artists” of those early years were often computer scientists, mathematicians or engineers who were interested in the aesthetic issues of algorithmic design, such as Frieder Nake, Georg Nees, Herbert W. Franke, and A. Michael Noll. [10] In contrast to many professional artists who rejected the output of these creative dilettantes as 'doodles', Lettner was fascinated by the huge potential of computational power and pursued the question of how to create art with information technology devices in the following decades. Ornament became a strategy for visualizing complex structures within analogue and digital systems. ... to Digital Painting Fig. 2. Raster (grid), 1972, Robert Lettner, pencil, ink on paper, Robert Lettner Archive, Vienna. Figure 1 anticipates his motif of the knot, while Figure 2 anticipates the series of digital Images Lettner's approach to ornament is complex and can be divided into three main areas: 1. The principle of serial sequence. This principle can be found in his works in the series Das Spiel vom Kommen und Gehen (The game of come and go, 1976–1990), Die reproduzierte Reproduktion (The reproduced reproduction, 1989–1992), and Landschaft Bilder Therapie (Landscape Paintings Therapy, 1982–1990). 2. The modular principle, which supports the idea of replacing single elements within a closed system, which Lettner applied in Landschaft Bilder Therapie (Landscape Paintings Therapy, 1982–1990), and most importantly, DiskettenBilder (1986–1989), Figurationen Proceedings of Art Machines: International Symposium on Computational Media Art 2019 43 Part I. Full Papers (peer-reviewed) (Configurations, 1991), Eindeu-tigkeiten (Unambiguities), Mutationen (1992), Dubliner Thesen zur Informellen Geometrie (Dublin thesis on informal geometry, 1992–1994), and Mein Herbarium (My Herbarium, 1990–1994). 3. The principle of the algorithmic image composition can be shown in his digital paintings (1995–2012), e.g. in the series Bilder zur magischen Geometrie (Images of magical geometry) in the series of Über die Dialektik des Fadenscheinigen im Ornament (About the dialectic of the flimsiness in the ornament) and in a variety of his Spiegelungen (Reflections) works. Since Lettner varied his motifs and compositional elements, the borders of those three principles are fluid and their content is strongly connected. 1. The Principle of Serial Sequence When Lettner started the series Das Spiel vom Kommen und Gehen (The game of come and go), he asked himself what to do with the leftover tapes of the airbrush production. In 1970, he came up with the idea of sticking acrylic paintpolluted tape into a spiral bound photo book. This idea resulted in a series of artworks: Klebestreifen (Tapes) in 1976 and Zeilen (Lines) in 1978 (Fig. 3). The artist created a small passepartout, which enabled him to select and focus on certain sections of the images. Some of the image sections inspired him and led him to a new series of artworks. While he focussed on simple and low-saturated visual language in his paintings in 1976, his 1982 paintings, which were up to 200x200 cm were much closer to the idea of his early tape works. Some of his paintings were given new titles for the exhibition Philosophie der Landschaft (Philosophy of Landscape) in 2011, so they are now named Eine frühe Aufzeichnung des Messbaren (An early record of the unmeasurable) or Ungenau aber schön (Unprecise but beautiful). He also created simple line-based ink drawings in 1982, which refer to the principle of the serial sequence and can be seen as 'audiovisual' drawings in terms of Farbpartituren (Colour scores) (Fig. 4). For the exhibition Elements. Austrian Paintings since 1980, which was held in Dublin in 1996, Lettner used his original material and 44 Fig. 3. Das Spiel vom Kommen und Gehen (Klebebilder) (The game of come and go, tape images), 1978–2010, Robert Lettner, acrylic on tape, Robert Lettner Archive, Vienna. created an artist’s book, titled Das Spiel vom Kommen und Gehen (The game of come and go). The members of the Viennese Low Frequency Orchestra assigned the tape to a score and create a performance, which has been displayed several times as a video installation and as a concert since 2006. According to musicologist Stephan Sperlich, the series Das Spiel vom Kommen und Gehen "renders readability (and in its consequence visibility and audibility as well) of an implicit structure [...]. A readability that can only happen in the process of creation." [11] In 1990, the series of Das Spiel vom Kommen und Gehen (The game of come and go) appeared again. This time Lettner created them in portrait format with higher contrast and played with elements of the Disketten-Bilder series. This is visible in his Figurationen series (1991–1992), which show the compositional concept after sequencing (Fig. 5). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer reproduction becomes an original. For this purpose, he used motifs from daily events, which can create different interpretations through multiple reproductions based on the intention of the content. One example for this series of works is N.Y. Times Square 1987 February 22, 5 p.m. (1989), which is a tribute to Andy Warhol. Lettner took a photograph of the news ticker at Times Square about Warhol's death on 22 February 1987, as he was in New York when Andy Warhol died. The unusual design for the exhibition Landschaft Bilder Therapie (Landscape Paintings Therapy), which was organized by Lettner in the Krems Minority Church in Krems in 1988, also relates to the principle of serial sequences. The exhibition showed 84 artworks from 1982 to 1988, separated into six groups of 14 paintings of the same format. The sequential arrangement of the artworks shows the strength of the variety of motifs. From a distance, the arrangement of the paintings recreates ornamental banding. Fig. 4. Das Spiel vom Kommen und Gehen (Tuschzeichnungen) (The play of come and go, ink drawings), 1982, Robert Lettner, ink on paper, Robert Lettner Archive, Vienna. Fig. 5. Figurationen (Configurations), 1991–1992, Robert Lettner, acrylic on canvas, Robert Lettner Archive, Vienna. Between 1989 and 1992 Lettner dealt with the problem of the “reproduced reproduction” and was interested in the question of when a 2. The Modular Principle The artist’s library contains a book about Tangram, the traditional Chinese puzzle with seven shapes. [12] Lettner pointed out the importance of this game "since it creates a constellation of seemingly incompatible elements of the same system, all in a playful way." [13] The hidden principle of modularity which supports the exchangeability of single elements within a system, can be applied to some series of Lettner's work, like the Figurationen (Configurations) (Fig. 5). This series contains forms that seem to spring directly from the Tangram game and at the same time demonstrate the infinite potential of juxtaposing forms. The previously mentioned series Landschaft Bilder Therapie (1982–1990), which is an example of the principle of serial sequence, can be understood as the modular principle as well. This modularity is even more evident at the interface of the series Disketten, which includes Disketten (1986–89), Kosmopolitisch (1989), and T1 to T4 (1992). [14] Inspired by the Tangram puzzle, Lettner used simple single Proceedings of Art Machines: International Symposium on Computational Media Art 2019 45 Part I. Full Papers (peer-reviewed) shapes for the wall design of a hospital in Mödling in Lower Austria (1993–1995). Fig. 6. Drei Eindeutigkeiten des Mathematikers Herbert Fleischner (Figure 6) / Drei Mutationen des Malers Robert Lettner (Figure 5), 1992, Robert Lettner, silkscreen on canvas, Robert Lettner Archive, Vienna. In Drei Eindeutigkeiten des Mathematikers Herbert Fleischner und Drei Mutationen des Malers Robert Lettner (Three uniquenesses of the mathematician Herbert Fleischner and Three mutations of the painter Robert Lettner) Lettner was also influenced by the philosophy behind Tangram (Fig. 6). But this series of three pairs of silkscreens was the result of collaboration between Lettner and mathematician Herbert Fleischner in 1992. The starting point of their collaboration was three graphs of the mathematician that had similar features and could be combined through transformation. Herbert Fleischner explained that mathematicians "think in abstract cases to recognize connections between the features of any object (e.g. the mentioned graphs)" and used this to visualise his thoughts. Hence, the mathematician created a new reality and clarity. Lettner was inspired by this and abstracted the clarity by comparing the mind-set of the mathematician with the mind-set of the artist. The term "Mutations" is of central significance, since "every single graph can be transformed into the other two graphs with their specific features." [15] 3. The Principle of the Algorithmic Image Composition Lettner described the technical structure of his digital paintings as follows: "A hand-drawn sketch has to be digitally printed in two colours on large plastic foils and attached to an aluminum bar." [16] Even though it sounds like a simplification of the artistic process, it is the result of a process the artist called "the merger of organic and inorganic aesthetics. The organic aesthetic is based on an automated hand drawing that is digitally edited.” The “digital editing process", which Lettner calls an "inorganic process", "creates the final result, which is not new, but represents something new in the way it was produced. This understanding of aesthetics is the result of the merger of two processes; historically, we are on that point of merging the 46 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer manual and technical worlds. That is the actual Fig. 7. Die magische Geometrie (Klebebild) (The magical geometry, tape image), 1981, Robert Lettner, tape with acrylic on photocopy, Robert Lettner Archive, Vienna. transition process." [17] The digital paintings have a playful approach to the laws of algorithmic ornaments. The works in the series Bilder zur magischen Geometrie (Paintings of magical geometry, 1995–1998), which were exhibited in Wiener Secession in Winter 1998/1999, were inspired by the series Die magische Geometrie (The magical geometry), created in 1981 (Fig. 7). Fig. 8. Bilder zur magischen Geometrie, Serie I/13; Serie I/2 (Paintings of magical geometry), 1996, Robert Lettner, Plotterprint, Robert Lettner Archive, Vienna. This work is based on a complex hand-drawn grid, which contains repetitive forms and a horizontal and vertical sequence, following the ABAB-scheme. Lettner copied his own drawing in 1981 (Fig. 7), decorated photocopies individually with tape and called his work Die magische Geometrie. Since the mid 1990s, printing technology has improved, enabling Lettner to print large-scale prints on acrylic plastic. In the Viennese Secession exhibition, several variations of Paintings of magical geometry were shown (Fig. 8). The ornamental structures he produced in an algorithmic process of data processing contain clear symmetrical features of the classic understanding of ornament, as well as the infinite multiplication of the Celtic understanding of ornament to create dynamic structures. [18] The following series, Über die Dialektik des Fadenscheinigen im Ornament (About the dialectic of the flimsiness in the ornament), produced in Lettner’s collaboration with Walter Worlitschek in 2000, generates special interest, since the motif of the knot appears in R. Lettner’s analogue paintings as well (Fig. 9). [19]. His paintings in the Knotenbilder (Knot Paintings) series show floating knots in a fictional landscape, which creates a connection between different creation techniques. The knots of his digital paintings are more highly saturated, but they relate more to the first generation of images of the Bilder zur magischen Geometrie series. Lettner’s knots can be associated with Arabian inspired ornaments as well as arabesque. The arabesque motif was described in 1893 by Austrian art historian Alois Riegl in his book Stilfragen. Grundlegungen zu einer Geschichte der Ornamentik. It is a prototype of ornamental design. [20] With this tendril from which buds and flowers sprout in infinite succession, like a Mandelbrot set, R. Lettner succeeds in interrupting and reinforcing the symmetrically arranged grid by means of another variable element. Arabesques are "the result of a highly complicated mathematical formula, which, as Proceedings of Art Machines: International Symposium on Computational Media Art 2019 47 Part I. Full Papers (peer-reviewed) Muslims feel, indicates the wonderful structure of the world." [21] In later conversations, Lettner said that during this time he studied the repetitive methods of Arabic and Celtic ornament. He understood his digital painting as an artistic reaction to the writings of Alois Riegl, Wilhelm Worringer and Max Bense, but also to the notion of Benoît B. Mandelbrot's fractal geometry. [22] In 2003, R. Lettner started a new collaboration with Philipp Stadler and created a new series of works. The Das unsichtbare Archiv des Arcimboldo (The invisible archive of Arcimboldo) series was inspired by oriental carpets. Illustrations of old Viennese cookbooks were cut out and scanned. This series, along with Bilder zur magischen Geometrie, is an example of the "mathematization of the arts", as Max Bense said, citing the "repetition of one single element after the laws of symmetry." [23] Mathematicians Herbert Fleischner and Fig. 10. Das unsichtbare Archiv des Arcimboldo (The invisible archive of Arcimboldo), 2003, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. Fig. 9. Über die Dialektik des Fadenscheinigen im Ornament (About the dialectic of the flimsiness in ornament), 2000, Robert Lettner and Walter Worlitschek, inkjet on canvas, Robert Lettner Archive, Vienna. 48 Christoph Überhuber, and philosophers Burghart Schmidt and Mara Reissberger, a specialist in the history of ornament, developed a huge interest in this new series of works, since the Information Technology industry uses the idea of “structural design patterns” as well. In the same year, 2003, R. Lettner created the series Mein Uterus verlangt nach deinem Zungenkuss (My uterus requires your tongue kiss) (Fig. 11). At first glance, it seems to show surfaces as they would develop from the process of marbling paper and invite random associations à la Rorschach. But through rotations, multiplications and mirroring, the program creates tensions between the elements, while the borders vanish. This work gains its tension from the co-existence between symmetrical order and asymmetrical chaos, which fight for attention. An der Schnittstelle zur Unendlichkeit (At the interface to infinity, 2009) is an extended version of this strategy (Fig. 12). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer Fig. 11. Mein Uterus verlangt nach deinem Zungenkuss, Reflection A4 V1 (My uterus requires your tongue kiss), 2003, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. Fig. 12. An der Schnittstelle zur Unendlichkeit (Reflection A67 V4) (At the interface to infinity), 2009, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. Though the works already had titles, some of them were renamed for the exhibition: Philosophie der Landschaft (Philosophy of Landscape), Natur ist keine Katastrophe (Nature is not a catastrophe), Der Wassergarten im Hause Neptun (The water garden in the house Neptune), Kalvarienberg – von allen Seiten kamen sie (Calvary – they came form all sides), and Bikiniatoll oder Ein Kreuzklangsonett (A cross sound sonnet). Lettner said the titles of his works float and can change over time, just as the audience will change over time. Also, he considered the title of a work a rebus to hide something about the work rather than giving an actual explanation. [24] Spiegelungen (Reflections, 2004–2012) and Synchronwelten (synchronous worlds, 20102012) are small-scale studies that R. Lettner created in large number. The scanned versions are the foundation for digital paintings. As described in the exhibition catalogue for the exhibition in Hong Kong, Lettner and Philipp Stadler had different approaches to finding a visual language. The work A27 (2005) and the three versions of it V1, V2 und V3 from the series Spiegelungen (Reflections) utilize the technique of zooming and therefore focussing on details. The microcosm and macrocosm are at the same level of importance since they complement each other. They appear as opposite coloured pairs, as in A25 and A26, or unite and complement each other, as in A61 to A 63 and A95. The pointillist work Solaris 1 (Reflection A17 V1) (Fig. 13), A28 and A30, and A38 to A40, look like colour plays, which visualize the simultaneous and successive contrasts of the colour theories of Maurice Chevreul. The inspiration for the motif were patchwork patterns and the ornamental visual language of the Orient. An interesting exception in the Spiegelungen (Reflections) series are the works A45 to A47, which are also named 33 liegt zwischen den Zahlen (33 lies between the numbers) (Fig. 14). In these three panels, Lettner skilfully combines the principle of serial sequences with the modular principle. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 49 Part I. Full Papers (peer-reviewed) Fig. 15. Seegras (Seagrass), ca. 1930, designed by Josef Frank for Haus & Garten, Austria, furnishing fabric of hand blockprinted and glazed cotton, Inv. No. CIRC.830-1967, Victoria & Albert Museum, London. Fig. 13. Solaris 1 (Reflection A17 V1), 2005, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. Fig. 14. 33 liegt zwischen den Zahlen (Reflections A45, A46, A47) (33 lies between the numbers), 2007, three parts, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. If we examine Spiegelungen (Reflections) in relationship with the works of other artists, we see a connection with the fabric and wallpaper patterns of Viennese architect and designer Josef Frank (1885–1967), [25] who said about ornamental design on digital paintings: "A pattern of organic lines has always the desire to dissolve the geometrical form which it is connected with." In the fabric pattern seagrass (seaweed) (Fig. 15), a block print from the 1930s, the floral elements of the ornaments are woodcuts, which are printed on fabric and vary by rotation. This work, created with stamps or paint rollers, has simple motifs and complex ornaments created by multiple rotations. This was a common design on room walls, in corridors, and in 50 fabric samples in Vienna around 1900. Lettner and Philipp Stadler used a similar approach in their work Kalvarienberg – von allen Seiten kamen sie (Calvary – they came form all sides, 2010) and A38 to A40 (Fig. 16). But they used algorithms that created reflections and twists. Since a comparison with the works of Josef Frank would not be comprehensive enough, I compare the digital paintings and landscape paintings of Lettner with those of William Morris and the Arts & Crafts Movement, and examine the influence of Josef Hoffmann, Koloman Moser and the ornamentality of Wiener Werkstätten. [26] Echoes of Worringer, Kandinsky, and Bense It is perhaps surprising that simple drawings and simple arranged shapes can create complex ornamental patterns and reveal artistic qualities. Lettner pointed out that every shape seems to be familiar, but they actually don’t exist in their form itself: "It only looks like one. More accurately, it is the fragment of a structure which is identifiable as such throughout our civilization and throughout nature and the world, but ultimately breaks free if it is stretched, and passes from being a microcosm to a macrocosm. Ultimately it becomes infinitely large, and I experience these intervening spaces. The structure, the ornament, is no longer identifiable. But if I move away, the ornament Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer Fig. 16. Kalvarienberg – von allen Seiten kamen sie (Reflection A10) (Calvary – they came from all sides), 2004, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienn ǤǡǤ̶ሾʹ͹ሿ Ǥ ǡ ǡ Ǥ For art historian Wilhelm Worringer, who created with his dissertation Abstraktion und Einfühlung (Abstraction and Empathy) in 1907 one of the theoretical foundations for the understanding of modern abstract art, these "abstract legal forms" of ornament are "the only ones and the ones the highest" and therefore "it was natural to see in mathematics the highest art form." [28] Max Bense, who quoted in his chapter "Die Mathematik in der Ornamentik" whole passages from Worringer, declared that it is "irrelevant at first whether the geometric ornament already existed as such or if it developed from a plant ornament." [29] For him, the mathematization of art has “a morphological purpose; it’s not just creating certain figures from the material prescribed for the artistic act that is subject to mathematics; the composition of artistic details and artistic elements also fall prey to mathematization." As an example, Bense calls the "repetition of an element according to the laws of symmetry one of the most general and the oldest processes of mathematization in fine art." [30] It wasn’t just Max Bense who took Worringer's Abstraction and Empathy as inspiration; artists like Wassily Kandinsky and Franz Marc saw in this important work the theoretical basis for their artistic involvement in abstraction. In order to grasp the gaps, Lettner approached his works with a vision of a "geometrical ornament" as it was envisioned by Wassily Kandinsky in 1911: "If we start to destroy our connection to nature, enforce liberation by all means, and remain satisfied with a combination of pure colour and independent shapes, we will create art that looks like geometrical ornaments that will look like a tie or a carpet." [31] It is surprising, that the works of digital painting with its visual language come so close to Kandinsky’s vision of a carpet. But Lettner was more engaged in lines and forms that can be ordered as structures, embody ornament, and lead to an ornamental consciousness. [32] Lettner continued developing the question of ornament with help of digital computing technology for scientific research purposes and included the art discussion, since just as the evolution of language affects society, the evolution of ornament affects the artistic system. [33] The calculability of the algorithm leads to unpredictable virtual space of experience since "it cannot be found more magical than in the order." [34] Acknowledgments This essay is dedicated to Herbert Fleischner in honor of his 75th birthday. I would like to thank Margit Lettner, Markus Lettner, and Philipp Stadler, and especially Park Ji Yun Jade, Alexandra Woermann and Tobias Klein for their help. References 1. Klaus Hoffmann, Neue Ornamentik. Die ornamentale Kunst im 20. Jahrhundert (Cologne: DuMont, 1970). 2. Jorge Enciso, Design Motifs of Ancient Proceedings of Art Machines: International Symposium on Computational Media Art 2019 51 Part I. Full Papers (peer-reviewed) Mexico (New York: Dover Publications, 1953); Claude Humbert, Ornamental Design (Fribourg: Office du Livre, 1970); Jules Bourgoin, Arabic Geometrical Pattern & Design (New York: Dover Publications, 1973); Carol Belanger Grafton; Traditional Patchwork Patterns (New York: Dover Publications, 1974). 3. Illustrations in: Harald Kraemer, Robert Lettner. Das Spiel vom Kommen und Gehen. Widerstand – Utopie – Landschaft – Ornament (Klagenfurt: Ritter Verlag, 2018), 131. 4. Harald Kraemer, Robert Lettner, 133. 5. Harald Kraemer, Robert Lettner, 130. 6. Robert Lettner. In Dialogue with the Chinese Landscape – Utopia of Ornaments – New Wunderkammer of Rococo, ed. Florian Knothe and Harald Kraemer, exhibition catalogue (Hong Kong: University Museum and Art Gallery, The University of Hong Kong, 2017), 10–11; illlustrations 29–35. 7. Der Senfkorngarten. Lehrbuch der chinesischen Malerei, 2 Vols., ed. Hans Daucher (Ravensburg: Otto Maier, 1987, Vol. 2), 21; illustrations see 60–61. 8. Max Bense, Aesthetica (I). Metaphysische Beobachtungen am Schönen (Stuttgart: Deutsche Verlags-Anstalt, 1954); Aesthetica (II). Aesthetische Information (Agis, BadenBaden: Agis, 1956); Aesthetica (III). Ästhetik und Zivilisation. Theorie der ästhetischen Zivilisation (Krefeld/Baden-Baden: Agis, 1958); Aesthetica (IV). Programmierung des Schönen. Allgemeine Texttheorie und Textästhetik (Krefeld/Baden-Baden: Agis, 1960) 9. Grant Taylor: "Routing Mondrian: The A. Michael Noll Experiment," in Journal of the New Media Caucus 8, no. 2 (2012), accessed August 28, 2018, http://median.newmedia caucus .org/routing-mondrian-the-a-michaelnoll-experiment/ 10. Cybernetic Serendipity. The Computer and the Arts, ed. Jasia Reichhardt, exhibition cataloge (Institute of Contemporary Art, London 1968, Studio International Special Issue, Praeger, 1970); Günter Pfeiffer, Kunst und Kommunikation. Grundlegung einer kybernetischen Ästhetik (Cologne: DuMont, 1972); Herbert W. Franke and Gottfried Jäger, 52 Apparative Kunst. Vom Kaleidoskop zum Computer (Cologne: DuMont, 1973); Frühe Computergraphik bis 1979. Die Sammlungen Franke und weitere Stiftungen in der Kunsthalle Bremen, ed. Wulf Herzogenrath and Barbara Nierhoff-Wielk, (Munich: Deutscher Kunstverlag, 2007). 11. Stephan Sperlich, "Das Spiel vom Kommen und Gehen," in Low Frequency Orchestra plays Robert Lettner: Das Spiel vom Kommen und Gehen, Wien, 2006. Reprinted in Harald Kraemer, Robert Lettner, 262–263. 12. Joost Elffers, Tangram. Das alte chinesische Formenspiel (Cologne: DuMont, 1978). 13. Robert Lettner in conversation with the author on 27.06.2012. 14. Konrad Paul Liessmann, "Zu Robert Lettners Diskettenbilder," in Robert Lettner. Dubliner Thesen zur Informellen Geometrie, Exhibition catalogue (Galerie Heiligenkreuzerhof, Wien, 1994). Reprinted in Harald Kraemer, Robert Lettner, 241. 15. Herbert Fleischner & Robert Lettner, "Mathematik in der Kunst Oder Kunst in der Mathematik?" Reprinted in Harald Kraemer, Robert Lettner, 231. 16. Robert Lettner: Letter to Purchase Commission of Cultural Department, Lower Austria Provincial Government on 07.03.1997. 17. Robert Lettner and Harald Kraemer, "Art is Reedeemed, Mystery is Gone. Conversations with Robert Lettner and Harald Kraemer," in Robert Lettner, Die Kunst ist erlöst, das Rätsel ist zu Ende. Bilder zur magischen Geometrie, ed. Wiener Secession, exhibition catalogue (Vienna: Wiener Secession, 1998), 15–23. Reprinted in: Florian Knothe and Harald Kraemer, Robert Lettner, 39–45. 18. Harald Kraemer, "Ornamentik zwischen Opulenz und Virtualität: Worringers Vermächtnis?" in Hundert Jahre 'Abstraktion und Einfühlung.' Konstellationen um Wilhelm Worringer, ed. Norberto Gramaccini and Johannes Rössler (Munich: Wilhelm Fink, 2012), 259–276, 271. 19. Harald Kraemer, Robert Lettner, Mara Reissberger and Burghart Schmidt, Im Bild über Bilder sprechen. Über die Dialektik des Fadenscheinigen im Ornament (Vienna: Verlag Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer der Universität für angewandte Kunst Wien, 2006). 20. Chapter IV "Die Arabeske," in Alois Riegl Stilfragen. Grundlegung zu einer Geschichte der Ornamentik (Berlin: Verlag von Georg Siemens, 1893), 259–346. 21. Annemarie Schimmel, "Die Arabeske und das islamische Weltgefühl" in Ornament und Abstraktion – Kunst der Kulturen, Moderne und Gegenwart im Dialog, ed. Markus Brüderlin, Fondation Beyeler Riehen/Basel, exhibition catalogue (Köln: DuMont, 2001), 31–35, see 31. 22. Robert Lettner in conversation with the author on 28.06.2012. 23. Max Bense, "Die Mathematik in der Ornamentik," in Konturen einer Geistesgeschichte der Mathematik II. Die Mathematik in der Kunst (Hamburg, 1949), 57–77, see 57. 24. Robert Lettner in conversation with the author on 28.06.2012. 25. Josef Frank 1885 – 1967, exhibition catalogue, (Vienna: Hochschule für angewandte Kunst, 1981); Josef Frank. Stoffe Tapeten Teppiche, exhibition catalogue (Vienna: Hochschule für angewandte Kunst, 1986), 62, see also 28, illustrations 25–28. 26. Linda Parry, William Morris. Textiles, ed. Victoria & Albert Museum, London (V&A Publishing, 1983, Reprint 2013); Linda Parry, Textiles from the Arts and Crafts Movement (London: Thames and Hudson, 2005); Josef Hoffmann. Ornament zwischen Verbrechen und Hoffnung, exhibition catalogue (Vienna: Museum für angewandte Kunst, Wien, 1987); Angela Völker, Die Stoffe der Wiener Werkstätte 1910–1932, ed. MAK Wien (Vienna: Brandstätter Verlag, 1990/2004). 27. Robert Lettner, Die Kunst ist erlöst, 16. Reprinted in Florian Knothe and Harald Kraemer, Robert Lettner, 40. 28. Wilhelm Worringer, Abstraktion und Einfühlung, Ein Beitrag zur Stilpsychologie (Neuwied: Heuer'sche Verlags-Druckerei, 1907), at the same time Dissertation, Faculty of Philology, University Bern, 12.1.1907. Reprint: Munich: Fink, 2007, Vol. 1, 39–139, see 76. On the input of Worringer: Hundert Jahre 'Abstraktion und Einfühlung.' Konstellationen um Wilhelm Worringer, ed. Norberto Gramaccini and Johannes Rössler (Munich: Wilhelm Fink, 2012). 29. Max Bense: Konturen einer Geistesgeschichte der Mathematik II. Die Mathematik in der Kunst, (Hamburg: Claassen & Goverts, 1949). See chapter "Die Mathematik in der Ornamentik", 57–77; about Worringer 5961. 30. Max Bense, Konturen, 59, 57. 31. Wassily Kandinsky Über das Geistige in der Kunst (München, 1912), (10. edition, Bern: Benteli, 1973), 115. See also Harald Kraemer, "Ornamentik zwischen Opulenz und Virtualität: Worringers Vermächtnis?" in Norberto Gramaccini and Johannes Rössler, Hundert Jahre 'Abstraktion und Einfühlung', 273. 32. Robert Lettner. Vienna Secession, 16. Reprinted in Florian Knothe and Harald Kraemer, Robert Lettner, 40. 33. Niklas Luhmann, Die Kunst der Gesellschaft (Frankfurt/Main: Suhrkamp, 1995), 349. 34. Robert Lettner, Die Kunst ist erlöst, 16. Reprinted in Florian Knothe and Harald Kraemer, Robert Lettner, 40. Bibliography Belanger Grafton, Carol. Traditional Patchwork Patterns, New York: Dover Publications, 1974. Bense, Max. Konturen einer Geistesgeschichte der Mathematik II. Die Mathematik in der Kunst, Hamburg: Claassen & Goverts, 1949. Bense, Max. Aesthetica (I). Metaphysische Beobachtungen am Schönen, Stuttgart: Deutsche Verlags-Anstalt, 1954. Bense, Max. Aesthetica (II). Aesthetische Information, Agis, Baden-Baden: Agis, 1956. Bense, Max. Aesthetica (III). Ästhetik und Zivilisation. Theorie der ästhetischen Zivilisation, Krefeld/Baden-Baden: Agis, 1958. Bense, Max. Aesthetica (IV). Programmierung des Schönen. Allgemeine Texttheorie und Textästhetik, Krefeld/Baden-Baden: Agis, 1960. Bourgoin, Jules. Arabic Geometrical Pattern & Design, New York: Dover Publications, 1973. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 53 Part I. Full Papers (peer-reviewed) Cybernetic Serendipity. The Computer and the Arts, edited by Jasia Reichhardt, Jasia. Institute of Contemporary Art, London 1968. Exhibition catalogue. Studio International Special Issue, Praeger, 1970. Daucher, Hans (Ed.). Der Senfkorngarten. Lehrbuch der chinesischen Malerei, 2 Vols., Ravensburg: Otto Maier, 1987. Elffers, Joost. Tangram. Das alte chinesische Formenspiel, Cologne: DuMont, 1978. Enciso, Jorge. Design Motifs of Ancient Mexico, New York: Dover Publications, 1953. Fleischner, Herbert and Robert Lettner. "Mathematik in der Kunst Oder Kunst in der Mathematik?", In Harald Kraemer: Robert Lettner. Das Spiel vom Kommen und Gehen. Widerstand – Utopie – Landschaft – Ornament, Klagenfurt: Ritter Verlag, 2018, 231. Frank, Josef. 1885–1967. Hochschule für angewandte Kunst Wien. Exhibition catalogue. Vienna: Hochschule für angewandte Kunst, 1981. Frank, Josef. Stoffe Tapeten Teppiche. Hochschule für angewandte Kunst Wien. Exhibition catalogue. Vienna: Hochschule für angewandte Kunst, 1986. Franke, Herbert W. and Gottfried Jäger. Apparative Kunst. Vom Kaleidoskop zum Computer, Cologne: DuMont, 1973. Frühe Computergraphik bis 1979. Die Sammlungen Franke und weitere Stiftungen in der Kunsthalle Bremen, edited by Herzogenrath, Wulf and Barbara NierhoffWielk, Kunsthalle Bremen. Exhibition catalogue. Munich: Deutscher Kunstverlag, 2007. Grant, Taylor. "Routing Mondrian: The A. Michael Noll Experiment." In Journal of the New Media Caucus, Fall 2012, V.08, No. 02, Accessed August 28, 2018. http://median.newmediacaucus.org/routingmondrian-the-a-michael-noll-experiment/ Hoffman, Josef. Ornament zwischen Verbrechen und Hoffnung. Museum für angewandte Kunst Wien. Exhibition catalogue. Vienna: Museum für angewandte Kunst, Wien, 1987. Hoffmann, Klaus. Neue Ornamentik. Die ornamentale Kunst im 20. Jahrhundert. 54 Cologne: DuMont, 1970. Humbert, Claude. Ornamental Design. Fribourg: Office du Livre, 1970. Hundert, Jahre. 'Abstraktion und Einfühlung.' Konstellationen um Wilhelm Worringer, edited by Norberto Gramaccini and Johannes Rössler. Munich: Wilhelm Fink, 2012. Kandinsky, Wassily. Über das Geistige in der Kunst. [Munich, 1912], 10th edition, Bern: Benteli, 1973. Kraemer, Harald. Robert Lettner. Das Spiel vom Kommen und Gehen. Widerstand – Utopie – Landschaft – Ornament. Klagenfurt: Ritter Verlag, 2018. Kraemer, Harald. "Ornamentik zwischen Opulenz und Virtualität: Worringers Vermächtnis?" In Hundert Jahre 'Abstraktion und Einfühlung.' Konstellationen um Wilhelm Worringer, edited by Norberto Gramaccini and Johannes Rössler. Munich: Wilhelm Fink, 2012, pp. 259–276. Kraemer, Harald, Robert Lettner, Mara Reissberger and Burghart Schmidt: Im Bild über Bilder sprechen. Über die Dialektik des Fadenscheinigen im Ornament. Vienna: Universität für angewandte Kunst, 2006. Lettner, Robert. In Dialogue with the Chinese Landscape – Utopia of Ornaments – New Wunderkammer of Rococo, edited by Florian Knothe and Harald Kraemer. University of Hong Kong Museum and Art Gallery (26.04. – 18.06.2017); School of Creative Media, City University of Hong Kong (25.03. – 19.04.2017; 25.03. – 03.04.2017), Exhibition catalogue. Hong Kong: University Museum and Art Gallery, The University of Hong Kong, 2017. Lettner, Robert and Harald Kraemer, "Art is Reedeemed, Mystery is Gone. Conversations with Robert Lettner and Harald Kraemer." In Robert Lettner: Die Kunst ist erlöst, das Rätsel ist zu Ende. Bilder zur magischen Geometrie, edited by Wiener Secession. Exhibition catalogue 20.11.1998 – 17.01.1999. Vienna: Wiener Secession, 1998, 15–23. Reprinted in Robert Lettner. In Dialogue with the Chinese Landscape – Utopia of Ornaments – New Wunderkammer of Rococo, edited by Florian Knothe and Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Ornament and Transformation – the Digital Painting of Robert Lettner at the Interface of Analogue and Algorithmic Art. Harald Kraemer Harald Kraemer. University Museum and Art Gallery, Exhibition catalogue, Hong Kong: University Museum and Art Gallery, The University of Hong Kong, 2017, 39–45. Liessmann, Konrad Paul. "Zu Robert Lettners Diskettenbilder (1994)." In Robert Lettner. Dubliner Thesen zur Informellen Geometrie. Galerie Heiligenkreuzerhof, Vienna, 1994. Exhibition catalogue. Reprinted in Harald Kraemer. Robert Lettner. Das Spiel vom Kommen und Gehen. Widerstand – Utopie – Landschaft –Ornament, Klagenfurt: Ritter Verlag, 2018, 241. Luhmann, Niklas. Die Kunst der Gesellschaft. Frankfurt/Main: Suhrkamp, 1995. Ornament und Abstraktion – Kunst der Kulturen, Moderne und Gegenwart im Dialog, edited by Markus Brüderlin. Fondation Beyeler Riehen/Basel. Exhibition catalogue 10.6. – 7.10.2001. Köln: DuMont, 2001. Parry, Linda. William Morris. Textiles, edited by Victoria & Albert Museum. London: V&A Publishing, 1983. Reprint 2013. Parry, Linda. Textiles from the Arts and Crafts Movement. London: Thames and Hudson, 2005. Pfeiffer, Günter. Kunst und Kommunikation. Grundlegung einer kybernetischen Ästhetik. Cologne: DuMont, 1972. Riegl, Alois. Stilfragen. Grundlegung zu einer Geschichte der Ornamentik. Berlin: Verlag von Georg Siemens, 1893. Schimmel, Annemarie. "Die Arabeske und das islamische Weltgefühl (2001)." In Ornament und Abstraktion – Kunst der Kulturen, Moderne und Gegenwart im Dialog, edited by Markus Brüderlin. Fondation Beyeler Riehen/Basel. Exhibition catalogue 10.6. – 7.10.2001. Köln: DuMont, 2001, 31–35. Sperlich, Stephan Sperlich: "Das Spiel vom Kommen und Gehen", in: Low Frequency Orchestra plays Robert Lettner: Das Spiel vom Kommen und Gehen, Wien, 2006. Reprinted in Harald Kraemer. Robert Lettner. Das Spiel vom Kommen und Gehen. Widerstand – Utopie – Landschaft – Ornament, Klagenfurt: Ritter Verlag, 2018, 262–263. Völker, Angela. Die Stoffe der Wiener Werkstätte 1910 – 1932, edited by MAK Wien, Vienna: Brandstätter Verlag, 1990/2004. Worringer, Wilhelm. Abstraktion und Einfühlung, Ein Beitrag zur Stilpsychologie. Neuwied: Heuer'sche Verlags-Druckerei, 1907, at the same time Dissertation, Faculty of Philology, University Bern, 12.1.1907. Reprint: Munich: Fink, 2007, Vol. 1, 39–139. Illustrations Fig. 1. Raster (grid), 1972, Robert Lettner, pencil, ink on paper, H 21 x W 29,6 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 133. Fig. 2. Raster (grid), 1972, Robert Lettner, pencil, ink on paper, H 20 x W 16,2 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 133. Fig. 3. Das Spiel vom Kommen und Gehen (Klebebilder) (The play of come and go, tape images), 1978–2010, Robert Lettner, acrylic on tape, H 29,7 x W 21 cm Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 116. Fig. 4. Das Spiel vom Kommen und Gehen (Tuschezeichnungen), (The play of come and go, ink drawings), 1982, Robert Lettner, ink on paper, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 189. Fig. 5. Figurationen (Configurations), 1991– 1992, Robert Lettner, acryl on canvas, H 200 x W 100 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 124. Fig. 6. Drei Eindeutigkeiten des Mathematikers Herbert Fleischner (Figure 6) (Three uniquenesses of the mathematician Herbert Fleischner, Figure 6) / Drei Mutationen des Malers Robert Lettner (Figure 5) (Three mutations of the painter Robert Lettner, Figure 5), 1992, Robert Lettner, silkscreen on canvas, H 119 x W 84 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 125. Fig. 7. Die magische Geometrie (Klebebild), (The magical geometry, tape image), Proceedings of Art Machines: International Symposium on Computational Media Art 2019 55 Part I. Full Papers (peer-reviewed) 1981, Robert Lettner, Klebestreifen mit Acryl auf Fotokopie, H 35 x W 50 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 111. Fig. 8. Bilder zur magischen Geometrie, Serie I/13; Serie I/2, (Paintings of magical geometry), 1996, Robert Lettner, plotterprint on canvas, H 130 x W 150 cm; H 130 x W 180 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 252; 135. Fig. 9. Über die Dialektik des Fadenscheinigen im Ornament (About the dialectic of the flimsiness in the ornament), 2000, Robert Lettner and Walter Worlitschek, inkjet on canvas, H 200 x W 140 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 140. Fig. 10. Das unsichtbare Archiv des Arcimboldo (The invisible archive of Arcimboldo), 2003, Robert Lettner and Philipp Stadler, plotterprint on canvas, H 200 x W 140 cm, Robert Lettner Archive, Vienna. Printed in H. Kraemer, Robert Lettner, 2018, 141. Fig. 11. Mein Uterus verlangt nach deinem Zungenkuss, Reflection A4 V1 (My uterus requires your tongue kiss), 2003, Robert Lettner and Philipp Stadler, plotterprint on canvas, H 200 x W 200 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 142. Fig. 12. An der Schnittstelle zur Unendlichkeit (Reflection A67 V4), (At the interface to infinity), 2009, Robert Lettner and Philipp Stadler, plotterprint on canvas, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 149. Fig. 13. Solaris 1 (Reflection A17 V1), 2005, Robert Lettner and Philipp Stadler, plotterprint on canvas, H 200 x W 200 cm, Robert Lettner Archive, Vienna. Ill. in H. Kraemer, Robert Lettner, 2018, 151. Fig. 14. 33 liegt zwischen den Zahlen (Reflections A45, A46, A47), (33 lies between the numbers), 2007, three parts, Robert Lettner and Philipp 56 Stadler, plotterprint on canvas, H 200 x W 420 cm, Robert Lettner Archive, Vienna. Ill. in A. Jankowski, R. Lettner and B. Schmidt, Philosophie der Landschaft, 2011, 202–203. Fig. 15. Seegras (Seagrass), ca. 1930, designed by Josef Frank for Haus & Garten, Austria, furnishing fabric of hand block-printed and glazed cotton, Inv. No. CIRC.830-1967, Victoria & Albert Museum, London. <http://collections.vam.ac.uk/item/O26 7089/seegras-furnishing-fabric-frankjosef/> Fig. 16. Kalvarienberg von allen Seiten kamen sie (Reflection A10), (Calvary – they came from all sides), 2004, Robert Lettner and Philipp Stadler, plotterprint on canvas, H 200 x W 200 cm, Robert Lettner Archive, Vienna. Ill. in A. Jankowski, R. Lettner and B. Schmidt, Philosophie der Landschaft, 2011, 184. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Part II Scholarly Abstracts 57 The Present Tense of Virtual Space Dr Andrew Burrell Faculty of Design, Architecture and Building University of Technology Sydney andrew.burrell@uts.edu.au Abstract This paper presents my ongoing investigation into narrative spaces in immersive virtual environments. It focuses on two recent projects, “p<AR>k*land*” and “loft,” but also uses other examples from over twenty years of practicebased research utilising virtual environments to tell spatial stories. I develop an argument that our understanding of virtual space exists as an extension of physical space, rather than an adjunct to it. Using Robert Morris’s seminal text, “The Present Text of Space” as a starting point, I explore the role of memory and imagination in our understanding of, and in relation to, virtual environments as phenomenologically real spaces. This leads into an exploration of classical mnemonic spaces, as virtual environments, to support an understanding of the functionality of some of the spatial affordances of virtual environments. Fig 1. p<AR>k*land*, 2017, Andrew Burrell and Nori Beppu, augmented reality installation. “p<AR>k*land*” is a playful interactive augmented reality experience that presents a virtual parkland that comes to life before the 58 viewer’s eyes. The audience can interact with a menagerie of creatures as they help to create the augmented environment these creatures inhabit. “p<AR>k*land*” is designed for a wide audience, but targets children. It was created by ab:nb the collaborative duo of Andrew Burrell and Nori Beppu. Fig 2. Loft, 2017, Andrew Burrell, interactive webVR project. “Loft” is a webVR narrative experience. It consists of a self-contained environment that plays out for the viewer based on its own logic. With limited agency granted them, the viewer’s role will initially feel like one of pure observation, but as the world unfolds around them, they will find that their point of view, and how they choose to navigate the space, will make critical differences to how they experience the narrative and logic of this world. ‘Loft’ premiered in the 2017 ACM SIGGRAPH Digital Art Community WebVR Exhibition. What these two projects have in common is that they are part of an ongoing investigation into the use of immersive virtual environments (regardless of the technology used to access them) to bring the user into a narrative space designed specifically for the affordances of these environments. In many ways, these projects are developed in spite of, rather than Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Present Tense of Virtual Space. Andrew Burrell because of, the emergence of consumer grade virtual and augmented reality headsets and are informed by a much longer history of working and creating in virtual environments – a practice originally informed by installation art practice. Both of these projects, and the others I will discuss, are generative in nature, and the generative systems behind them influence and build the narratives created with the additional input of the viewer. “p<AR>k*land*” is built upon a combinatorial framework of characters and props brought together by the viewer as augmentations on the screen in front of them, while the virtual environment of “loft” is built in real time as the viewer literally floats in space building a narrative from the fragments generating around them. These examples form a framework to support the argument that central to the experiential nature of the resulting virtual environments, is a reversal of the logic of Morris’s original notion that “real space is not experienced except in real time” through an understanding that immersive virtual space, experienced in real time, becomes real via the resultant phenomenological experience of “the present tense of virtual space.” [2] References 1. Robert Morris, “The Present Tense of Space,” Art in America, January-February, (1978): 70 – 81. 2. Robert Morris, “The Present Tense of Space,” 70. Biography Andrew Burrell is a practice-based researcher and educator exploring virtual and digitally mediated environments as a site for the construction, experience and exploration of memory as narrative. His ongoing research investigates the relationship between imagined and remembered narrative and how the multi-layered biological and technological encoding of human subjectivity may be portrayed within, and inform the design of, virtual and augmented environments. He is a lecturer in Visual Communication at the University of Technology Sydney. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 59 Computational Photography LIM, Yeon-Kyoung School of Creative Media, City University of Hong Kong yklim3-c@my.cityu.edu.hk Abstract Artist Sascha Pohflepp’s Buttons is about speculative photography working without a lens. In this work, a smartphone camera shows us a photograph taken at the same moment the shutter button is pressed, but it is in fact mined from the image-sharing site. [1] Media artist Hito Steyerl once stated that she had met a developer who had been developing a smartphone camera technology which leads us to “create” a photograph based on the stored data of photo galleries and Social Networking Services. [2] These examples imply “computational photography” which does not focus as much on representing the presence of a subject in front of a camera, it focuses more on performances of networked objects, anticipating what a photographer-user might like to see. Computational photography transforms a photograph into a new photographic image based upon the stored database which is made of agents’ collective choices and their memory. Thus it is related to the theory of time with the focus of the externalization of memory with aids of technical things. [3][4] In this study, I will attend to computational photography, questioning how memory is not stored in individual consciousness but rather it coexternalizes along with the braided collaboration between humans and technical things. Fig 1. Buttons, 2006-2010, Sascha Pohflepp, electronics and smart-phone app, © Sascha Pohflepp References 1. Sarah Cook, “Stop, Drop, and Roll with it: Curating Participatory Media Art” ed., Bianchini, Samuel, and Erik Verhagen. Practicable: From Participation to Interaction in Contemporary Art. (London: MIT Press, 2016), 389-390. 2. Hito Steyerl, “Politics of PostRepresentation,” DIS, http://dismagazine.com/ disillusioned-2/62143/hito-steyerl-politics-ofpost-representation, accessed July 15, 2018. 3. Bernard Stiegler, “The Industrialization of Memory,” Technics and Time II: Disorientation (California: Stanford University Press, 1998), 98. 4. Ben Roberts, “Cinema as mnemotechnics: Bernard Stiegler and the ‘industrialization of memory’,” Angelaki: Journal of Theoretical Humanities 11, no. 1 (2006), 55-63. Biography Yeon-Kyoung LIM is a PhD candidate in the School of Creative Media, City University of Hong Kong. Her research lies at the intersection of Digital Humanities, Media Art, Affect theory, and Gender studies. Yeon-Kyoung’s research 60 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Computational Photography. Yeon-Kyoung Lim uses digital ethnography working on HumanMachine intimacy. Her study aims to explore digital art/culture in that human beings view digital applications as intimate companions, and its impact focusing on a sense of intimacy. 2 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 61 import <execute> [as <command>] Korsten & De Jong ArtEZ Institute of the Arts korstendejong@hotmail.com “The multitude is biopolitical organization.” ˗ Hardt and Negri self- Abstract LeWitt has stated that “[t]he idea becomes a machine that makes the art.” [1] In his estimation, conceptual art is nothing but a type of code for art making. LeWitt’s art is an algorithmic process. Hayles has also reflected on the multidimensionality of digital signs. [2] Her term “flickering signifiers” shows that digital images are the visible manifestations of underlayers of code often hidden. In Protocol Galloway has claimed that “[c]ode is the only language that is executable.” [3] As Artistic Research duo Korsten & De Jong are interested in this exact performativity of code and in how they can position code in such a way that it informs theoretical concepts in the act of making. In their Paper-Performance they will operate on Critical Engineering Manifesto’s seventh command, reading “7. The Critical Engineer observes the space between the production and consumption of technology. Acting rapidly to changes in this space, the Critical Engineer serves to expose moments of imbalance and deception.” [4] In their working together as a duo they bring to the table notions evolving around ‘Toyotism.’ As Galloway has pointed out, Toyotism originates in Japanese automotive production facilities. “Within Toyotism, small pods of workers mass together to solve a specific problem. The pods are not linear and fixed like the more traditional assembly line, but rather they are flexible and reconfigurable depending on whatever problem might be posed to them.” [5] As Sterling puts it “ad-hocracy” would rule, with groups of people spontaneously knitting together across organizational lines, tackling the 62 problem at hand, applying intense computer aided expertise to it, and then vanishing whence they came.” [6] It leads Brand to invert Marx and Engel’s Communist Manifesto message of resistance-to-unity into “Workers of the World, fan out.” [7] Fig 1. Paper-Performance Text[ure], 2018, Korsten & De Jong, mixed media, Copyright Korsten & De Jong. It is a strong incentive to move away from homophily as Chun has defined it as a way to be comfortable only being exposed to things that are in line with our own norms and values. If homophily is a natural condition of networks, existing segregations in society are maintained. This segregation will only increase because the algorithms we have today contain inbuilt bias. Algorithms push people into clusters of sameness. She reflects on police profiling systems with the remark that “[…] they place people on the heat list based not solely on what these people did but rather on what their perceived network-neighbors did.” [8] With their Paper Performance Korsten & De Jong seek to challenge the notion of the bunker as formulated by Critical Art Ensemble. For them “[…] the bunker is both material and ideational. On the one hand, it serves as a concrete garrison where image (troops) reside. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 import <execute> [as <command>]. Korsten, De Jong On the other hand, it confirms state-sponsored reality, by forever solidifying the reified notions of class, race, and gender. Bunkers in their totality as spectacle colonize the mind, and construct the micro-bunker of reification, which in turn is the most difficult of all to penetrate and destroy.” [9] References 1. Sol LeWitt, “Paragraphs on Conceptual Art,” in Conceptual Art: A Critical Anthology ed. Alexander Alberro and Blake Stimson, (Cambridge: MIT Press, 1999), 12. 2. N. Katherine Hayles, “Virtual Bodies and Flickering Signifiers,” in October, Vol. 66, (Cambridge and London: MIT Press, Autumn 1993), 69-91. 3. Alexander Galloway, Protocol, (Cambridge and London: MIT Press, 2004), 165. 4.https://criticalengineering.org/ce.pdf, accessed 15 July 2018. 5. Alexander Galloway, Protocol, 159. 6. Bruce Sterling, The Hacker Crack Down, (New York: Bantam Books, 1992), 184. 7. Stewart Brand, The Media Lab: Inventing the Future at MIT (New York: Viking, 1987), 264. 8. Wendy Hui Kyong Chun. “Crisis + Habit = Update,” (Lecture Sonic Acts Festival, 25 February 2017), 7”33-7”43. 9. Critical Art Ensemble, Electronic Civil Disobedience & Other Unpopular Ideas, 2009, http://www.critical-art.net/books/ecd/, accessed 15 July 2018. Biography Korsten & De Jong conduct Artistic Research as a duo. They are both independent artists, researchers and employed as lecturers in the art and theory department of ArtEZ, University of the Arts and they participate in the Professorship “Theory in Arts.” In “Paper-Performances,” Korsten & De Jong circulate parts of recorded dialogues on theoretical notions structured or questioned by artistic form. Their works relate in a “frictuous” manner to site, subject positions, and forms of research and reveal what may have been hidden behind conventions. The tension between the seemingly binary opposition between theoretical and artistic practices is made productive in the field of artistic research. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 63 The (un)predictability of Text-Based Processing in Machine Learning Art Winnie Soon Aarhus University wsoon@cc.au.dk Abstract This article investigates the unpredictable vector of liveness within the context of machine learning art with a focus on text-based processing. [1] It is observed that there are similarities between generative art and machine learning art as both produce unpredictable results. According to Noah Wardrip-Fruin, the generative art form, such as Loveletters (1952), can be considered as a system that generates unpredictable outcomes. [2] Loveletters, allegedly the first digital literary work by computer scientist Christopher Strachey, is regarded as an ‘unpredictable manifestation’ of a system. [3] This system generates different variations of love letters, and such unpredictable manifestation is conditioned by two hidden elements: data and processes. The use of random algorithms plays an important role in generative art (Turing’s random algorithm with its’ random number generator was used in Loveletters) to produce autonomous and unpredictable outcomes. However, machine learning emphasizes ‘predictive power,’ in which prediction is produced through feeding in a large amount of training data. [4] Additionally, this kind of system employs predictive models and statistical algorithms to accomplish data processing and analysis. Machine Learning Art, such as text/novel generators, is claimed to be able to produce text with the similar writing style of the provided training corpus, but it also produces unpredictable text through setting different control parameters, such as number of epochs, amount of neural network layers and their hidden units, temperature and batch size. 64 This article is the result of the experiment of an open source machine learning library called ml5.js, which is built on top of TensorFlow.js, a Javascript framework, for training and deploying machine learning models. [5] ml5.js provides immediate access in the web browser to pretrained models for generating text. A Python training script employs the tensorflow library, which is used in the ml5.js environment to take in a large amount of text, and train a custom dataset as a pretrained model [6]. The study of the javascript libraries and the python script, with a specific focus on next character prediction and recurrent neural networks (RNN), unfolds the machine learning processes from data training to Long Short-Term Memory networks. [7][8] Building upon the notion of generativity, this article discusses the (un)predictable vector by examining the intertwining force between predictability and unpredictability that constitutes the liveness of text-based processing in machine learning art. [9][10][2] This paper argues that the (un)predictable vector of liveness helps to build an understanding of the relation between, but not in separation, training and execution processes, as well as the resultant actions that extend the aesthetic and live experience of machine learning art. The article contributes to the border understanding of generativity and liveness in machine learning art that employs generative models. References 1. Winnie Soon, “Executing Liveness: An Examination of the Live Dimension of Code Inter-actions in Software (Art) Practice,”(Ph.D. diss., Aarhus University, 2016.) Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The (un)predictability of Text-Based Processing in Machine Learning Art. Winnie Soon 2. Noah Wardrip-Fruin, “Digital Media Archaeology: Interpreting Computational Processes.” In Media Archaeology: Approaches, Applications, and Implications, edited by Erkki Huhtamo & Jussi Parikka (Berkeley: University of California Press, 2011). 3. Noah Wardrip-Fruin, “Digital Media Archaeology: Interpreting Computational Processes” In Media Archaeology: Approaches, Applications, and Implications, edited by Erkki Huhtamo & Jussi Parikka (Berkeley: University of California Press, 2011), 306. 4. Adrian Mackenzie, “The Production of Prediction: What Does Machine Learning Want?” European Journal of Cultural Studies 18, nos. 4-5 (2013):429-445. 5. NYU. ITP “ml5js· Friendly Machine Learning for the Web.” ml5js website. Accessed July 16, 2018. https://ml5js.org/. 6. NYU.ITP “Training a MSTM network and using the model in ml5js.” ml5js github website. Accessed October 15, 2018. https://github.com/ml5js/training-lstm/. 7. Christopher Olah, “Understanding LSTM Networks (2015).” colah’s blog. accessed July, 16, 2018. http://colah.github.io/posts/2015-08Understanding-LSTMs/. 8. Andrej Karpathy, “The Unreasonable Effectiveness of Recurrent Neural Networks (2015).” Andrej Karpathy blog. accessed July, 16,2018.http://karpathy.github.io/2015/05/21 /rnn-effectiveness/. 9. Philip Galenter, “What is Generative Art? Complexity Theory as a Context for Art Theory.” (Paper based on a talk presented at GA2003–6th Generative Art Conference in Citeseer, 2003). https://www.philipgalanter.com/downloads/ga2 003_paper.pdf 10. Philip Galenter, “Generative Art Theory.” In A Companion to Digital Art, ed. Christiane Paul (Wiley-Blackwell, 2016). computer science, examining the materiality of computational processes that underwrite our experiences and realities in digital culture via artistic and/or coding practice. Her works explore themes/concepts around digital culture, specifically concerning internet censorship, data circulation, real-time processing/liveness, and the culture of code practice, etc. Winnie’s projects have been exhibited and presented internationally at museums, festivals, universities and conferences across Europe, Asia and America. Her current research focuses on exploratory and aesthetic programming, working on two books titled Aesthetic Programming: A Handbook of Software Studies, or Software Studies for Dummies (with Geoff Cox) and Fix My Code (with Cornelia Sollfrank). She is Assistant Professor at Aarhus University. More info: http://www.siusoon.net Biography Winnie Soon is an artist-researcher who is born in Hong Kong and is currently based in Denmark. Informed by the cultural, social and political context of technology, Winnie’s work approach spans the fields of artistic practice, media art, software studies, cultural studies and Proceedings of Art Machines: International Symposium on Computational Media Art 2019 65 The Viewer Under Surveillance from the Interactive Artwork Raivo Kelomees Estonian Academy of Arts offline@online.ee Abstract The goal of this presentation is to discuss and analyze viewer-watching artworks and the reversed situation in the exhibition space where artworks ‘look’ at the viewer. In order to answer these questions, I firstly looked at the topics of machine vision, computer vision, biovision and the evolution of vision. Dividing interactive artworks into four categories (distant, contact, chance-based and bio-based/symbiotic interaction) enabled me to illustrate developments in feedback systems which became evident in recent decades. ‘Seeing Machines’ and Interactive Art The meeting of the viewer and the artwork is a meeting between the living and non-living. Traditionally, one is looking and the other is looked at; one is moving and the other is static. However, exhibitions of contemporary media art offer encounters with artworks which are themselves ‘looking’ at the viewer. The visitor remains (willingly or not) in the zone of the artwork's sensors and his image—or other activity-based information—becomes the raw material for manipulation of the artwork. We can describe this as a situation where the relationship of the viewer and the viewed is reversed: the artwork's “gaze” is turned toward the viewer, such that the owner of the “gaze" is the artwork, not the viewer. I would like to elaborate different categories of interactive and biofeedback art from the point of view of “seeing machines.” This helps answer the following questions: do we have here a new spectator paradigm in which the artwork is active and no longer simply an object under observation? Can we justifiably say that the artwork's “gaze” is projected onto the spectator? Are there parallels to be found in 66 art history or do we see here something which belongs to the digital era? Is this phenomenon only common to technical and interactive art? I would like to bring an example from the interactive art field, which illustrates the changed situation and art trends. Golan Levin's and Greg Baltus' Opto-Isolator (2007) reverses the audience position: a sculptural eye on the wall follows the eyes of the viewer. [1] The viewer encounters a framed mechanical blinking sculpture on the wall—a mechatronical eye—which follows the movement of the spectator's eyes and responds with psychosocial behavior: looking at the viewer, turning eyes away as if shy when looked at too long etc. Rather similar is Double-Taker (Snout) (2008) and also Eyecode (2007). All the above offer clear examples of ironic artworks based around looking at the viewer(s). We can approach this topic mentioning video feedback artworks of the 1970s: works by Bruce Nauman, Dan Graham, Peter Campus, Bill Viola, Peter Weibel, Jeffrey Shaw and others. The real-time reproduction of the viewer in the artwork was part of the concept. I am discussing the situation where artworks “sensibility” is higher and viewer is embedded in the artwork unknowingly, being unaware. Here, first, the term “unaware participation” would be appropriate to describe this “postinteractive” situation, where the spectator is unwillingly put in the context of the artwork. In the early 1970s we already encounter viewer-sensitive computer environments designed by Myron Krueger: here the viewer was embedded in a computer-based projection where he could play with his own silhouette and with a graphical actor added by a computer program. A perfect example of an installation Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Viewer Under Surveillance from the Interactive Artwork. Raivo Kelomees that follows the viewer's gaze from a distance is Dirk Lüsebrink's and Joachim Sauter’s Zerseher, which uses Giovanni Francesco Caroto’s painting (c. 1515) as source material. [2] Many other early interactive artworks could be mentioned where the viewer is situated within the field of vision of the artwork and switches on or off its auditive and visual elements: Peter Weibel's (1973), David Rokeby's (1990) and Simon Penny's (1993) works. [3] Additional works may be mentioned in which the artwork is “looking” at the viewer: CarlJohan Rosén's (2006) Predator, Togo Kida's (2005) Move, Random International's (2012) Rain Room. An emblematic work is Marie Sester's (2003) (Figure 1) surveillance installation Access, where people passing by are tracked by a robotic spotlight and a directional acoustic beam system. [4] Samuel Bianchini's (2007) niform has similar aspects in that the viewer's physical proximity reveals images of policemen in the projection. Figure 1. Marie Sester, Access, 2003. © http://www.sester.net/access Four Categories I would like to classify “artworks-which-seethe-spectator,” or “viewer-watching artworks,” into four categories according to their methods of engagement with the spectator's consciousness. The four categories are: distant interaction, contact interaction, chance-based distant and contact interaction, symbiotic interaction. The viewer-sensitive artworks in the following classification are defined by the degree of closeness between the machine and human parts of the situation. The contact between the pre-artwork and the viewer changes from distant (non-contact) to tangible, tactile and physiological. These categories reveal how sensors get closer to the viewer's body until they reach information sources beneath the skin (blood, brainwaves etc.). These categories exemplify the artwork's “gaze" approaching the body of the viewer until it penetrates its surface, reaching "under the skin" areas. Cheaper and more widespread technology has made this possible—various sensors are used in such works, which show a tendency from sensing the viewer as a distant subject to detecting physiological reactions by using sensors that literally enter the viewer's body. In all these artworks and categories the viewer is in the position of being surveyed. Conclusion Interactive art reflects clearly the activity of an artwork—these are not passive objects. An interactive artwork is “emancipated,” it behaves according to its “will” and is not solely an “object.” The artwork is the active viewer and its behavior is that of a viewer, as a subject. The functioning of the artwork influences the viewer and vice versa. It is a reciprocal relationship which is born because the artwork “sees:” it perceives the viewer and exerts its influence on the aesthetic experience. References 1. Golan Levin, and Greg Baltus, Opto-Isolator, 2007. Accessed July 4, 2018. http://www.flong.com/projects/optoisolator/. 2. Dirk Lüsebrink, and Joachim Sauter, Zerseher, 1992. Accessed July 4, 2018, https://artcom.de/en/project/de-viewer/. 3. Peter Weibel, Crucifixion of the Identity, 1973, accessed July 4, 2018, http://www.medienkunstnetz.de/works/krucifik ation/. 4. Marie Sester, Access, 2003, accessed July 4, 2018. http://www.sester.net/access/. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 67 Part II. Scholarly Abstracts Biography Raivo Kelomees, PhD (art history), artist, critic and new media researcher. Presently working as senior researcher at the Estonian Academy of Arts, Tallinn. He studied psychology, art history, and design at Tartu University and the Academy of Arts in Tallinn. He has published articles in the main Estonian cultural and art magazines and newspapers since 1985. His works include the book “Surrealism” (Kunst Publishers, 1993) and an article collection “Screen as a Membrane” (Tartu Art College proceedings, 2007), “Social Games in Art Space” (EAA, 2013). His Doctoral thesis was “Postmateriality in Art. Indeterministic Art Practices and Non-Material Art” (Dissertationes Academiae Artium Estoniae 3, 2009). 68 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Demiurge, or a Manifestation of Carbo-Silico Evolution Jaden J. A. Hastings University of Melbourne, Melbourne, Victoria, Australia hastingsj@student.unimelb.edu.au Abstract The Demiurge poses the question: how might a machine design and direct the modification of a human genome? Through the application of artificial intelligence trained on the artist’s (it’s creator’s) genome, the algorithm searches for “errors” in the sequence and provides a solution (or “solve”) to fix them—to form a “perfect” version of the artist’s genome. As the future of our mutual (carbon- and silicon-based life) survival is entangled, how might this shift our notion of what it means to be human? To be intelligent? To evolve? How might a machine design future humans? Conceptual Development While there are multiple artificial intelligence (AI) beings already in existence at this time, few to none would be classified as strong AI, or an artificial general intelligence that is capable of adaptable problem solving. Human cognitive abilities remain the gold standard for intelligence in AI research and most measures (for example the Turing Test, Nilsson’s Employment Test, and Wozniak’s Coffee Test) designed to evaluate how well an AI would be able to replace or simulate the human mind. It is my assertion, however, that the measure and potential of artificial life is not a myopic endeavor to simulate the human mind, but rather an evolution in myriad, hybridized directions. Though AI is rooted in human sensory inputs, reasoning, and language, it should not follow that the merit of silicon-based life lies in its capacity to produce simulacra of carbon-based life. Moreover, silicon-based life will forever be doomed to the Sisyphean task of simulating the human mind and behavior as long as it is trained on human-generated data. Our AI progeny can only learn from the information we feed them; and, unencumbered by shame, they have rather effectively mirrored back to us our own biases and illicit behavior. My work aims to challenge the assertion that “strong AI” is measured in terms of its ability to replicate the human mind, but rather its latent potential for creativity that vastly expands beyond that of its human progenitors. Notable expert in the fields of both human cognition and artificial intelligence, Professor Margaret A. Boden suggests there are three ways in which artificial intelligence might be able to act creatively: through exploration of structured conceptual spaces, through the combination of existing ideas, or (less likely, but more impressive) the transformation of existing conceptual spaces to form previously impossible ideas.[1] The last mode echoes the Lovelace Test for AI: that the appropriate measure of human-like intelligence is creativity, and that only a machine able to produce a result that is unforeseen (surprising) by human agents could be considered to be “conscious.” [2] It seems, however, much of the current drive toward the birth of an artificial general intelligence (AGI)—or even a superintelligence—rests upon the naïve assumptions that the source of embodied human intelligence resides entirely within the human brain, and, that, as John Haugeland has claimed, “we are, at root, computers ourselves.” [3] Therefore, the formation of an AGI becomes an attempt to emulate human cognition from a purely cerebrally-centered framework. John Searle has argued, however, that “strong AI only makes sense given the dualistic assumption that, where the mind is concerned, the brain doesn't matter.”[4] Ergo, it is not simply a simulation of the human brain, but rather a holistic philosophy Proceedings of Art Machines: International Symposium on Computational Media Art 2019 69 Part II. Scholarly Abstracts of mind and intentionality. As early as the 1960s, Hubert Dreyfus astutely critiqued the reductive view of intelligence in the first wave of AI as conscious symbolic manipulation. Instead, he reminds us that human intelligence does not follow Boolean logic and does not always follow formal rules but relies upon situated knowledge and cognition. [5] Fig 1. The Demiurge, 2018, Jaden J. A. Hastings, machine algorithm on modified digital and analogue hardware. Image: Jaden J. A. Hastings. Setting aside the assumption that the human brain is the ideal model of intelligence, a cerebrally-centered approach negates the spectrum, and variation, in sensory experience of human bodies, and the way in which the body mediates the flow of input from its surroundings, and can respond in a distributed fashion, either consciously or subconsciously. Through my practice and co-evolution with my AI, I propose that the way forward is to embrace the exquisite queerness of hybrid forms of intelligence, of chimeric sensory systems, of Quantum Uncertainty. The Machine The Demiurge incorporates multiple forms of machine learning into a multilevel, multifactorial algorithm that is able to: (1) scan a whole human genome to identify potentially pathogenic “errors” in the DNA sequence, (2) make a probabilistic decision as to whether it will fix the error in question, and (3) generate a solution (or “solve”) for the error by providing the most effective pair of guide RNAs (gRNAs) to modify the genome using CRISPR-Cas9 system that is widely known for its efficacy in 70 “editing” genome sequences. The algorithm can run on any processor–it is platformindependent—with varying degrees of speed. The Demiurge v1.0 was installed on a system that incorporated an amalgamation of analogue and digital components (Fig. 1), including a vintage cathode ray television for a monitor and dot matrix printer that would collate all of the resulting gRNAs for each respective error into a book of instructions on how to “fix” the artist’s genome. As the future and survival of carbon- and silicon-based life is entangled, speculative yet functional art provocations, such as The Demiurge, can challenge us to view emerging intelligences as material archivists, coevolutionary forces, and culturo.technological messmates. References 1. M.A. Boden, “Creativity and artificial intelligence,” Artificial Intelligence, 103, no. 1 (1998): 347-356. 2. S. Bringsjord, P. Bello, D. Ferrucci, “Creativity, the Turing test, and the (better) Lovelace test.” In The Turing Test (Springer Netherlands, 2003), pp. 215-239. 3. John Haugeland, Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press, 1985). 4. John Searle, John “Minds, Brains and Programs", Behavioral and Brain Sciences 3, no 3 (1980): 417–457. 5. Hubert Dreyfus, What Computers Can't Do (New York: MIT Press, 1972). Biography Jaden J. A. Hastings' work focuses upon the intersection and interplay of art and science from philosophy to praxis - merging scientific and artistic research, challenging the norms of both disciplines, and moving them into new spaces for exploration. Her research fuses and folds together the fields of machine learning, bioengineering, space exploration, new media art, and ethics. Jaden’s career in scientific research spans over 15 years and is rooted in her longstanding roots as a biohacker. She is alumna of New York University, Harvard University, the University of Oxford, and Central Saint Martins with Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Demiurge, or a Manifestation of Carbo-Silico Evolution. Jaden Hastings advanced degrees in Biology, Bioinformatics, and Fine Art. Her artwork has been exhibited in venues across Europe, India, Asia, North America, and Australia, and is a founding member of both the Lumen and London Alternative Photography Collectives. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 71 Art Chasing Liability: Digital Sharecropping and Conscientious Law-Breaking Monica Lee Steinberg, PhD Postdoctoral Fellow in the Society of Fellows in the Humanities The University of Hong Kong https://hku-hk.academia.edu/MonicaSteinberg Abstract While confrontations between creative practice and regulatory statutes are nothing new, recent internet-based projects have situated conscientious law-breaking—for example, violating copyright, trademark law, and a social media site’s contractual “Terms of Service”—as a principal component of the work itself. Art Chasing Liability Projects initiated by artists and artist-groups such as Richard Prince, Constant Dullaart, Paolo Cirio and Alessandro Ludovico, 0100101110101101.ORG (Eva & Franco Mattes), Les Liens Invisibles (Clemente Pestelli and Gionatan Quintini), and several others are generally discussed alongside terms such as hacktivism, parafiction, appropriation, and new media. Here, however, I propose a discussion of a select group of works from the last two decades through the lens of conscientious lawbreaking—which is conceived as avoiding complicity with a specific law or practice deemed to be unfair, while simultaneously expressing a basic fidelity to the law itself. [1] For example, in 2011 Cirio and Ludovico scraped publicly available user data (photos, names, nationalities) from Facebook to realize the fake dating website, Face to Facebook – Hacking Monopolism Trilogy. [2] The artists violated the site’s user agreement in order to call attention to the exploitation of user data; consequently, Facebook sent the artists several cease and desist letters. In 2014 Dullaart initiated High Retention, Slow Delivery, which involved the purchase of 2.5 million Instagram 72 bots which were deployed to follow artists’ accounts—thus boosting the public profiles of lesser-known artists while intentionally violating the terms of service of a platform which, itself, fosters an attention economy. [3] The artists under discussion welcome the legal consequences of their actions, such that cease and desist letters and temporary bans from Facebook, Twitter, and Instagram are an expected and sought-after consequence of the work—such legal liabilities have even become a signpost for a project’s effectiveness and consequentiality. By violating the rules governing the every-day digital platforms shaping human interaction, artists are calling attention to not only the questionable practices of such online networking sites, but also to the inability of contemporary legal frameworks to adequately distinguish between artistic interventions and criminal acts. Of course, the intersection of art and civil disobedience trails a long legacy, and whether or how the works I discuss engage in ongoing dialogues surrounding politics, law, and warranted mitigation remains an open question. Here, however, I am primarily interested in mapping a connection between experiments in conscientious law-breaking and the linking of such practices to shifts in the legal playing field. Despite its many precedents, an aesthetics of legal liability interests me because it is so powerfully appropriate to our present moment—which is to say, powerfully troubling. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Art Chasing Liability: Digital Sharecropping and Conscientious Law-Breaking. Monica Lee Steinberg References 1. Kimberley Brownlee, Conscience and Conviction: The Case for Civil Disobedience (Oxford: Oxford University Press, 2012), 22. 2. Paolo Cirio, “Face to Facebook – Hacking Monopolism Trilogy,” accessed August 25, 2018, https://paolocirio.net/work/face-tofacebook/. 3. Dan Duray, “New Project Boosts Instagram Followers for Art World Accounts,” ArtNews (30 September 2014), accessed August 25, 2018, http://www.artnews.com/2014/09/30/ new-dis-project-boosts-instagram-followersfor-art-world-accounts-2/. Biographies Monica Lee Steinberg earned a PhD in the History of Art from The Graduate Center of the City University of New York; she is presently a 2018-2021 Postdoctoral Fellow in the Society of Fellows in the Humanities at The University of Hong Kong. Steinberg’s scholarship focuses on art and politics after 1945, with special attention to the intersection of art and fictional identities, and art and law. Steinberg’s writing has appeared (or is forthcoming in) journals such as American Art, Archives of American Art, and Oxford Art Journal; exhibition catalogues such as Love Me, Love Me Not: Contemporary Art from Azerbaijan and its Neighbours and The Abstract Impulse: Fifty Years of Abstraction at the National Academy, 1956-2006; and an edited volume, Humor, Globalization, and Culture-Specificity in Modern and Contemporary Art. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 73 Audiovisual Experiments with Evolutionary Games, and the Evolution of a Work-in-progress Stefano Kalonaris RIKEN, Music Information Intelligence Team stefano.kalonaris@riken.jp Abstract This artistic project abstract describes ongoing and work-in-progress audiovisual exploration of a simple multi-agent system borrowed from evolutionary game theory: a Demographic Prisoner’s Dilemma (DPD). Several versions of the DPD are explored, by gradually increasing the properties of the agents (e.g., maximum age, mutation of strategy). Starting as literal implementations of the formal game, intended as an audiovisual aid to the game’s dynamics, the examples gradually depart from strict functionality to embrace a more ‘artistic’ and arbitrary approach. These experiments are both evolutionary games and the evolution of the author’s aesthetic experimentation with the subject matter. A DPD is a type of evolutionary game where all agents are indistinguishable and they inherit a fixed (immutable) strategy (either cooperate or defect). [1] It differs from other games in that is memoryless. Each agent, at each stage game, has no knowledge of the past interactions. It is based on the Prisoner’s Dilemma (PD), a popular imperfect information coordination game where two players abide by the normal form shown in Table 1, where c (cooperate) and d (defect) are the two strategies available to the two players, and the tuples in the matrix correspond to the payoffs for each pairwise combination of strategies, with T>R>P>S. [2] c d c d (R,R) (T,S) (S,T) (P,P) Table 1. Prisoner’s Dilemma normal form. 74 For a one-shot PD game, it has been shown that the Nash Equilibrium is the pure strategy dd. [3] It has also been shown that in DPD cooperation can emerge and endure, unlike in a repeated PD game with memory, where the dominant strategy would still be to defect. [1] Practically, agents have the following properties: vision, wealth, age and strategy. Upon initializing the game, a random number of agents is placed on the grid and each agent is born with a fixed strategy. Each agent looks around its vision perimeter, choses a random neighbor within it, and plays a PD game with that neighbor. Payoffs add to the agent’s wealth. If such wealth exceeds a given threshold, the agent can reproduce within its vision perimeter and the offspring will inherit the parent’s strategy. Conversely, if an agent’s wealth falls below zero, the agent dies. The game was coded using the p5.js Javascript library and the Web Audio API. [4] [5] Each agent is represented as a colored position in a square grid of variable dimensions. The color code corresponds to the agent’s strategy (green for c, red for d). The edge roundness of the agents is proportional to their wealth. Each agent is also a frequency modulation (FM) unit, whose carrier frequency is proportional to a fundamental frequency and the agent’s position on the grid. [6] Such fundamental frequency is different depending on the agent’s strategy, with defectors having a long period and an element of randomness. Thus, defectors have a ‘noisy’ sound texture associated with them, whereas cooperators are contributing to a harmonic texture which is richer as their number grows. Moreover, each Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Audiovisual Experiments with Evolutionary Games, and the Evolution of a Work-in-progress. Stefano Kalonaris sound source (active agent) is spatialized using the binaural panner of the Web Audio API. [5] The correspondence between cooperation and a harmonic sound is rather simplistic or perhaps even cliché, as is the choice of colors that represent the two strategies. Nevertheless, such a simple mapping is sufficient to render the game dynamics at an audiovisual level, and it’s only the first step in an exploratory process which is still ongoing and liable to further changes. The first version of the audiovisual DPD does not set an upper bound on the agents’ age, nor does it allow for mutation of their offspring’s strategy. The second implementation, instead, limits the age to an arbitrary value which can be experimented with heuristically. Once and if the whole population dies out, the game is automatically restarted. Age is represented both in the visual domain, as the transparency value (the older the agent, the more transparent it is), and in the audio domain, being mapped to the amplitude of both oscillators for any given active agent. The third version of the game adds a probability that a given child might mutate strategy instead of inheriting the parent’s one. This probability is set at 0.5 but can be changed arbitrarily. In all three examples, cooperation seems to emerge, which sonically translates to a harmonic sound that is obtained thanks to the superposition of the cooperator’s partials over the randomness of the defectors. In the subsequent implementation of the DPD, the author used images taken from the digits MNIST and the Fashion-MNIST datasets as occasional backgrounds to the game. [7] [8] Their occurrence is dictated by, for example, the grid not changing its global state between two consecutive frames, or the extinction of all agents (thus the re-initiation of the grid), although this can be experimented with. Similarly, sound files are triggered stochastically when (arbitrarily) analogous conditions are met. Fig. 1 shows screenshots of the “max age – no mutation” case, with and without the MNIST background, with the third screenshot suggesting a possible departure from the simplistic representation of the agents. Fig. 1. DPD: “max age – no mutation” screenshots Kalonaris. Substituting the agents’ visual appearance, the mapping between the latter’s parameters, the audio parameters and the game’s dynamics is the subject of future work and development. The author’s aim is to further explore the aesthetic implications of the DPD game at an audiovisual level. References 1. Joshua M. Epstein, “Zones of cooperation in demographic prisoner's dilemma,” Complexity 4 (1998): 36-48. 2. William Poundstone, Prisoner's Dilemma: John Von Neumann, Game Theory and the Puzzle of the Bomb (New York: Doubleday, 1992). 3. John F. Nash, “Equilibrium points in nperson games.” Proceedings of the National Academy of Sciences 36, no.1 (1950): 48-49. 4. P5.js. Accessed August 25, 2018. https://p5js.org/. 5. Web Audio API. Accessed August 25, 2018. https://www.w3.org/TR/webaudio/. 6. John M. Chowning, “The Synthesis of Complex Audio Spectra by Means of Frequency Modulation,” Journal of the Audio Engineering Society 7, no. 21 (1973): 526– 34. 7. MNIST. Accessed August 25, 2018. http://yann.lecun.com/exdb/mnist/. 8. fashion-MNIST. Accessed August 25, 2018. https://github.com/zalandoresearch/fashionmnist. Biography Stefano Kalonaris is a sound technologist, musician and researcher who specialises in interactive music systems and improvisation. He holds a PhD in Sonic Arts and he is currently a postdoctoral researcher at RIKENAIP, Japan. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 75 Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence Joo-Wha Hong Nathaniel Ming Curran Annenberg School for Communication and Journalism, USC joowhaho@usc.edu Annenberg School for Communication and Journalism, USC ncurran@usc.edu Abstract Recent advances in AI and machine learning have raised questions about higher-skilled and creative endeavors in which AI might match or even outperform humans. Art is one domain in which advances in AI have recently caused lines over authorship to become blurred. Coeckelbergh (2017) argues that AI generated products can be considered “art” by both objective and subjective criteria. [1] In light of this point, the question “Can AI create art?” should be differentiated from the question “Can AI create art that is good and worthy?” (Qfiasco, 2018). [2] Taking this question as a point of departure, this study asks whether artwork created by AI are evaluated equally to artwork created by human artists and if so, how knowledge of the artist’s identity (AI or human) affect participants’ evaluation of the artwork. This study approaches these questions using Schema theory and the theory of Computers Are Social Actors (CASA) in order to consider how previously held biases and social norms might affect peoples’ evaluation of AI created artwork. There already exists substantial discourse from the technical perspective that discusses creative artificial intelligence (Eppe et al., 2018; Walther, 1994). [3][4] However, research considering AI created artwork often fails to bring in nuanced, humanistic perspectives. This is a shortcoming because measuring aesthetic value requires taking into consideration multiple factors, including stimulus, personality, and situation. The aesthetic of AI created art can be better understood if these aspects are considered (Jacobson, 2006), rather than merely focusing on the technical competence of an AI artist. [5] 76 Therefore, this study adopted scales used in the art world in order to better capture peoples’ perception of AI art. This study used a 2x2 survey-experiment design (real vs attributed identity of artists, human vs AI created artwork) to examine participants’ attitudes toward AI artwork. Participants (n=288) were recruited using Amazon Mechanical Turk (MTurk). First, four groups were formed based on the real identities of artists (AI vs. Human) and attributed identities of artists (AI vs. Human). Then, participants were randomly placed into one of four groups, which were A) AI artist (actual) x AI artist (attributed), B) human artist (actual) x AI artist (attributed), C) human artist (actual) x human artist (attributed), and D) AI artist (actual) x human artist (attributed). The study employed three types (two images per type) of AI-created artworks and three types of humancreated artworks. The pieces were chosen for their similarity in composition and style. The AI-created artworks were based on several already existing AI art generators. Multiple AI generators were selected because each generator had different ways of producing images, or “styles,” even though they were all AI-based. Human-created artworks were chosen based on the rough similarity of style or theme with each AI-created artwork. Six images of artwork (either AI-created or human-created) were shown to participants. Participants were given either the actual identity of the artists or an attributed identity. Screening measures were undertaken to ensure that participants were unaware of the purpose of the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence. Joo-Wha Hong, Nathaniel Ming Curran study and lacked familiarity with the stimulus material. All participants were asked to evaluate the artwork on the same set of dependent variables, which were adopted from those used among art studios and which consist of criteria related to originality, the degree of improvement or growth, composition, development of personal style, experimentation or risk-taking, expression, successful communication of idea, and aesthetic value (Sabol, 2006). [6] Results from the survey-experiment revealed preliminary significant differences in evaluation between human-created artworks (M= 3.18, SD = 0.56) and AI-created artworks (M = 3.13, SD = 0.64); p = .065, and it is possible to infer that such differences are due to human-created artworks receiving significantly higher ratings in “composition (AI artists: 3.34 ± 0.65, human artists: 3.63 ± 0.72); p < .000,” “degree of expression (AI artists: 3.22 ± 0.70, human artists: 3.41 ± 0.66); p = .02,” and “aesthetic value (AI artists: 3.16 ± 0.61, human artists: 3.34 ± 0.63); p = .02.” Interestingly, acknowledging the identity of the artist, either AI or human, did not influence the overall evaluation of artworks (p = .569), except “development of personal style (AI artists: 3.19 ± 0.69, human artists: 3.35 ± 0.67); p = .04.” However, participants who agreed with the statement “AI cannot produce art” gave significantly lower ratings (M=2.81, SD=0.59) compared to people who disagreed (M=3.26, SD=0.61); p < .000. An independent-samples ttest was conducted to examine the influence of the perceptions toward AI created art on the evaluation of AI created artworks, and this also yielded statistical significance. The results of this survey-experiment shed light on the ways that people evaluate AI and human artwork, including the degree of skill and creativity they assign to each. Such evaluation has implications not only for the way that society views AI created creative projects, but also for the ways that society defines concepts like creativity and art more broadly. This study contributes to understanding of public perceptions of AI in a novel circumstance, that of the art. References 1. Mark Coeckelbergh. “Can Machines Create Art?” Philosophy & Technology 30, no. 3 (2016): 285–303. 2. Flash Qfiasco, Garry Kasparov and Mig Greengard, Deep Thinking, Where Machine Intelligence Ends and Human Creativity Begins, (John Murray, London 218), Book Review. Artificial Intelligence. Elsevier B.V., n.d. 3. Manfred Eppe, Ewen Maclean, Roberto Confalonieri, Oliver Kutz, Marco Schorlemmer, Enric Plaza, and Kai-Uwe Kühnberger. “A computational framework for conceptual blending.” Artificial Intelligence 256 (2018): 105–129. 4. Christoph Walther. “On proving the termination of algorithms by machine.” Artificial Intelligence 71, no. 1 (1994): 101–157. 5. Thomas Jacobsen. “Bridging the Arts and Sciences: A Framework for the Psychology of Aesthetics.” Leonardo 39, no. 2 (2006): 155– 162. 6. Robert F. Sabol. “Identifying Exemplary Criteria to Evaluate Studio Products in Art Education.” Art Education 59, no. 6 (2006): 6– 11. Biographies Joo-Wha Hong is a PhD student at the Annenberg School for Communication and Journalism at the University of Southern California. His research interests have included the cognitive and psychological attributes in Human-computer interaction, particularly artificial intelligence. Nathaniel Ming Curran is a PhD student at the Annenberg School for Communication and Journalism at the University of Southern California. His research interests have included the intersection of education, identity, and the English language in South Korea. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 77 Introducing Machine Learning in the Creative Communities: A Case Study Workshop Matteo Loglio Serena Cangiano oio.studio matteo@oio.studio SUPSI Maind serena.cangiano@supsi.ch Abstract Recent developments in machine learning have made it one of the most popular fields of computer science of the last few years. Mostly adopted by engineers and data scientists, it recently started to open up to the creative community. This paper presents the journey through an experimental workshop, where a group of designers and artists explored new ways of using machine learning as a tool for creative projects, outside of the purely technological domain. workshop was to develop a creative brief, where participants with different levels of technical and creative abilities could join forces and participate in a collaborative project. Machine Learning for Creatives: a workshop at MuDA Zurich In July 2018, the Master of Advanced Studies in Interaction Design, in collaboration with MuDA Museum of Digital Arts, promoted the organization of a three-day project-based workshop on machine learning. Under the direction of Matteo Loglio, tutor and teacher of the course, the workshop aimed to experiment with the opportunity to involve a larger community of creators, from artists to designers and amateurs, and to validate the interest of the creative community in such topic. The fundamental idea of the workshop was to provide simple tools that could enable everyone, even people with basic technical skills, to include machine learning in a creative process and to open up this technology to unpredictable users and applications. If the conceptual aspect of the workshop was mainly theoretical, the hands-on part was riskier. The available prototyping tools are still in their infancy, as was learned from other experiments held by the authors of this paper. [1] There was a high chance that participants would struggle with the practice. To facilitate this process, part of the 78 Authentications: The Brief The focus of the creative brief assigned to the participants was to re-imagine the authentication process, using machine learning prototyping tools. The emphasis of the task was on the obsolescence of the password as an interface, and how it could be replaced with more modern solutions. After several decades, while the rest of our digital rituals evolved, in the domain of the interface, the password still remains unchallenged. Machine learning seemed like the perfect candidate for the brief: the most popular applications are in fact centered on the recognition of unique features and patterns. Passwords are just one of many examples of how this technology could radically transform not only computer science, but also the interaction design practice. For this reason, the brief challenge focused on the following question: what if we could design alternative ways to authenticate users, using modern hardware and software, like machine learning? The results The workshop participants collaborated in groups on the development of eleven functioning prototypes of authentication applications using machine learning. In the project “Divided We Fall”, for example, passwords are re-designed to be shared across communities, or groups of people. In order to unlock the screen, users have to combine their bodies in a secret combination of postures. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Introducing Machine Learning in the Creative Communities: A Case Study Workshop. Matteo Loglio, Serena Cangiano When the user is not authenticated, the application displays an incomplete message that becomes readable only when more participants join the scene. Fig 1. Divided We Fall, 2018, Emanuele Bonetti, Ruggero Castagnola, ml5js with Posenet, photo Matteo Loglio. Fig 2. Drake Gate, 2018, Sam Seemann, Ivan Iovine, ml5js with Wekinator, photo Matteo Loglio. In the “Drake Gate” project, the authentication system unlocks the computer only when the user performs a specific sequence of moves of the famous hip-hop singer, Drake. Fig 3. Glyphword, 2018, Davide Pedone, Matteo Sacchi, ml5.js library with the Feature Regression Extraction model, photo Matteo Loglio. Also worth mentioning is the project “Glyphword”, where the digital password is replaced by a physical one, in this case a custom printed token. To be granted access, the user has to perform a specific rotation of the physical key in front of the camera, mimicking the actual keylock interaction. Lesson learned The conceptual challenge of the workshop was to find a balance in explaining just enough concepts to make the subject interesting and understandable, but also to avoid technicalities. Furthermore, we learned how accessible machine learning software is still in the early days. [2][3][4] The workshop enabled participants to understand both the processes and technical constraints behind the opening of machine learning knowledge to designers and artists through a project-based learning journey. References 1. Matteo Loglio, Cangiano Serena, Massimo Banzi, Reports From a Machine Learning Workshop for Designers, online proceedings of IXDA Interaction Design Association Education Summit, Lyon 3-4 February 2018, available at, https://medium.com/ixda/reportsfrom-a-machine-learning-workshop-fordesigners-ce2621d5ba0c, accessed 24 August 2018. 2. Pj5, www.p5js.org. 3. Posenet, www.github.com/tensorflow/tfjsmodels/tree/master/posenet. 4. Wekinator, www.wekinator.org. Biographies Matteo Loglio is a designer and creative technologist and director of oio.studio. He cofounded the ed-tech startup Primo Toys and his work was exhibited at the MoMA NY the MIT and the V&A. Serena Cangiano is an interaction designer and researcher at SUPSI Lugano and coordinator of the Master in Interaction design and programs on tech education for designers. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 79 Storytelling for Virtual Reality Film: Structure, Genre, Immersive and Interactive Narrative Dr. Chan Ka Lok Sobel Senior Lecturer, Academy of Film Hong Kong Baptist University sobelc@hkbu.edu.hk Abstract Storytelling has one of the longest histories of human art and culture. Artists, painters, writers all tell their unique story by the means of different art forms and medium. Nowadays, people explore the form of virtual reality film in the fields of education, experimental cinema, porn, property sale, etc. However, what kind of story is the most suitably told by this immersive, 360-degree art form? This question is seldom discussed and studied before. [1][2] In aesthetic understanding, we know that creative content and aesthetic form cannot be separated. Immersion and interactivity, and vision and body movement following the direction of Dolby surround sound are the necessary conditions of what VR films have. We need to explore the unlimited possibility of what stories can be told by it perfectly and it may challenge our conventional concept and form of what a story should be like. To answer this essential question, I undertook extensive research, viewing lots of archive, updating information, conducting interviews, researching case studies with both pioneers, innovators and VR filmmakers in many locations, including the Virtual Reality Village at Bucheon International Fantastic Film Festival, the Australia Centre of the Moving Image (ACMI) in Federation Square, Melbourne, and Hong Kong Baptist University. I tried to compare, digest and combine the pros and cons of Virtual Reality film from East and West cultures and the moving image aesthetics and form of Virtual Reality. In this research, the possible bridge between the emerging world of VR technology 80 and the traditional art form of classical storytelling will be examined from the angle of structure, generic convention, the transformative function of story told in virtual immersive, and the interactive spaces of the medium. I will try to elucidate a paradigmatic concept and framework of what Virtual Reality Story may be for future implication and application. It may be quite different from the canonized traditional story from our long-rooted cinema history. References Bucher. Storytelling for Virtual Reality: Methods and Principles for Crafting Immersive Narratives (New York: Focal Press Book, 2017). 2. Jason Jerald. The VR-Book: Human Centered Design for Virtual Reality (The Association for Computing Machinery and Morgan & Claypool Publisher, 2016). 1. John Biography Chan Ka Lok Sobel is a senior lecturer and script thesis supervisor of Master of Fine Arts in Film, Television and Digital Media at the Academy of Film, Hong Kong Baptist University (HKBU). He is also the university honorary scholar of SCE, HKBU and Senior Fellow, Higher Education Academy, UK (in the nomination by HKBU). He received his Ph.D. in Cinema Studies from HKBU. His teaching and research interests primarily include Chinese-language films (Mainland, Taiwan, Macau and Hong Kong), screenplay, film directing, cinema therapy, He is the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Storytelling for Virtual Reality Film: Structure, Genre, Immersive and Interactive Narrative. Ka Lok Sobel Chan author of the books including: How to Write a Film Comment; Scriptwriting Handbook; Studies on Hong Kong Film, TV, and New Media; Politics on Hong Kong Films; The 97 Handover and Identity in Hong Kong Films, and Hong Kong Cinema: Nostalgia and Ideology, etc. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 81 Generation of a Multi-pictorial Script Haytham Nawar Assistant Professor of Design and Director of the Graphic Design Program The American University in Cairo haytham.nawar@aucegypt.edu Abstract The ability to express our thoughts is a very powerful tool in our society. Being able to write is more difficult than being able to read, and this applies specifically to alphabetical languages/scripts. From a personal experience, being able to write in Latin/Arabic/Chinese is a lot more difficult than just being able to read them and requires a greater understanding of the language. We now have machines that can help us accurately classify images and read handwritten characters. However, for machines to gain a deeper understanding of the content they are processing, they will also need to be able to generate such content. The next natural step is to have machines draw simple pictures of what they are processing, and develop an ability to express themselves. Seeing how machines produce drawings may also provide us with some insights into their learning process. In this project/paper, a machine will be trained to learn pictographic scripts by exposing it to a database of selected ancient and modern pictographic scripts. The machine learns by trying to form invariant patterns of the shapes and strokes that it sees, rather than recording exactly what it sees into memory. This is a simulation of how our brains operate. Afterwards, using its neural connections, the machine would attempt to write something out, stroke-by-stroke. A technique that could be applied and used on different platforms, opening the door for a language or means of communication for the future. Generated Pictographic Language In the light of the concept of machine learning, the prospect of generating a novel language becomes a certain scenario. Relying on pattern 82 recognition and the theory that computers can learn by merely being exposed to data, without the necessity of being programmed to perform specific tasks, machines can indeed offer mankind a newly developed language (writing system) that is conceived from its processed language(s). After becoming exposed to a set of characters and/or symbols, a machine becomes capable of independently adapting, learning from acquired computations to produce reliable, repeatable results on a very large scale as it weaves the similarities amongst the data it has been exposed to. Project Concept The pictography existing in all early scripts of mankind is a crucial cornerstone in the theoretical argument of universal iconography common to all writing systems. The fact that all independently derived writing systems came to be as arrangements of pictograms before their evolution into sophisticated forms, serves as evidence of the significant iconographic nature of the very notion of writing. In light of what has been raised and examined above, the aim of my project revolves around the basic idea of introducing a designed pictographic generative language utilizing machine learning. The machine would be exposed to a database of vector-based ancient pictographic scripts, ranging from Sumerian Cuneiforms, Egyptian hieroglyphs, Dongba and Nsibidi symbols, Agean script, to Chinese characters. By forming consistent patterns of the shapes and strokes it processes, the machine utilizes its neural connections in attempting to Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Generation of a Multi-pictorial Script. Haytham Nawar produce new pictographic characters, stroke by stroke, onto a digital screen. Ultimately, by recognizing and grouping similar patterns and pinpointing the similarities amongst these scripts in relation to style of strokes, complexity of figures, and proportions, the machine becomes capable of generating a firsthand pictographic language reflecting the homogenous characteristics of each of the writing systems, combined. Writing systems created civilizations; hence, the final result produced would serve as a unique investigation of the existing, yet unconsciously neglected, relations among the diverse cultures of many civilizations. Fig 2. Hieroglyphic Script characters. References 1. Ethem Alpaydin, Machine Learning: The New AI. MIT Press, 2016. 2. “A Book from the Sky 天书 Exploring the Latent Space of Chinese Handwriting.” A Book from the Sky 天书 , Genekogan, genekogan.com/works/a-book-from-the-sky/. 3. L. Bloomfield, Language. (University of Chicago Press: Chicago, 1958). 4. W. Chafe, Meaning and the Structure of Language (University of Chicago Press: Chicago, 1970). Fig 1. Cuneiform Script characters. 5. “大トロ Ml ・ Design.” Recurrent Net Dreams Up Fake Chinese Characters in Vector Format with TensorFlow | 大トロ, Studio Otoro, 28 Dec. 2015. blog.otoro.net/2015/12/28/recurrent-netdreams-up-fake-chinese-characters-in-vectorfor- mat-with-tensorflow/. 6. Golan Levin, et al. “Alphabet Synthesis Machine - Interactive Art by Golan Levin and Collaborators,” Golan Levin and Collaborators, 2001, flong.com/projects/alphabet/. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 83 Part II. Scholarly Abstracts 7. Bing Xu, et al. Tianshu: Passages in the Making of a Book (Bernard Quaritch Ltd., 2009). Biography Nawar is an artist, designer, and researcher who currently lives and works in Cairo. He is Assistant Professor and Director of the Graphic Design program, Department of the Arts at the American University in Cairo. He is the founder and Artistic Director of the Cairotronica, Cairo Electronic, and New Media Arts Festival. Nawar received his Ph.D. from the Planetary Collegium, Center for Advanced Inquiry in Integrative Arts, School of Art and Media University of Plymouth. He is a Fulbright alumni. Since 1999, he has participated in several international exhibitions, biennales, and triennials, the latest of which was Venice Biennial in 2015. Nawar won awards and acquisitions nationally and internationally in Algeria, Bosnia and Herzegovina, China, Cyprus, France, the US, among many others. 84 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Speculation and Acceleration: Financialization, Art & The Blockchain Ashley Lee Wong School of Creative Media, City University of Hong Kong ashley.lee.wong@my.cityu.edu.hk Abstract This paper looks at the financialization of art and the economy through a discussion on how art functions as a financial instrument and gains value by its circulations as images, digital objects and information. [1] These economies of original and scarce artworks and the ubiquitous symbolic value of art as information is also underpinned by the new blockchain models presented by start-ups looking to ‘disrupt’ the art market. Artists are also exploring finance as a medium in their work as a reflection on their own economies and practices. Through an analysis of artist projects and start-ups, this paper explores the new models and possibilities of blockchain for the economies of art. By relating blockchain to cybernetics as new frontier in techno-capitalism, we can analyze the hype around ‘Web 3.0’ as another libertarian dream. [2] Despite the ideal of decentralization (the ideal of enabling peer-topeer transactions) there are risks of greater centralization with the blockchain just as we have seen with the internet. [3] All the while China has placed bans on new ICOs (Initial Coin Offerings) in an attempt by the government to retain control of development of the technology which they are rapidly spearheading. [4] While Hong Kong remains a global financial center, the logic of finance and the accelerated developments of new technologies becomes one that directs the economy at scales beyond our individual capacity. There is a need for artists to engage within these economies to open up to new possibilities rather than having the future be determined by a technological or financial elite. We will look at several start-ups applying blockchain technologies to the art market including Ascribe, Verisart, Monegraph, Maecenas. These companies are creating models for the authentication, verification and financialization of works of art. These models present a means to support artists, but also a distributed model of ownership for collectors, though often following traditional logics of the art market. Blockchain enables people to create their own currencies and tokens that are defined by protocols that can enable decentralized governance and transparency. When considering blockchain for art it allows for an immutable universal ledger making transactions records visible that contrasts with the opacity of the art market in general. This is of particular interest for digital works of art and digital assets, which are difficult to track and remunerate for artists. It could enable a system for artists to be remunerated for their work as it circulates online. Among the most successful applications of blockchain in the digital creative field is Cryptokitties, a platform for the creation and trade of generative images of kitties as digital ‘art’ objects. In a gamefied experience, this example can be considered a playful application of the technology with entertaining results. Cryptokitties is an example of the applications for the trade of art as digital objects. Whereas other emerging alt-coins that parody Bitcoin generate value as memes like Doge Coin and Pepe Coin, which is more a reflection of the populism of online cultures. Paolo Cirio in his Art Commodities project critically imagines an economy where socially engaged projects gain value as they circulate Proceedings of Art Machines: International Symposium on Computational Media Art 2019 85 Part II. Scholarly Abstracts and where works are offered at an accessible price, inverting the logics of the art market and encouraging participation through a low barrier to entry. [5] Other artists like Brad Troemel, Sarah Meyohas, Andy Bauch, Jonas Lund, and Ed Fornieles are exploring the implications and potential of blockchain for the financialization of their own artwork, as well as providing new perspectives on the developments of blockchain technologies in the broader society.[6] [7] Groups like the Economic Space Agency formed of radical economists, finance theorists, and computer scientists aim to create open source tools for creating one’s own economy to “provide an open yet safe platform for the interoperability of heterogeneous value and risk systems and the scalability of tokenbased economies to create new social, economic and financial relations.” [8] They present an alternative and altruistic vision for the potentials of blockchain through practical tools for development. As the hype around blockchain remains dominated by techno-utopians and financial speculators, there are artists seeking to accelerate the art market, while others search for alternative models and fairer practices. There is risk of further centralization and control by a largely Western male technological elite; however, experimentation and speculative imaginaries of artists opens to new perspectives and visions that may not stop rapid technological innovations, but may influence intensities towards another future. References 1. McKenzie Wark, “My Collectible Ass”, eflux Journal, Issue 85, 2017, accessed October 14, 2018, http://www.e-flux.com/journal/85/ 156418/my-collectible-ass/. 2. Tiqqun, “The Cybernetic Hypothesis” Tiqqun 2 (2001): 40-53. 3. Duncan MacDonald-Korth, Vili Lehdonvirta, Eric T. Meyer, “Art Market 2.0: Blockchain and Financialisation in Visual Arts,” The Alan Turing Institute, (2018). https://www.oii.ox.ac.uk/publications/blockcha in-arts.pdf. 4. Orange Wang, “Welcome to China’s wild, wild world of blockchain investment,” South 86 China Morning Post, April, 30 2018. accessed 14 October, 2018. https://www.scmp.com /tech/china-tech/article/2144007/welcomechinas-wild-wild-world-blockchain-investment. 5. Paolo Cirio, Art Commodities (2014), Paolo Cirio website, accessed October 14, 2018. https://paolocirio.net/work/art-commodities/. 6. Ben Luke, “Artists as cryptofinanciers: welcome to the blockchain,” The Art Newspaper, June 13, 2018, accessed October 14, 2018. https://www.theartnewspaper.com /feature/artists-as-cryptofinanciers-welcome-tothe-blockchain. 7. Ruth Catlow, Marc Garrett, Nathan Jones, and Sam Skinnerm, Artists Re:thinking the Blockchain (Liverpool: Liverpool University Press, 2017). 8. Erik Bordeleau, “Re-engineering finance as an expressive medium”, Economic Spacing, Medium, August 10, 2017, accessed October 14, 2018, https://medium.com/economicspacing/re-engineering-finance-as-anexpressive-medium-221e09d7042e. Biography Ashley Lee Wong is a curator and researcher based in Hong Kong and London. She is a PhD Candidate at the School of Creative Media, City University of Hong Kong. She completed an MA in Culture Industry at Goldsmiths University of London and a BFA in Computation Arts from Concordia University, Montreal. She is Artistic Director of the digital studio, MetaObjects, which facilitates projects with artists and cultural partners. She worked as Head of Programmes for Sedition, an online platform for the distribution of digital limited editions in London. She has presented in international conferences including Research Values, PhD Workshop, Transmediale Festival, Berlin, 2018, Art With or Without the Art Market Symposium, Institut national d'histoire de l'art, Paris, 2018; and Media Art and the Art Market Symposium II, Ars Electronica, Linz, 2017. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Aesthetic Coding: Exploring Computational Culture Beyond Creative Coding Winnie Soon Shelly Knotts Aarhus University wsoon@cc.au.dk Durham University michelle.knotts@durham.ac.uk Abstract Learning to code has started to be part of the core strategy in educational curriculum, from primary school to higher education, especially in many developed countries that promote stem education, or at least coding is recognized as an important aspect of science and technology development. [1][2][3][4][5] In the art and design-related disciplines, creative coding emphasizes code as an expressive material and embraces exploration and experimentation of code beyond functional applications. [3][6][7][8]. OpenFrameworks, Sonic Pi, p5.js Processing and ml5.js are some examples of open source platforms that facilitate creative and expressive creation through sharing and remixing code. In other words, the community of creative coding expands the usual way of learning to code beyond science and engineering disciplines. However, with the increasing demand of computational practices in emerging disciplines such as software studies, platform studies, new media studies and digital humanities, coding is increasingly considered as “literacy” to humanities. [9] This perspective of coding literacy becomes a critical tool to understand the history, culture and society alongside its technical level, especially since our digital experiences are ever more programmed, both technically and culturally. This presentation introduces two cases where two artist-coders consider code practice as a mode of aesthetic and critical inquiry, and they teach coding (in a format of workshop delivery) in a critical way through engaging with their artistic and coding practice. This aesthetic approach includes not only introducing coding practically and creatively but also cultivating an open space where discussing and reflecting on computational culture is possible. This is similar to what scholar Michael Mates describes as ‘procedural literacy’, which is to connect social and cultural issues with coding through theoretical and aesthetic considerations. In particular, how “the culturally-embedded practices of human meaning-making and technically-mediated processes” are intertwined. [10] By introducing two different hands-on code learning workshops, this presentation examines how aesthetic production or critical thinking can be cultivated and developed through learning to code. We suggest connecting code with social and cultural issues through performing, showcasing and discussing code-related art and performance as a departure point to develop code or procedural literacy. Without losing sight of exploring code technically and creatively, the two hands-on workshops illustrate how the suggested aesthetic coding approach could be realized in both epistemic and practical levels. The first workshop was conducted in 2017 titled ‘Feminist coding in p5.js | Can Software be Feminist?’ by Winnie Soon, and the second case was conducted in 2016 titled “Rewriting the Hack” by a live coder Shelly Knotts and curator Suzy O’Hara. [11][12] We argue that the practice of aesthetic coding provides epistemic insights to explore computational culture beyond creative coding, shedding light on how to work with code across disciplines and to consider coding practice as a means to think critically, aesthetically and computationally. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 87 Part II. Scholarly Abstracts References 1. Xie Yu, Michael Fang and Kimberlee Shauman, “STEM Education,” Annual Review of Sociology 41, (2015): 331–357. 2. Miros aw Brzozowy et al. “Making STEM Education attractive for young people by presenting key scientific challenges and their impact on our life and career perspectives (paper based on a talk presented at 11th annual International Technology, Education and Development Conference, Valencia, March, 2017),” INTED2017 Proceedings, https:// library.iated. org/view/BRZOZOWY2017 MAK. 3. Bryan Chung, Lam Pong and Winnie Soon, “Computer Programming Education and Creative Arts,” (paper based on a talk presented at ISEA, Hong Kong, 2016) ISEA2016 Conference Proceedings. 4. Stuart Heaver, “STEM education key to Hong Kong’s ‘smart city’ plan, but long-terms steps must be taken now, experts warn (2017),” South China Morning Post, accessed August 31, 2018. https://www.scmp.com/lifestyle/ article /2124487/stem-education-key-hong-kongssmart-city-plan-long-term-steps-must-be. 5. Meng Jing,, “China wants to bring artificial intelligence to its classrooms to boost its education system (2017)”, South China Morning Post, accessed August 31, 2018. https://www. scmp.com/tech/science-research /article/ 2115271 /china-wants-bring-artificialintelligence-its-classrooms-boost. 6. Winnie Soon,, “Executing Liveness: An Examination of the live dimension of code interactions in software (art) practice,” (Ph.D. diss., Aarhus University, 2016). 7. John Maeda, Creative Code: Aesthetics + Computation (London: Thames & Hudson, 2004). 8. Kylie Peppler and Yasmin Kafai, “Creative coding: Programming for personal expression,” The 8th International Conference on Computer Supported Collaborative Learning 2 (2009): 7678. 9. Annette Vee, Coding Literacy: How Computer Programming Is Changing Writing (Cambridge, MA: MIT Press, 2017). 10. Michael Mateas, “Procedural Literacy: Educating the New Media Practitioner,” The 88 Horizon. Special Issue. Future of Games, Simulations and Interactive Media in Learning Contexts 13, no. 1 (2005). 11. Winnie Soon, “A Report on the Feminist Coding Workshop in p5.js.” Aesthetic Programming website 2017, accessed August 31, 2018. http://aestheticprogramming.siusoon. net/category/thoughts/. 12. Shelly Knotts and Suzy O’Hara, “Rewriting the Hack (2015),” accessed August 31. 2018. http://rewritingthehack.github.io/index.html. Biographies Winnie Soon is an artist-researcher, exploring themes around digital culture. Her current research focuses on the culture of code practice, working on two books titled Aesthetic Programming: A Handbook of Software Studies, or Software Studies for Dummies (with Geoff Cox) and Fix My Code (with Cornelia Sollfrank). She is Assistant Professor at Aarhus University. More info: http://www.siusoon.net. Shelly Knotts produces live coded and network technology facilitated music projects. She presents her artistic work internationally and has attended several residencies, think tanks, seminars and workshops including a number of hack events. She was recently Performance Chair for the 1st International Conference of Live Coding and has worked on several projects developing communities in technology focused music making including Network Music Festival and SOUNDKitchen. More info: https://datamusician.net/ Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Distributed Cognition in Ecological/Digital Art Scott Rettberg University of Bergen scott.rettberg@uib.no Abstract This essay will consider ecologies of distributed cognition, as represented in a number of recent works of digital art and electronic literature, which themselves reflect upon contemporary environmental crises. The investigation will be framed by the work of theorists including N. Katherine Hayles, Bernard Stiegler, and Timothy Morton in considering ideas of assemblages of cognition distributed between humans, non-human lifeforms, and machines, exteriorized and unthought memory, and environmental hyperobjects. The essay will consider how these concepts can be read through installation artworks and works of digital literature by authors and artists including Phillipe Parreno, Rafael Lorrezo-Hamar, Kobie Nel, Scott Rettberg, Roderick Coover, Johannes Heldén and Håkon Jonson, and David Jhave Johnston. How are digital artworks helping us to think through ecologies of distributed cognition during the contemporary period of planetary crisis in which they operate? Assemblages of Distributed Cognition In her Unthought N. Katherine Hayles articulates a relationship between human and non-human cognition that is distributed between three types of actors: human beings engaged in the types of cognitive activity we typically characterize as “thought,” nonhuman life forms (from whales to microorganisms to plants) that also clearly engage in acts of individual and distributed cognition, and AI and other forms of machine cognition. She argues that it no longer makes sense to consider human thought as a process that occurs in isolation from the cognitive processes of these other cognizers with whom humans co-evolve in various forms of symbiotic and sometimes agonistic relation. Human semiotics must encounter biosemiotics and cyber-semiotics. Hayles describes the position of homo sapiens within this network of cognitive associations as “open to and curious about the interpretative capacities of non-human others, including non-biological life-forms and technical systems; she respects and interacts with material forces, recognizing them as the foundation from which life springs; most of all, she wants to use her capabilities, conscious and unconscious, to preserve, enhance, and evolve the planetary ecology as it continues to transform, grow, and flourish.” [1] This essay will, in part, consider how particular art installations and works of electronic literature represent these cognitive assemblages, which are spread across human and non-human actors. An Immersive Ecology of Cognition Phillipe Parreno’s “Immersion—Exhibition 4,” exhibited at the Gropius-Bau in Berlin during the summer of 2018 is an assemblage of different elements which could be discussed as discrete objects and events but are better understood as a collective whole, an immersive ecology. As I entered the imposing open atrium space of the Gropius-Bau, I felt a strange sense of entering another world with uncanny rhythms of its own. A large rectangular recessed reflecting pool was laid out directly in front of the entrance. The room was quite still aside from some distant music from off in alcoves all around the central space. In the pool at occasional intervals, barely perceptible bursts of water plopped up from beneath, creating reverberating circles in Proceedings of Art Machines: International Symposium on Computational Media Art 2019 89 Part II. Scholarly Abstracts the water. On the other side of the pool, a large sculptural cluster of triangular sofa sections rotated slowly on a circular turntable before two black steel grids. After a few moments I heard a sudden surge of raw voltage. The grids lit with electricity, and as they charged, an image seemed to flash briefly in arcing bolts of light. As I settled onto the rotating furniture and watched the grids as they charged up again, I saw that this was indeed a kind of picture, imprinted as a retinal afterimage when I closed my eyes: an electric insect, a flickering dragonfly. Throughout the rooms of the exhibition, strange events occurred, organized by some not-immediately-apparent logic. In one room, dozens of polystyrene fish balloons floated in one room, driven by small fans that created shifting air currents. In two other rooms, player pianos occasionally sounded notes. In several of the rooms, automated window shades moved up and down of their own accord. I encountered a small laboratory enclosed in a plexiglass case in another room, including beakers, scientific measurement equipment, and computers. The exhibition brochure described this as a bioreactor “in which micro-organisms multiply, mutate, and adapt to their environment.” Monitored and transcoded, the yeast cultures in the beakers are connected to computers and are in fact the engine “orchestrating the contingent events” elsewhere in the exhibition. The documentation claims that over time “these yeast cultures develop a memory—a collective intelligence—that learns the changing rhythms of the show and evolves to anticipate future variations.” [2] Parreno describes the micro-organisms’ interactions with each other and with the conditions of their environment as “neural circuitry” that “sets a complex non-deterministic, non-linear mise-en- scène in motion through a series of non-periodic cycles.” [2] Parreno’s exhibition is one example of an artwork that effectively communicates the type of cognitive assemblage that Hayles’s theory describes. In the essay I will consider how the sensations that experiences of such an interaction with 90 artistic embodiments of distributed cognition represented by this and other artworks provide may help us to situate our ecological interaction with other cognizers in our lived experience of everyday life. Hyperobjects Timothy Morton describes hyperobjects as things that are “massively distributed in space and time in relation to humans.” According to Morton a hyperobject “could be the very long- lasting product of direct human manufacture, such as styrofoam or plastic bags, or the sum of all the whirring machinery of capitalism. Hyperobjects, then, are ‘hyper’ in relation to some other entity, whether they are directly manufactured by humans or not.” [3] Hyperobjects pose problems of comprehension for human actors. We cannot see climate change as one entity. We cannot plan effectively in terms of the lifespan of uranium. Reading the concept of hyperobjects through a number of digital artworks and works of electronic literature, I will further situate ecologies of distributed cognition within an environmental crisis that is also a crisis of human comprehension of our situation in the Anthropocene epoch. References 1. Katherine Hayles, Unthought: The Power of the Cognitive Unconscious (Chicago and London: The U of Chicago P), 40. 2. Phillipe Pareno, Brochure for GropiusBau exhibition (Berlin: Gropius Bau, 2018). 3. Timothy Morton, Hyperobjects: Philosophy and Ecology After the End of the World (Minneapolis: U of Minnesota P, 2013), 224. Biography Scott Rettberg is Professor of Digital Culture in the Department of Linguistic, Literary, and Aesthetic Studies at the University of Bergen, Norway. He is the author or coauthor of numerous works of electronic literature, combinatory poetry, and films including The Unknown, Kind of Blue, Implementation, Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Distributed Cognition in Ecological / Digital Art. Scott Rettberg Frequency, The Catastrophe Trilogy, Three Rails Live, Toxi•City, Hearts and Minds: The Interrogations Project and others. His work has been exhibited online and at art venues such as the Venice Biennale, Inova Gallery, Rom 8, the Chemical Heritage Foundation Museum, Palazzo dell Arti Napoli and elsewhere. Rettberg is the author of Electronic Literature (Polity, 2018), a comprehensive study of the histories and genres of electronic literature. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 91 Playing with the Sound Wing On Tse Independent Researcher wingontse@gmail.com Abstract This paper addresses the relationship between sound and game players. Nowadays, gamers and game designers pay great attention to storytelling and sound design, and many of them consider that sound effects and music significantly enhance their enjoyment. Collins observes that in the past, very limited sound effects and one song were available for an entire game; however, game audio has considerably improved and already reached a cinematic quality and gained some recognition. [1] A music-based game implies more fun because players can interact with one another. For example, they can make the music or sing along to the soundtrack, and even in regular games, the timing of the sound is controlled by the players. This paper explores the history of Foley and the distinction between film Foley and game Foley. Priestley writes that Foley decided to project a film onto a screen and record its effects all on one track. [2] Jorgensen further studies the influence of sound using techniques from the field of psychology, and the technique is especially useful when the sound engineer deals with virtual reality (VR). [3] It is essential for the sound engineer to understand the new technique because it will be used by different media, such as gaming, movies, and news reports. Harvey states that sound is a key tool for VR experience, and it is the Wild West because sound technologies have rapidly evolved in terms of both hardware and software, and their application in or incorporation into VR is still very much in flux. [4] However, Maori, Kanako, and Shiro argue that during their test, the participants responded both in the real world and in the virtual world with the sense of the presence of 92 the physiological responses in both nonstressful and stressful virtual environments. They reproduced the 3-D sound condition compared with the non-3-D area, and the auditory stimuli had the same sound pressure levels and frequency characteristics in both conditions. [5] Numerous studies about the sound effects and music for video games are currently available because they have increasingly become an important criterion for buyers. When gamers buy a video game, they do not only consider how good the story or creation is but also include the sensory aspects on their list, such as how interesting the sound effect is, whether the quality of the sound reaches the cinematic level or not, and whether the music matches the game scenario. They consider numerous variables. Hence, VR has become more popular because players can have all the elements that I have outlined, and the 3D audio has already left a huge space for sound design and dialogue. To quote Scott Gershin, a Technicolor expert who also presented some advanced audio techniques during their “Beyond 360” session: “Audio is going to give you that style. . . It’s going to give you information as to where you are.” [4] References 1. Karen Collins, Game Sound: An Introduction to the History, Theory, and Practice of Video Game Music and Sound Design (Cambridge, Mass.: MIT Press, 2008), 111-116. 2. Jenny Priestley, “The Art of Foley,” TVB Europe (2017): 16-19. 3. Kristine Jorgensen, Comprehensive Study of Sound in Computer Games (New York: The Edwin Mellen Press, 2009), 82. 4. Steve Harvey, “GameSoundCon Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Playing with the Sound. Wing On Tse Ponders Realities of VR,” Pro Sound News 38, no.11 (2016): 28-30. 5. Maori Kobayashi, Kanako Ueno, and Shiro Ise, “The Effects of Spatialized Sounds on the Sense of Presence in Auditory Virtual Environments: A Psychological and Physiological Study,” Presence: Teleoperators & Virtual Environments 24, no. 2, (2015): 163174. Biography Wing On Tse graduated with a bachelor’s degree in broadcasting, telecommunications, and mass media at Temple University and also obtained a master’s degree in Creative Media at City University of Hong Kong. He worked at two US radio stations, iHeart Media and CBS, as a sound engineer. He is currently working at the Hong Kong Baptist University as a technical officer. He always loves and reads any subject that is related to sound. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 93 Art and Automation: The Role of the Artist in an Automated Future Lodewijk Heylen PXL-MAD School of Arts, University of Hasselt lodewijk.heylen@uhasselt.be Abstract Rapid development in automated technology is the catalyst for a paradigmatic change in society. Exponential growth of machine learning and AI applications may bring to an end the monopoly on creative production currently claimed by the arts. In this new world, the position of the artist as the producer of authentic human experience wavers. Considering various models of an automated future, this research aims to outline the possible modus operandi of the artist in changing productive environments. Neoliberalism and Automation Through the past few decades our society has grown increasingly neoliberal in its principles, foregrounding certain fundamental economic ideas — e.g. efficiency, marginal utility, computability, standardization, specialization, globalization — above others. These principles have bled into our personal, sensory understanding and the making of the world around us; as such, it is safe to speak of a dominant neoliberal hegemony, unconsciously built into our daily habits. [1] Neoliberal conceptualizations of an endless, expansive commodity market influence our views on, for instance, labor, freedom, safety, authenticity, humanity, and value. Also, they reappear and reiterate themselves in our human interactions. The purpose of this study is to focus on one of the major excesses of the neoliberal thinking: the rapidly increasing application of automation. Automation can be seen as the installation of devices, physical or virtual, that replace repetitive or regular actions. Normalization of this sort is based on conventions or statistics amassed through experience, and hinges on the predictability of the future. It is the logical 94 extension of an archaic human habit —that is, to control and anticipate the future, to augment and transcend the human condition of the unknown. Efficiency and Authenticity Yet, under influence of neoliberal thought, automation is mostly an instrument of efficiency. The quest for efficiency, in fact, drives the engine of the automatization altogether. Inefficiency is seen as the source of all problems, as something to be solved by means of ever-progressing technological advance. This constant yearning for efficiency has been largely a frustration of the markets of industry and everything that revolves around it: production, transportation, distribution, sales, stocks, information and services demand less and less loss from logistical friction. But when the world becomes the market, as in the neoliberal model — when the disruptive force of technology surpasses the threshold of commerce, and seeps into the spheres of private and community life — the agency of automation becomes more than a luxury commodity. It renders human action burdensome and ultimately redundant. Automation has become, in many aspects, the opposite of authenticity, creativity, culture, nature, and even humanity itself — the opposite of human production. The all-encompassing influence of automation will continue to have a profound impact on the fabric of society, as data-driven research presents automation services that had never before existed. Entrenched local jobs are already being replaced by robots, services are streamlined by algorithms, and traditional enterprises are made superfluous by the disruptive technological economy. Through the development of machine learning in combination with the Internet of Things, among Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Art and Automation: The Role of the Artist in an Automated Future. Lodewijk Heylen other technological advances, these automation services are bound to expand tremendously. Estimates suggest that anywhere from 47 to 80 percent of current jobs are likely to be automatable in the next two decades. [2] Certain professions are more prone to automation than others, but nothing suggest that the practice of the artist, in its current form and convention, is immune to this evolution. Still, history proves the malleability of the artistic profession: Under the influence of early industrialization, the anonymous craftsman became a creative author; 20-century advancements further transformed this craftsman into an avant-garde critic. Technological progress in material production during the modernist era billed the artist as author of the authentic. Authenticity, defined as the antithesis of automation, implies the involvement of human actions. It suggests that there is a human author, a person who has at some point made a creative decision to produce something: man must be behind the wheel. [3] Authenticity is the difference between something real and something fake; without necessarily rejecting the use of tools, machines or computers, authenticity defies mass production, standardization and reproduction. established traditions, of which the dispositions are not yet known; it is possible to imagine a future in which art may deviate once again from its present purpose. Computational learning, neural networking and other systems of data mining will have a profound impact on our perception of the authentic, not only in the field of art but far beyond. Lines will become blurred between human creation and the inauthentically recreated, between human production and the mindlessly re-produced, between imagination and the re-imagined. What will the value of creativity be if it can be automated? The goal of this artistic study is not only to discover the effects of automated machine learning emulating the labor of the artist, but to imagine what an adaptation of the artist in relation to this evolution could entail. Creativity in the Time of Machine Learning Assuming that: a) the role of the artist in society is ever adapting to new social situations, in many ways influenced by advancements of technology that currently push the profession into that of a producer of authenticity; b) the urge for authenticity originates from a reaction against the sprawls of the comprehensive generalization and globalization of everyday life, giving rise to the premise that only the human touch can create something genuine or original; and, c) the outsourcing of human action through a rapidly accelerating development of information technology and data driven automation is laying the groundwork for a shift in the general mentality towards Biography Lodewijk Heylen (Belgium, °1989) obtained his masters degree at the ENSAV La Cambre (Brussels, BEL) in the field of art in the public environment. He continued his studies at the postgraduate Institut für Kunst im Kontext at the UdK (Berlin, GER). Recently he started a PhD in the Arts at the University of Hasselt and the PXL/MAD School of Arts (Hasselt, BEL). He is also a member of the Belgian Young Academy and the founder of the artistic think tank BIN. He practices as a conceptual, contextual and independent artist in collaboration with specialists, experts, scientists and other people outside the art field. His oeuvre is built around reflections on standardization and normalization in relationship to the human urge to control, somewhere in between art, science, design, philosophy and politics. References 1. N Srnicek and A Williams, Inventing the Future. Postcapitalism and a World Without Work (2015). 2. C.B Frey and M Osborne, The Future of Employment: How Susceptible Are Jobs to Computerisation? (2013). 3. D Dutton, Authenticity in Art in The Oxford Handbook of Aesthetics (2003). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 95 Atom, Bit, Coin, Transactional Art Between Sublimation and Reification Prof Maurice Benayoun Tobias Klein ACIM Team Research Fellow ACIM Team Research Fellow School of Creative Media, City University of Hong Kong m.benayoun@cityu.edu.hk School of Creative Media, City University of Hong Kong ktobias@cityu.edu.hk Abstract A hypothesis about the origin of language and the way we conceive, understand, therefore compute and, ultimately try to control the world (and subsequently make art): 1.0 Language was the first way to convert the world of things – objects, ideas and actions - into a world of signs, “coining” the observable as well as the otherwise inconceivable large and complex. 1.1 In human history, the first attempt of absolute discretisation of the world into units able to exchange was quantification, reductio ad transactional unit: money (calculus, numbers, coins…). It helped defining equivalences, differences and transactions. Thus, converting the world into discreet units comes down to translating the world into something that our brain can understand and measure. 2.0 Subsequently, having gained the ability to quantify, measure and abstract, that is to “democritize” 1 , (from Democritus who coined “atom”) the application of the concept followed as a unifying and ordering principle for all that is then considered as made of atoms: indivisible particles that constitute the unique substratum of the world. 2.1 Naturally, the binary digit came as an extension of these observations as it is the ultimate way to convert the world into data 1 2 From Democritus who coined “atom.” κυβερνήτης (cybernḗtēs) "steersman, governor,… 96 that can be computed by both natural and artificial brains. 2.2 Datafication, describing the conversion of the whole world into data, characterizes the ultimate convergence of discretisation, quantification and language. Dataism is a form of articulated dematerialized reality, and computable immateriality. The discretised world is at the same time an alphabetisation and a grammatization of the world. 3.0 Therefore, within the described discretisation process, resides the primitive ambition of the humankind to achieve a definitive neutralisation of the ontological difference of the being by the assumption of its universal convertibility and thus not only evaluate but control the world - the cybernetic 2 fantasy of mankind, (Cybernetics from υβερνήτης (cybernḗtēs) "steersman, governor”.) Isn’t Googol3 the estimate number of particles in the universe? Working within confines of above described reality – what is art? Some would consider that the purpose of art is giving a shape to ideas. Beyond mimesis, art is more often the expression of things that usually reside in our mind: relations, forces, emotions…, elements contradicting the quantifiable discretized. Or so it seems. Started 3 3 10100, The term was coined in 1920 by 9-year-old Milton Sirotta (1911–1981), nephew of U.S. mathematician Edward Kasner.( Bialik, Carl (June 14, 2004) Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Atom, Bit, Coin, Transactional Art Between Sublimation and Reification. Maurice Benayoun, Tobias Klein years ago, Brain Factory is an art/research project investigating the consequences and opportunities of the ability of Datafication. In the context of the project - then extended to the contemporary world - we coined the construct sublimation, describing the transfer of a real-world item into the digital immaterial realm, and reciprocally the process of converting the immaterial, namely thought, via the process of reification into a state of real-world existence. The Brain factory enacts this process resulting in a series of artworks that offer the possibility to give a quantifiable shape to human abstractions. This conundrum between the artistic existential ability to confuse and corrupt the quantifiable and the machinic discretised is based on sensing, translation and computing of the brain’s activity via a Brain Computer Interface (BCI 4 ). The Brain Factory installation is comprised of two parts – Sublimation and Reification. Sublimation: In the Brain factory installation, a visitor, called Brain Worker, is seated in front of a screen, connected to a BCI device reading EEG data – basic brain activity. The factory randomly assigns an abstract human construct, such as Love, Peace or Power to the worker and displays the word on the screen to focus on. The brainworker’s brain activity, related to the assigned construct is translated into an emerging, particle driven form on the screen in front of the worker. This is called the Shape Generator. At the same time, as the form dynamically emerges from the Shape Generator, the brain assesses its evolution in real time, in an attempt to check its relevance as an expression of the specific suggested human abstraction. Reification: Once the shape is completed, it becomes possible to assign to it certain physicality. One of the direct translations of such generated shape is 3D printing. It is a way to materialise the concepts. While using a physical material, the materiality is constructed and free of associated narratives that would affect and complicate the translation of the human abstraction. Converting the “projection” into “translation.” Thus while being physical, it is not interpreted through our preconceived valuation of material origin (think of cast gold, carved wood… and their material narratives). If “sublimation” processes the world into computable Data, “reification” is the opposite action: to make the immaterial material, to convert thought into object. It corresponds to an ancestral aspiration of humankind: to control matter by thought. The Brain Factory installation is more than a station to record the brain worker’s reactions, it is an evolution engine, a conceptual ecosystem. Instead, each worker’s cogitation and its resulting shape are based on the previous worker’s labour in shaping the same human abstraction. Thus, there is a growing library of interconnected ontologies of forms and thus iterations of increased morphologic resolution of the shape. Brainfactory considers thought inspired shapes as living beings with a generative CDNA (ConceptDNA). Each shape is made of a chain of descriptors that evolve according to the natural ecosystem of thought: the human mind and the described iterations and reactions to the preceding brain workers labour resulting in a morphogenesis based on the natural selection process resulting of the series of visitors who inherit the previously defined CDNA. Ultimately, this process narrows down the shape through increase ing iteration leading to a more “universal” significance. Thus constituted shapes can be reified or simply considered as the most accurate symbolization of human abstractions: Freedom, Peace, Love, Power... Returning to the hypothesis 1.1, that quantification, reductio ad transactional unit: money (calculus, numbers, coins…) is at the base of the world’s discretisation, the shaping process is surprisingly similar to contemporary cryptocurrency’s minting process. It confers the resulting “digital object” a unique power of significance. The digital object in itself is, through its ontology, neither sublimation nor reification, yet both at the same time. In terms of quantifiable ownership of human abstraction, the brainworkers can be considered as the last in the chain of authors of the concept-made-form. The shaped abstractions are collected in a database, a distributed ledger based on the Blockchain. Each token becomes the brainworker’s own property. He or She can use the digital form to produce objects, artworks, to collect, trade or barter it. 4 BCI is using here EEG, Electro encephalography and biofeedback through visual, audio and haptic interfaces. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 97 Part II. Scholarly Abstracts In the current state of the project, we have reached the next level in the Brain factory project, creating VoV Values of Values, is a crypto currency made of shaped human values. All at once, the exhibition spectator has become, artist, producer, art dealer, and collector. The observation of the trading process produces a real time monitoring of human Values in their transactional milieu. VoV is at the same time a real currency, a critical metaphor of the art production narrative and a dynamic reflection on its founding ontology. Biographies Artist, theorist and curator, Maurice Benayoun (MoBen, 莫奔) is a pioneering and prominent figure in the field of New Media Art. MoBen’s work freely explores media boundaries; from virtual reality to large-scale public art installations, on a socio-political perspective. His work has been widely awarded (Golden Nica Ars Electronica 1998…) and exhibited in major international museums (2 solo shows at Centre Pompidou Paris), biennials and festivals in 26 different countries. Some of MoBen’s major artworks include The Tunnel under the Atlantic (VR, 1995), World Skin a Photo Safari in the Land of War (VR, 1997), the Mechanics of Emotions (20052014), and Cosmopolis (VR, 2005). Elaborating on the concept of Critical Fusion applied to art in physical or virtual public space, Maurice Benayoun initiated the Open Sky Project on the ICC Tower Hong Kong media façade. With The Brain Factory and Value of Values, he is now focusing on the morphogenesis of thought, between neuro-design and crypto currency, brain and money. With a PhD in Art and Art Sciences, MoBen taught from 1984 new media art practice and theory at Paris 1 Pantheon Sorbonne and Paris 8 University. He was Professor and artist in residence at the French National School of Fine Arts (ENSBA). Since 2012, Maurice Benayoun is full Professor at the School of Creative Media, City University of Hong Kong. rary CAD/CAM technologies with site and culturally specific design narratives, intuitive nonlinear design processes, and historical cultural references. Before joining City University Hong Kong in the role as interdisciplinary Assistant Professor in the School of Creative Media and the architectural department, he was employed at the Architectural Association (2008-2014) and the Royal College of Art, (2007-2010), teaching students at the postgraduate level. The works of his studio are exhibited international with examples being in the collection of the Antwerp Fashion Museum, the London Science Museum, the V&A, the Science Gallery (Melbourne), the container (Tokyo), the Bellevue Arts Museum, Museum of Moscow and Vancouver and in the permanent collection of China’s first 3D Print Museum in Shanghai. He lectures and publishes internationally, recently winning SIGGRAPH 2018’s Best Art Paper Award for his research on the translation from traditional to digital Craftsmanship. Tobias Klein works in the fields of Architecture, Art, Design and interactive Media Installation. His work generates a syncretism of contempo- 98 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Facial (Re) Cognition: Windows and Mirrors, and Screens Megan Olinger School of Creative Media, City University of Hong Kong Mmolinger2-c@my.cityu.edu.hk Abstract The increasing prevalence of facial recognition software in everyday life has prompted both criticism and examination of the ethical use of facial recognition as it pertains to issues of surveillance, privacy and discrimination. The use of facial recognition as a tool of socialsorting with the potential to result in discrimination, inequality, and invasion of privacy rights, is an urgent issue that numerous researchers currently address. Much of the research has focused on the lack of diversified data sets that are used to design algorithms used in facial recognition software and how the results are used in ways that discriminate and are an invasion of privacy. [1][2][3][4][5][6] While these critical claims justify further research and action, there has been little consideration of the agent(s) of recognition in facial recognition systems and how the issue of agency effects human perception of the results. Hayles argues that technical information-processing systems (such as facial recognition) function as cognizers, because they have the ability to make decisions. [7] It is important that debates about the use of facial recognition systems acknowledge algorithmic agency and its potential to enhance human perception. Developing this argument, I contextualize facial recognition as a current device in the evolution of photographic portraiture, and how it addresses the politics of the face through identification, classification and social-sorting as an assertion of power.[8] According to Szarkowski, photographs can be read and understood as either perspectives on the world or as extensions of their maker’s selfconception. [9] The addition of algorithmic agency in facial recognition systems adds a layer of complexity to the traditional photographer– subject–viewer relationship that is referred to by Szarkowski. By acknowledging the cognitive agency of algorithmic intelligence, we must consider who or what is doing the recognition? Who or what is generating the portrait? Do the outputs determined by facial recognition function as a window and/or mirror, and in either case who or what is being revealed or reflected – a human perspective, an artificial intelligence perspective, or an assemblage of both? I shall examine these questions through the philosophical lens of Deleuze and Guattari, who assert that “the face is a politics,” and through their theory of becoming, which argues for the idea of seeing with greater openness and the expansion of perception beyond the human being as the origin of perception. [11] I argue that algorithmic re-cognition is generative, not representational. Therefore, we must consider the portrait generated through facial recognition as an algorithmic re-cognition of the subject, portrayed in a constant state of becoming. I propose that the challenge presented to humans is to perceive the state of becoming offered by algorithmic production and allow it the potential to enhance human perception. References 1. Joy Buolamwini, “InCoding – In the Beginning – MIT Media Lab – Medium.” Medium, May 16, 2016. https://medium.com/mit-media-lab/ incodingin-the-beginning-4e2a5c51a45d. 2. Simone Browne, Dark Matters: On the Surveillance of Blackness (Duke University Press, 2015). 3. John Cheney-Lippold, We are Data: Algorithms and the Making of our Digital Selves (New York: New York University Press, 2017). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 99 Part II. Scholarly Abstracts 4. David Lyon, Surveillance as Social-Sorting: Privacy, Risk, and Digital Discrimination (London: Routledge, 2008). 5. Lucas Itrona and David Wood, “Picturing Algorithmic Surveillance: The Politics of Facial Recognition Systems.” Surveillance & Society, v.2, no. 2/3, (September 2002). 6. Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Penguin Books, 2017). 7. N. Katherine Hayles, Unthought: The power of the cognitive nonconscious (Chicago: The University of Chicago Press, 2017). 8. Jenny Edkins, Face Politics (London and New York: Routledge, 2015). 10. John Szarkowski, Mirrors and windows: American photography since 1960. (New York: Museum of Modern Art; 1978). 11. Felix Guattari and Gilles Deleuze, A Thousand Plateaus: Capitalism and Schizophrenia (New York: Athlone Press, 1988). Biography Megan Olinger is a PhD candidate at City University of Hong Kong in the School of Creative Media. Her research focuses on artificial intelligence and human perspective. 100 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Are Photographers Superfluous? The Autonomous Camera Elke Reinhuber School of Art, Design and Media ADM, NTU Singapore elke@ntu.edu.sg; eer@me.com Abstract Once upon a time, photography was a true art. The skilful arrangement of image composition, the accurate illumination and the particular palette, let alone the technical process behind the image deserved elaborate knowledge and yearlong training and practice. Nowadays, millions of images are captured every day without the consideration of exposure, the musing on the effect of focal length, aperture, shutter speed or ISO. On top of that, even more images are captured by machines – not necessarily for the human eye, but to be read again by machines. With my background as a photographer using analogue processes and large format cameras in my three year training, I keep pondering on the development of the medium in the days in which every one – human, animal or robot – is able to take correctly exposed and focused images in full-auto mode. Therefore I propose with this paper, that an intelligent apparatus might soon replace the image-taking human being. The Superfluous Photographer in Automode To observe the end of photography is more of a platitude, because this statement has been made for years, yet the snapping continues without ceasing. [1] Most of the resulting images nonetheless are unlikely to ever be seen, some will be deleted or simply lost, become unreadable after the next update, or they will disappear without being missed. The essence of digital photography is itself transient, since these photos exist only as long as you look at them, they are generated by the imaging software instantly just to dissolve again as bits in the stream of data then, and they manifest themselves only for a moment. Conventional practices such as printing secure those fleeting impressions for the longterm, but to transfer digital data on to photographic paper or celluloid is a transmutative act into a different state of matter. With the actual image being gone, the authenticity of the creator becomes arguable. The concept of an automated photographer is not a fancy idea or a futuristic invention but a very reasonable notion, merging the possibilities of imagecapturing and recognition. One could even suggest that non-intelligent photography-machines were already invented with the Photomaton. Postponing the Decisive Moment The ‘decisive moment,’ as postulated by Henri Cartier-Bresson, serves as a catchphrase for professional photographers to describe their craft, finding exactly the right adjustments and timing for each picture. [2] Photography is for him “the simultaneous recognition, in a fraction of a second, of the significance of an event as well as of a precise organization of forms which give that event its proper expression.” [3] Since the framing of the shot constitutes the essential idea of a compelling image similar to the decisive moment, the prospect of finding another perspective retroactively seems propitious and sombre at the same time. Recent developments such as plenoptic cameras, also known as lightfield photography, enable the photographer to decide retrospectively on focus and the depthof-field. Analogously, postponing the perfect framing, while shooting a 360° image in high resolution, one can subsequently choose any aspired angle. So-called smart cameras have arrived already in the market, eg. the Insta360 Pro, which can record movies or stills in a 360° sphere and frame the final image according to simple markers, put into the software viewer. [4] Proceedings of Art Machines: International Symposium on Computational Media Art 2019 101 Part II. Scholarly Abstracts The Intelligent Camera The technical history of photography shows plenty of inventions to simplify the act of imagetaking by automating certain stages in the process. The approach remained always the same, streamlining the technique to free the person behind the lens from any obstacles, with shutter priority, aperture priority, program mode or autofocus. Today’s techniques allow retrospective decisions. High dynamic imaging is made possible by intentionally over- and underexposing the same picture, weighing the different light values into an image and allowing the recovery of unseen details in bright and dark areas. This demonstrates even more capability than the electronic photo detectors of the uncompressed images in RAW format and allows to recover unseen details in bright and dark areas. ‘Intelligent’ cameras can delay the release of the shutter until the presumed subject is in focus – or even more. A decade ago, Sony introduced the smile detection algorithm in certain cameras to the effect that all portraits were made with happy faces. However, the intensities of smiles could be adjusted by the photographer. [5] The ubiquity of cameras at any time of day in every corner of the world results surprisingly in hardly anything happening unnoticed. But not only the arbitrary activities of anyone will be recorded, so will our surroundings be documented for future generations. In times of unrest and war, these documents can come handy – when the dust settles, an architectural site which lies in ruins could be reconstructed only with the aggregate of the many existing photographs. This restoration would not necessarily depend on a professional photogrammetric assessment. The mass of images from all angles could suffice such as in the recent example of Palmyra [6]. The Autonomous Photographers Based on the observations of the state-of-the-art, we can only imagine what will be the next technical achievement to facilitate and automate photography, considering all the industrial advances in image recognition. Surrounded by surveillance cameras, the individual photographic apparatus might soon become superfluous, at least for selfies and other 102 concepts to record the proof of an individual’s happiness at a certain location. The public spaces around us, cities and crowded places all over the world, are pervasively furnished with surveillance cameras which act as autonomous photographers, framing and recognising faces, following people’s movements, and filling databases. Since these devices point in every direction to catch perpetual glimpses of us, we could demand to capture us on our holidays and deliver the images right to our email account, associated with our facial recognition profile. With pre-sets for stylistic elements such as basic rules for composition and colour, these postcards from the omnipresent observer could console us in our loss of independency and privacy. References 1. Anonymous, ed., Is Photography Over? Transcript of symposium at SFMOMA, April 22–23, 2010 (San Francisco: Museum of Modern Art), sfmoma.org/photography-over/ 2. Henri Cartier-Bresson, The Decisive Moment, in: Images à la sauvette (New York: Simon and Schuster 1952), i. 3. Henri Cartier-Bresson, Images à la sauvette. 4. Will Nicholls, Insta360 ONE: A 4K 360 Camera That Lets You ‘Shoot First, Point Later’ (Berkeley: PetaPixel, 2017), petapixel.com/2017/08/28/insta360-one-4k360-camera-lets-shoot-first-point-later/ 5. Yu-Hao Huang, Chiou-Shann Fuh, Face Detection and Smile Detection (National Taiwan University, Dept. of Computer Science and Information Engineering, 2009), csie.ntu.edu.tw/~fuh/personal/FaceDetectionandSmileDetection.pdf 6. Tim Williams, Syria – The Hurt and The Rebuilding (Conservation and Management of Archaeological Sites, Volume 17, Issue 4, 299-301, 2015). Biography With her background in applied photography, media artist Elke Reinhuber has experienced a wide range of cameras. While being fascinated but also scared by the omnipresent lenses which are pointing at each and everyone, she is curious to explore expanded photography such as stereoscopic imaging, photogrammetry, and further aspects of re- Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Are Photographers Superfluous? The Autonomous Camera. Elke Reinhuber cording light and other electromagnetic radiation, even beyond the visible spectrum. Elke teaches currently at the School of Art, Design and Media at NTU, Singapore. Her artwork was presented internationally. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 103 How Machines See the World: Understanding Image Labelling Carloalberto Treccani School of Creative Media, City University of Hong Kong carloalberto.t@my.cityu.edu.hk Abstract Michael Baxandall, in Painting and Experience in 15th Century Italy (1988), shows the existence of a series of rules that painters were advised to follow. These "guidelines" explained, for instance, how each different figure or hand position painted, within that specific cultural context, represented a different concept. These rules helped the painter maintain relevance in that historical and cultural context. [1] Today, more than 500 years after the Renaissance Italy described by Baxandall, companies all around the world are trying to teach Machines and Algorithms (M/A) to see and understand what they see (image recognition). However, this process of signification, simple for a human being, is still complex for M/A. Therefore hundreds of thousands of workers, therefore, are hired, through crowdsourcing platform, in order to label what they see. An image of a house appears on the monitor and the worker then attributes the "house" label to that image. These images are then categorized by the received label, or semantic area, and then collected in databases which are used to train M/A. [2] However, this labelling process produces a series of problems. The workers are paid in pennies per image labelled and work in precarious working conditions without any labor protection. [3] Sometimes the annotators are required to label unknown scenes or objects (e.g., objects and tools in a physics laboratory) even when they lack the competence or knowledge. Moreover, if the employer considers their work unsatisfactory, payment can be denied without any explanation. [4] All these different reasons often cause insufficient and confusing labelling. Yet these "low quality" 104 labels are determining the way M/A understands the world. Furthermore, every time we make a click on internet, on social media we are not only conveying some information, but also engaging and establishing a pedagogical process. We are not only viewers and users, instead, we are teaching M/A how to look at the world. [5] Given this context, I would like to address some questions as follows: What are the consequences of a learning process that is confused, inaccurate, and qualitatively poor, in this unprecedented historical moment where there are more M/A than human beings examining and trying to create sense of what they see? What are the implications of this low quality work, which does not appear today as an image but instead as labelled data, which in turn contributes to fully defining the visual experience of these M/A? [6] References 1. Michael Baxandall, Painting and Experience in 15th Century Italy (Oxford: Oxford University Press, 1988). 2. Alexander Sorokin & David Forsyth, “Utility Data Annotation with Amazon Mechanical Turk”. 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops (2008). doi:10.1109/cvprw.2008.4562953. 3. Nicholas Malevé, “The politics of image search - A conversation with Sebastian Schmieg” [part I and II], The photographers gallery website, accessed February 15, 2018. https://unthinking.photography/themes/intervie ws/interview. 4. Treccani Carloalberto, "How Machines See the World: Understanding Image Annotation", Proceedings of Art Machines: International Symposium on Computational Media Art 2019 How Machines See the World: Understanding Image Labelling. Carloalberto Treccani NECSUS_European Journal of Media Studies, no. Spring 2018_#Resolution, accessed October 12, 2018. https://necsus-ejms.org/howmachines-see-the-world-understanding-imageannotation/. 5. Nicholas Malevé, “The politics of image search - A conversation with Sebastian Schmieg” [part I and II], The photographers gallery website, accessed February 15, 2018. https://unthinking.photography/themes/intervie ws/interview. 6. Trevor Paglen, “Invisible Images (Your Pictures Are Looking at You)”, The new inquiry website, accessed May 01, 2017. https://thenewinquiry.com/invisible-imagesyour-pictures-are-looking-at-you/. Biography Carloalberto Treccani is a PhD candidate at the School of Creative Media, City University of Hong Kong, and an artist. His research investigates how machine vision is affecting human vision. More broadly he is interested in how technology affects human perceptions and emotions. His artworks have been exhibited in group and solo exhibitions and commissioned by galleries and institutions. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 105 The Struggle Between Text and Reader Control in Chinese Calligraphy Machines Yue-Jin Ho The Open University of Hong Kong yjho@ouhk.edu.hk Abstract This paper introduces a work-in-progress typology to classify and study the characteristics of Chinese text-based playable media (e.g. interactive installations, screen-based works, mobile applications, and computer games). Two factors are initially proposed for building such model: 1) How the visual properties of the characters are being used in the meaning making process of the works? 2) The degree and/or type of freedom provided to the users for interacting with the Chinese characters in the works. The first factor is borrowed from Cosima Bruno who introduced a model for studying static Chinese visual poetry. For her, the ideographic nature of Chinese characters expanded the potentials of visual poetry. The author can and often will create a context for extracting embedded historical meanings (etymo-visual text) or inventing new meanings (beyond-lexical text) from the components of a character. [1] Bruno’s model is useful for analyzing the intersecting relation between the visual arrangement and the semantic values of Chinese words, but it only deals with static and nondigital works. As for the condition of interactive media, I would propose that the factor of how a user can interact with the individual character itself is vital for such analysis. The Chinese language differentiates itself from letter-based languages as it consists of thousands of characters instead of a small number of letters. This affected how the Chinese language coped with the Western-led development in IT technology (i.e. from typewriter to smart phone), but it also contributed to some unique inventions and possibilities such as the predated technology of predictive text in the 1950s. [2] Nowadays, there are also many Chinese textbased works which make use of the uniqueness of Chinese languages and create their interactive aesthetics on how a user can play with the individual characters. I summarize three 106 possible conditions for these works: 1) Users are technically totally free to “write” anything, similar to using a pen in real-life; 2) Users can control the components of characters; 3) Users can only control the completed characters. After deploying the three factors above for studying various Chinese text-based works, a specific kind of Chinese text-based work clearly stood out for which I coined the term Chinese Calligraphy Machines. In this kind of work, users are usually invited to draw a single character of their choice and to expand the character’s etymological meaning and/or create new meaning with the provided context. To achieve this, these works are always designed to provide a large degree of freedom for the users to draw/write. Some scholarly works on digital calligraphy have been done to study Chinese Calligraphy Machines along with static and performative works. [3] However, most of these researches focus on the traditional aesthetic issues or the phenomenological factors during the artistic creation, and their studies are not specific to interactive works. In this paper, I will try to apply a concept from Aarseth who suggested three constantly struggling ideological positions in cyborg aesthetics, namely author control, text control and reader control. [4] I would suggest that, when being played, the meaning making process of most of the Chinese Calligraphy Machines are struggling between text control and reader control, which also contributes to the interactive aesthetics of these works. References 1. Cosima Bruno, “Words by the Look: Issues in Translating Chinese Visual Poetry,” in China and Its Others: Knowledge Transfer Through Translation, eds. James St André and Peng Hsiao-Yen (Leiden: Rodopi, 2012), 245. 2. Thomas S. Mullaney, “The Moveable Typewriter: How Chinese Typists Developed Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Struggle Between Text and Reader Control in Chinese Calligraphy Machines. Yue-Jin Ho Predictive Text during the Height of Maoism,” Technology and Culture 53, (2012): 78. 3. Pei-chen Yeh, “Writing & Image,” (Diss., Department of Visual Arts, National Pingtung University, 2011.); Wan Siang Lim, “The Combination of Calligraphy and Interactive Media Introducing by Phenomenology of BodyShu Fa,” (Diss., College of Design, National Taipei University of Technology, 2017.) 4. Espen J. Aarseth, Cybertext: Perspectives on Ergodic Literature (MD: JHU Press, 1997), 55. Biography Yue-Jin Ho is a Senior Lecturer in Creative Arts at the Open University of Hong Kong and currently a PhD candidate in the School of Creative Media, City University of Hong Kong. He is also an artist, translator and writer. His works often deal with the relations between materiality, writing and history. His works have been selected by international festivals such as the IFVA Hong Kong, Cinetribe Osaka, ZEBRA Poetry Film Festival Berlin and Shanghai Biennale. Currently, his research focuses on Chinese text-based new media arts and visual poetry. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 107 Bacterial Mechanisms: Material Speculation on Posthuman Cognition Mariana Pérez Bobadilla City University of Hong Kong maropebo@my.cityu.edu.hk Abstract Cognition is not uniquely human. Life on earth, from microbes to mammals, has performed cognition deep in time. Through the material speculation that art makes possible, this paper considers bringing forth microbial cognition as a wider possibility for models of intelligence. Artificial intelligence has often had human intelligence as a parameter and aspiration. Basic definitions of AI describe its goals in relation to human intelligence. This paper focuses on how art can be the place where research about nonhuman cognition in microorganisms can encounter AI machine non-human intelligence. Some authors like Adrian Mackenzie have described how biology has been used as a technology, in the specific case of bacteria as technical objects, comparing bacterial genomes to operating systems or in synthetic biology. [1] [2] This project, thinking with and through art, goes beyond DNA-centered biology and its implied simplification, to think of microorganisms’ cognition as a whole, in collectivity and with its environment. [3] [4] Lyon carefully describes non-human centered forms of cognition present in eubacteria such as sensory signal transduction, valence, different forms of communication, sensorimotor coordination, memory, learning, anticipation, and decision making in complex and changing circumstances. [4] Following Lyon’s work as a theoretical framework, this paper refers to the artwork Speculative Communications (2017) by the art collective Interspecifics as a form of material speculation. In Speculative Communications the imagination of possibility is grounded in the materiality of art and it is made possible through 108 DIY logics of production. The speculative figurations in this paper are a way of understanding the intersecting practice of art and biology, which gives value to this practice as an open-ended process without the constraints of institutionalized science, while opening possibilities of speculative thought and imagination, and maintianing the nonessentialist grounding of new materialism of biological matter. [5] In other words, this paper seeks to preserve a powerful capacity of speculation without losing accountability, by imagining strategies that balance political accountability, with scientific speculation and a valuable esthetical experimentation on materiality. Speculative Communications was premiered at the MUTEK, a festival dedicated to the promotion of electronic music and the digital arts in Montreal 2018. The work is a microscope powered by AI to observe and learn from a culture of Bacillus circulans bacteria. The data is then used as a sound art score. This generates an experience of the phenomenon of machines and microorganism’s cognition together, becoming sound and image. Resourcing to DIY techniques and transdisciplinary collaboration, the machine monitors and learns. [6] Through computer vision in the microscope it learns from the bacteria module, by tracking and recognizing its movements and patterns. This information is fed to an algorithm that starts to learn and recognize behaviours. Then, AI is given the freedom to generate with the input of images and data, using OpenFrameworks and Supercollider, as a continually generative piece. All the contents are transmitted, so a human audience can experience the phenomenon of Speculative Communications. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Bacterial Mechanisms: Material Speculation on Posthuman Cognition. Mariana Pérez Bobadilla The work contains multiple dimensions. Aesthetically, it shows the tracking and analysis of the behaviour of Bacillus circulans, and the data turned into sound, mediated by algorithms, in an experience similar to contemporary sound art. Interspecifics, also resources to DIY logics to access AI, allowing it to focus on another side of cognition and AI, that of the non-human cognition of microorganisms. In the use of art as a way to carry out material speculation, Speculative Communications brings forward the posthuman (understood by Braidotti as a post-anthropocentric approach to life). [7] This work also enacts the cyborg continuum of non-humans and machines of Braidotti’s classic cyborg manifesto with the minimum of what is considered living in bacteria, and without the anthropomorphic shape of many robots, a greater posthuman leap. [8] Finally, the situated imagination of possibility takes place through art in the work of Interspecifics, as the material speculation about cognition of microbial-machine. This form of speculation intimates to a post-anthropocentric perspective bringing forth the communication, coordination and behavioural patterns of Bacillus circulans. This paper suggests that the aims of AI could be widened by notions of intelligence including forms of cognition from the most basic and prevalent forms of life: bacteria. Biography Mariana Pérez Bobadilla is an Art Historian concerned with the intersections of art, science, and technology. She studied an Erasmus Mundus master in Gender Studies at the University of Bologna, Italy. She has presented her work in ISEA 2012 and has been involved in the Mexican Pavilion at the 56th Venice Biennale. Her research in the School of Creative Media revolves around Art and Biology, Epistemology, New Materialism, Biohacking, Wetware, and bacteria. Fig 1. Speculative Communications, 2018, Interspecifics, multispecies performance, courtesy of the artists. References [1] Adrian Mackenzie, “Technical objects in the biological century” Zeitschrift für Medien und Kulturforschung. 12, no.1 (2012): 151-168 [2] Koon-Kiu Yan, Gang Fang, Nitin Bhardwaj, Roger P. Alexander, and Mark Gerstein, "Comparing Genomes to Computer Operating Systems in Terms of the Topology and Evolution of their Regulatory Control Networks." Proceedings of the National Academy of Sciences 107, no. 20 (2010): 91869191. [3] Evelyn Fox- Keller, Refiguring Life: Metaphors of Twentieth-Century Biology (NY: Columbia University Press, 1996). [4] Pamela Lyon, "The Cognitive Cell: Bacterial Behavior Reconsidered." Frontiers in Microbiology 6:264 (2015). [5] Donna Haraway, Staying with the Trouble: Making Kin in the Chthulucene. (Durham, NC; London: Duke University Press, 2016). [6] Interview with the author, Mexico City, January 2018. [7] Rosi Braidotti. The Posthuman. (Oxford: Polity Press, 2012). [8] Donna Haraway, "A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late Twentieth Century". In Simians, Cyborgs and Women: The Reinvention of Nature. (New York; Routledge, 1991), pp.149181. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 109 Lying Sophia and Mocking Alexa – An Exhibition on AI and Art Iris Xinru Long China Central Academy of Fine Arts, Beijing longxinru@cafa.edu.cn Abstract This abstract is the curatorial statement of an exhibition exploring the relationship between AI and art, curated by the author, to be launched in 2019. Sophia, the humanoid robot who became a Saudi Arabian citizen is interpreted by Yann LeChun as a story intertwined with elements of ambiguity and deception co-compiled by the mass media and technological companies. Alexa, the cloud-based virtual assistant developed by Amazon, was reported as letting out eerie and unsettling laughter, which soon became viral on YouTube. A recent BBC news piece even reveals Alexa recording domestic conversations and sending them to people on the owner’s contact list “by mistake”. “Sophia” and “Alexa” seem to be two contemporary metaphors on machine lives, two thin slices interposed among the imbricated discourses on artificial intelligence. Sophia symbolizes the imagination of AI cast by the mass media, films and television: highly humanimitating appearances, alert and responsive, and even diplomatic – a quasi-human being embedded among us. Alexa, on the other hand, is an “assistant” or “servant” who takes a machine outlook and resides in domestic corners, whose laughter implies the nontransparent, anti-regulating, even peeping, subversive dimension of the artificial intelligence black box – even a “mistake” to be amended. Sophia’s lies are projections of poetic imaginations, Alexa’s mocking is a glitch in the algorithmic black box; what they share in common is a quantum-state like scenario of uncertainties, as if part of the “ZONA” in Andrei Tarkovsky’s 1977 Stalker. This is the point of departure for this exhibition. In the alternations 110 and evolutions of technologies, we’ve rarely encountered such a subject as the artificial intelligence: it is paradoxical, mind stimulating, and implies manifold future potentials – all trajectories that carry paradoxical and ambiguous underpinnings. Even as AI has been ubiquitously employed by microchips, processors, data mining and analysis, forming the new frontier of a global technological competition, it remains imperceptible and equivocal to the average citizen. Wrapped within information on mass media, AI has transformed into a story both the easiest to tell, and the most difficult to narrate. In Tarkovsky’s script, the stalker guides writer and a scientist to take a cable car, steer by the policemen’s chase, traverse tunnels of dripping water, detour rooms filled with sand dunes, and finally approximate the core of “ZONA”: a “Room” that makes beliefs true. The writer is concerned about the darkness of human nature that the Room suggests, while the scientist wishes to destroy the Room in case villains would take advantage of it; meanwhile, the Room endows the stalker with a meaning of existence. The exhibition sets up a metaphorical “ZONA” which embodies our contemporary situation: a time-space where both science and art are simultaneously deprived of the power of autocracy and narratives that command assent. Artists and researchers involved in this exhibition blend perspectives of Sophia (bright, poetic, media imagination) and Alexa (dark, black-box, technological criticism). They investigate how AI shuffles global, technical politics, and the relationships between nations and civilians; the dark, inhuman labor of using real humans (in exhausting fashion) to train “human-like” algorithms; the creation of Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Lying Sophia and Mocking Alexa – An Exhibition on AI and Art. Iris Xinru Long subjects of surveillance; the aspiration to project the entire human spiritual architecture on one single technology form; and the fairy-tale construals of AI elaborated by mass media. The concluding “Room” of the exhibition is to be built by the visitor (“stalker”). It interweaves the richness, uncountability/non-computability and vitality of the psychological world, and the implications of AI in the fundamental menace and nihilism of our own existence. Would it “break all the prophecies” like the event horizon in Vernor Steffen Vinge’s assertions, or be “the biggest mistake we have ever made” in Steven Hawking’s alert? The future of humanity is written in this “Room” containing unlimited new permutations and combinations. Biography Iris Long is a curator. She currently works as a researcher on art, science and technology at Central Academy of Fine Arts, with a research focus on how art responses to the current global reality of ubiquitous computing and big data. She lectures on data art at CAFA. Her artistic work has been exhibited internationally in venues including CAFA Art Museum (Beijing), Chronus Art Center (Shanghai), Power Station of Art (Shanghai), V2_ Institute for the Unstable Media (Rotterdam), ISEA (Hong Kong), and so on. Her work has been shortlisted in Prix Cube Art Prize, and received an honorable mention in ifva, Hong Kong. She was shortlisted by the first M21IAAC Award (International Awards for Art Criticism). Her translation work, Rethinking Curating: Art after New Media, received a nomination from AAC Art China awards in 2016. Iris Long has a master’s degree in Critical Writing in Art and Design from the Royal College of Art, UK. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 111 Art of Our Times: A Temporal Position to Art and Change Dr. Tanya Toft Ag City University of Hong Kong tanyatoft@gmail.com Abstract How we understand and approach art from certain epistemological grounds has implications for how we trace its genealogies, formulate its trajectories, understand its contextual and discursive departures and impacts, and develop our expectations to what the art might pursue – and do. This paper advocates for a holistic, non-linear perspective on contemporary (media) art as interfering with our world through time, rather than matter. It anchors ‘art machines’ in their temporal, operational core as art of our times, as implicated with and acting through temporal experience and ecologies. Boris Groys has suggested that contemporary art can be distinguished from that which prevailed during the modern era significantly by its core commitment to a notion of radical temporality, as it engages with a contemporary situation in which every element may be considered ‘temporary’. [1] In this contemporary perspective, art has emerged conceptually and materially from questions that pertain to perceptual-ontological conditions with contemporary technological realities. Art has reflected and challenged the communicative conditions of their times – oftentimes critical of the given dominant conditions of mediated experience. Besides being radically enabled by the evolution and mobility of the perceptual lens of, for example, the video camera and the mobile phone, art has evolved from concerns with expanding perception and with liberating the subject from fixed viewing structures. For example, through efforts to destabilize fixity of meaning and remediate power structures of physical places and their dynamics of social 112 encounters, and through initiatives of expanding and reconfiguring perception with media aesthetic ambiance and augmentation of realworld environments. Art both exists and expresses in contingency with technological culture and our contemporary communicative existence. Here I refer to art of our times not only in terms of art that engages time-based technologies that are implicated with perceptual experience, or which addresses issues of time and perception – across behavioral modes of e.g. temporal overlay, disruption, interactivity, forms of networkedness and telepresence, among many others. Art of our times denotes how the art operates by way of interfering with temporalities of various ecologies of our communicative existence, as art machines that enact a sense of ‘radical temporality’ – acting as present rather than represented ‘images,’ in direct, operational engagement with temporal, perceptual experience. [2] I exemplify the operation of art of our times in a current condition in which processes of change accelerate through machinic language and temporal effect, speeding up how we shape the world through language – from speech and writing to communication and algorithmic and machine learning processes. Machinic language accelerates the infrastructures and interfaces of how we see, do and make; how we distribute our subjectivity and sensibilities across multiple temporalities. [3] Machinic language affects our behaviors, routines and paths of understanding, and machinic and scientific processes rooted in ideas of linearity and relativity extend into innovation, design, social phenomena and human relations. Eventually, the human- Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Art of Our Times: A Temporal Position to Art and Change. Tanya Toft Ag machine symbiosis evolves fast and at temporal frequencies that bypass human consciousness and awareness. [4] In this context, the deeper questions with which art is implicated concern how our lived experience shapes our human, cultural and societal evolution. Our experienced, temporally conditioned sense of presence shapes our behaviors and our acting out of politics, economic systems, and cultural norms. At this point in time, when multiple and increasingly machinic temporalities structure and disperse our present being and experience, and in which hybrid environments of expanded reality increasingly become our experienced reality, I propose art of our times as a both epistemological, time-based contextualization of media art and an actual mode by which the art does. I argue that rather than existing as an object in space, art machines – as art of our times – act while embedded in our temporal experience. [5] With this I suggest a conception of art’s roles – and rules – of existence in the urban context as deeply implicated with dynamics of change. I exemplify how, with art, we can ask: What ritualistic behaviors are facilitated and encouraged through our designed, coded and instructed temporal experiences? Which cultural, social, political and economic ideas inform the modalities of our temporal experiences and immersion, and are these for example grounded in liberalization, separation and distance – or in association, interconnection and co-existence? Do they evoke sameness, or difference? I challenge a currently dominant and much celebrated discourse in the cross-field between the technical arts, architecture, innovation design, and urban development that anticipates art’s direct effect on matter and environment as an inevitable good, as an effective way of changing and optimizing our environments. This is a discourse that nonetheless does not account for how the ‘art machine’ complies with dominant narratives of politics, economy, or culture, affects ecologies of evolution, and results in intuitive-behavioral modes of indifference and production of more of the same. Instead of reproducing a Western-anchored, anthropocentric discourse obsessed with controlling and changing matter, with the concept of art of our times I examine a temporal perspective on art machines and advocate for a holistic perspective on how art affects ecologies of material, memory, and behavior, by affecting our relation to time and temporal experience. References 1. Boris Groys, “On the New,” in Art Power (Cambridge: MIT Press, 2008), 40. 2. Henri Bergson, Matter and Memory (1911), trans. N.M.P. and W.S.P (Mansfield Centre: Martino Publishing, 2011), 28. 3. Jacques Rancière, The Politics of Aesthetics, ed. and trans. Gabriel Rockhill (London and New York: Bloomsbury Academic), 2015. 4. N. Katherine Hayles, How We Think: Digital Media and Contemporary Technogenesis (Chicago and London: The University of Chicago Press), 2012. 5. Richard Grusin, “Radical Mediation”, Critical Inquiry 42, no. 1 (2015). Biography Dr. Tanya Toft Ag is a curator, researcher, writer and lecturer on urban media aesthetic phenomena and media art’s engagement with societal and urban change. She gained her doctoral degree from Copenhagen University with visiting scholarships at Columbia University and Konstfack (CuratorLab), and MA degrees from The New School and Copenhagen University. Her curatorial practice evolves with media art and media architecture in urban environments, and she has held keynotes and presented her critical perspectives on art and urban media worldwide. She is editor of Digital Dynamics in Nordic Contemporary Art (Intellect, 2019) and co-editor of What Urban Media Art Can Do – Why, When, Where, and How? (av edition, 2016). In 2017 she cofounded the globally networked Urban Media Art Academy. Her current research is situated at School of Creative Media, City University of Hong Kong. www.tanyatoft.com Proceedings of Art Machines: International Symposium on Computational Media Art 2019 113 Do Machines Produce Art? No. (A Systems-Theoretic Answer.) Michael Straeubig University of Plymouth michael.straeubig@plymouth.ac.uk Abstract Machines do not produce art, social systems do. Machines and Art Since early experiments with computergenerated art in the mid 1960s, the idea of “art machines,” entities that are not merely tools or assistants for human artists but capable of autonomous art production, has undergone a significant development. [1] Both technological progress and shifts in art appreciation have contributed to this. Our modern understanding of (capital-A) Art and the related concept of Fine Arts emerged during the 18th century. [2] However, like any established notion of art this understanding has faced critical re-negotiation. Thus, Postmodernism rattled those fundaments while Machine Art disturbs newly formed agreements what constitutes art. [3] Proponents of algorithmic art seek to re-define aesthetic concepts in information processing terms, questioning conventional anthropocentrism. [4][5][6] Recent contributions like Michael Matejas’ Expressive AI, Leonel Moura’s stigmergic robots and Marius Klingemann’s uncanny neural imagery push the aesthetic boundaries of generative machines and computational procedures. [7][8][9] But do those machines and algorithms produce art? I give an answer that I base on Niklas Luhmann’s systems-theoretic thinking, and this answer is: no. [10] Likewise, humans do not produce art either. Art is not created by any biological or nonbiological entity, but within social systems, constructed through recursive networks of communication. [11] 114 The answer does not change if we recast generative art as variants of the Turing Test. [12][13] It does not even change if we conceptualize machines and humans as ensembles or take into consideration the fluidity of their difference. [14][15] This observation invites us to refocus on different distinctions than the still prevalent discourse around humans vs. machines. To understand the ramifications of the shift from the artist as an individual auteur to art as a social system, it is useful to observe and explore forms of art that make this approach visible. “The new artist” by Alex Straschnoy et al. presents a robot that is performing for a robotic audience. [16] Techne is an algorithmic community that produces as well as mutually critiques digital art. [17] In both projects, the relationship between art, artist and audience is re-negotiated and humans become second-order observers of the art production. [18] Machines do not produce art, social systems do. We may begin to ignore the difference between human and machine; it does not make a difference. What we need to do is to restructure our expectations and to invite more machines into our art system. To achieve this, it may be well worthwhile to revisit systems art as a bridge between cybernetic tradition and currently emerging generative techniques. [19][20] Before that we have to update concepts of art and systems in order to understand the art of machines. [21] References 1. Grant D. Taylor, When the Machine Made Art: The Troubled History of Computer Art (2014). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Do Machines Produce Art? No. (A Systems-Theoretic Answer). Michael Straeubig 2. Paul Oskar Kristeller, “The Modern System of the Arts: A Study in the History of Aesthetics Part I,” Journal of the History of Ideas 12, no. 4 (Oct 1951). https://doi.org/10.2307/2707484. 3. Louise Norto, “The Richard Mutt Case.” The Blind Man, May 1917. 4. Frieder Nake, Ästhetik als Informationsverarbeitung: Grundlagen und Anwendungen der Informatik im Bereich ästhetischer Produktion und Kritik (Wien: Springer, 1974). 5. Jürgen Schmidhuber, “Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts,” Connection Science 18, no. 2 (2006): 173–187. 6. Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. “A Neural Algorithm of Artistic Style,” ArXiv Preprint ArXiv: 1508.06576, 2015. https://arxiv.org/abs/1508.06576. 7. Michael Mateas, “Expressive AI - A Hybrid Art and Science Practice,” Leonardo: Journal of the International Society for Arts, Sciences, and Technology 34, no. 2 (2001): 147–53. 8. Leonel Moura, “Machines That Make Art.” In Robots and Art, edited by Damith Herath, Christian Kroos, and Stelarc (New York, NY: Springer Berlin Heidelberg, 2016), 255–69. 9. Mario Klingemann, “Quasimondo | Mario Klingemann, Artist” (2018). http:// underdestruction.com/. 10. Niklas Luhmann, Social Systems. Writing Science (Stanford, Cal: Stanford University Press, 1996). 11. Niklas Luhmann. “Das Kunstwerk Und Die Selbstreproduktion Der Kunst,” Delfin, 3 (1984): 51–69. 12. Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone, “CAN: Creative Adversarial Networks Generating ‘Art’ by Learning About Styles and Deviating from Style Norms,” 2017. 13. Jörg Räwel. “Können Maschinen denken?” Telepolis, August 4, 2018. https://www.heise .de/tp/features/Koennen-Maschinen-denken4117648.html. 14. Bruno Latour, “A Collective of Humans and Nonhumans: Following Daedalus’s Labyrinth.” In Pandora’s Hope: Essays on the Reality of Science Studies (Cambridge, Mass: Harvard University Press, 1999), 174–215. 15. Victor Marques, and Carlos Brito, “The Rise and Fall of the Machine Metaphor: Organizational Similarities and Differences Between Machines and Living Beings,” Verifiche XLIII, no. 1–4, (2014): 77–111. 16. Axel Straschnoy, Ben Brown, Garth Zeglin, Geoff Gordon, Iheanyi Umez-Eronini, Marek Michalowski, Paul Scerri, and Sue Ann Hong. “The New Artist” (2008). http://www.the-newartist.info/. 17. Johnathan Pagnutti, Kate Compton, and Jim Whitehead, “Do You Like This Art I Made You: Introducing Techne, A Creative Artbot Commune,” Proceedings of 1st International Joint Conference of DiGRA and FDG, 2016. 18. Niklas Luhmann, “Observation of the First and of the Second Order.” In Art as a Social System, Meridian, Crossing Aesthetics. (Stanford, Ca: Stanford University Press, 2000), 54–101. 19. Jack Burnham, “Systems Esthetics,” Artforum (1968). 20. Edward A Shanken, “Reprogramming Systems Aesthetics: A Strategic Historiography,” Proceedings of the Digital Arts and Culture, UC Irvine (2009). 21. Niklas Luhmann, Art as a Social System (Stanford, Ca: Stanford University Press, 2000) Biography Michael Straeubig (@crcdng) is a Marie Curie Fellow and former Award Leader for Game Arts and Design at Plymouth University. He is researching and exploring the relationships between systems, play and games in various media with a focus on mixed reality and posthuman play. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 115 The Janus-Face of Facial Recognition Software Romi Mikulinsky Ph.D. Bezalel Academy of Arts and Design, Jerusalem rominska@gmail.com Abstract The human desire “to know the face in its most transitory and bizarre manifestations” was stimulated by the use of photography, argues film historian Tom Gunning. [1] Subsequently, it also inspired the invention of motion pictures. The drive to “know the face” and to decipher its diverse characteristics and manifestations continues to inspire scientists, health and advertising professionals, as well as law enforcement experts, in their efforts to develop automated facial recognition systems. Such systems are used for identifying human faces and distinguishing them from one another, and for recognizing human facial expression. This tendency to teach computers to “see” the human face is part of a broader effort to automate vision – to create machines that not only can generate images, but also analyse their content. As artist and geographer Trevor Paglen asserts, most images produced today are created by machines for machines to decipher. [2] For Paglen this “machine-to-machine seeing” is dramatically changing many spheres of human lives. Machine vision systems and digital images have permeated and are now transforming economy and transportation, industrial operations, law enforcement and urban lives, in autonomous cars and “smart” cities. The rise of machine-to-machine seeing apparatuses has also impacted art. We now hear of machines making art, almost independently of humans. But machines and machine learning (ML) are also affecting the art world in a more immediate way. Since various manifestations of artificial intelligence (AI) and ML have become a cultural phenomenon, artists and designers are responding to them, and are already investigating ways of harnessing ML and computer vision to their arsenal. 116 This paper contextualizes efforts made by artists and designers to reinvent facial recognition technology so that it can be put to other uses than computerized forms of surveillance. I examine artworks that take automated face perception technologies, reverse-engineer them, re-appropriate them and reveal their biases. These include, for example, Adam Harvey’s “DIY Camouflage” (2010-), Shinseungback Kimyonghun’s “Cloud Face” (2015), and Trevor Paglen’s “A Study of Invisible Images” (2017). I then go on to explore examples from art, fashion and design that propose an alternative visibility, one that renders faces unrecognizable to computer vision systems: Zach Blas’ “Facial Weaponization” (2011-2014) and “Face Cages” (2013-2016), or Hungry’s distorted drag (which can also be seen in the body of Björk‘s recently released album Utopia). Inspired by drag, theatre and religion, and drawing on queer ideology, these masks, jewellery and make-up can be considered as anticipatory of future avant-garde practices designed to make faces informatically invisible and inaccessible to the machine, empowering us to choose between and play on our identities. Ironically, the right to disappear from the machinic gaze, to fly under the surveillance radar, straddles the line between what is socially acceptable and what appears grotesque or inadequate. The right to disappear also demarcates what computers and humans can or cannot see - or rather, make sense of. This paper proceeds by undermining the concept of having one stable identity, of one’s face as an “unchanging repository of personal information from which we can collect data about identity,” as feminist theorist Shoshana Amielle Magnet puts it. [3] Going beyond the vantage point of contemporary AI and machines’ ability to detect Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Janus-Face of Facial Recognition Software. Romi Mikulinsky and decode faces as means of power and control, the emerging technical developments call for possibilities much less familiar, and perhaps much more exciting, than government surveillance. Such possibilities promise to reassess the expressive capacities of the face, and its multiple features and qualities, inviting a revolutionary outlook on culture and acceptability, identification and social interaction. Can we conceive of new uses and new narratives for facial recognition technology? References 1. Tom Gunning, “In Your Face: Physiognomy, Photography, and the Gnostic Mission of Early Film,” Modernism/Modernity 4, no. 1 (1997). 2. Tevor Paglen, “Invisible Images (Your Pictures Are Looking At You)”. The New Inquiry (December 8 2016). https:// thenewinquiry.com/invisible-images-yourpictures-are-looking-at-you/. 3. Shoshana Amielle Magnet, When Biometrics Fail: Gender, Race, and the 4. Technology of Identity (Durham, NC: Duke University Press, 2011). Biography Romi Mikulinsky is the head of the Master of Design (MDes) program in Industrial Design and a senior lecturer at the Bezalel Academy of Arts and Design in Jerusalem. Her dissertation at the University of Toronto's English Dept. was dedicated to photography, memory, and trauma in literature and film. Dr Mikulinsky researches and lectures on the future of reading and writing as well as on the various interactions of words and images, texts, codes, and communities in the information age. She has worked with various startup companies and media websites, corporations and municipalities on implementing innovative communication technologies. She served as the Director of the Shpilman Institute for Photography and worked with various art museums in Israel. Her book Digital Clutter: Topics in Digital Culture, coauthored with Prof. Sheizaf Rafaeli, is to be published in 2019. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 117 A Pixel-Free Display Using Squid's Chromatophores Juppo Yokokawa Graduate School of Design, Kyushu University 2DS18079Y@s.kyushu-u.ac.jp Haruki Muta Ryo Adachi Hiroshi Ito Kazuhiro Jo Graduate School of Design, Kyushu University intrjctn@gmail.com Graduate School of Design, Kyushu University mumanddad6660816@gma il.com Faculty of Design, Kyushu University hito@design.kyushu-u.ac.jp Faculty of Design, Kyushu University / YCAM jo@jp.org Abstract In this ongoing project, we propose a pixel-free display using a squid's chromatophores. The squid's body surface has cells which contain a pigment called chromatophores. Instead of using pixels of standard visual displays, we stimulated the chromatophores of a squid by sending sound signals of accompanied music to its body through electronic probes and made an experimental music video. chromatophores by sound signals and shot our experiments as a music video. 2. Related work Backyard Brains Inc. introduces an experiment to stimulate chromatophores by sound signals from iPod. [3] Based on the trial, we measured Chromatophore’s frequency response (Fig. 1). [4] 1. Introduction Our daily lives are surrounded by various types of visual displays, such as computer monitors, smart phones, projectors, VR headsets etc. The information carried by them varies depending on the applications or purposes such as videos, news, slides, games, etc. However, almost all of the displays are the same in that they are composed of pixels. Even in the case of computational “generative” art like biological simulation, it is inevitable to compute and render an image by pixel unit. [1] In this project, we consider squids as an alternative display free from pixels. The squid's body surface has cells which contain a pigment called chromatophores. [2] The squid freely changes its body color by changing the size of the pigment with electric signals from nerve cells to the chromatophores. To take advantage of these features, we stimulated 118 Fig. 1. The area of chromatophore per frequency [4] Based on the experiments, we seek a best relationship between chromatophores and sound signals in a form of music. 3. Our approach 3-1. Sound As a preliminary experiment, we first explored how chromatophores respond to sound signals. We attached a copper needle to a sound cable as an electronic probe and directly produced sound signals from a computer into the surface Proceedings of Art Machines: International Symposium on Computational Media Art 2019 A Pixel-Free Display Using Squid’s Chromatophores. Juppo Yokokawa, Haruki Muta, Ryo Adachi, Hiroshi Ito, Kazuhiro Jo of a fresh squid (Fig. 2). The result showed that the lower frequencies (i.e., the bandwidth of drum and bass) cause a higher effect to the chromatophore. Fig. 3. Footage form a music video Fig. 2. Setup of the experiment. To make music (i.e., sound signals) appropriate to the squid, we finely generated waveforms of the music (i.e., sound signals) with a numerical programming environment, MATLAB, by checking the effectiveness with different arrangements. We generated each waveform with MATLAB, and arranged the waveforms in a form of music with standard music production software Ableton Live10. 3-2. Shooting We shot our experiments by Canon 60D with Canon EF-S 35mm f/2.8 Macro IS STM. We shot several footages by changing the stimulation point of the squid with the same music. After the shoot, we edited the footages by Adobe Premiere Pro CC 2018. To keep the consistency between the sound and the display, we limited our edits to cutting, masking, and subtle color grading. 4. Result As a result, we made a music video as one application of the pixel-free display using the squid's chromatophores (https://youtu.be/66RoX2h8aI ). The duration of the video is 2min 45sec, and the resolution is 1080p (Fig. 3). Even though display successfully escaped from the use of pixels, the video itself remained in pixel form. Therefore, as future work, we plan to show the display in real time as the form of live performance. Acknowledgements This work was supported by JSPS KAKENHI Grant Number JP17H04772. References 1. Hartmut Bohnacker, et al. Generative Design: Visualize, Program, and Create with Processing (2012). 2. R.A. Cloney, and E Florey. Ultrastructure of cephalopod chromatophore organs. Cell and Tissue Research 89, (1968) : 250-280 3. Backyard Brains, “Insane in the Chromatophores,” (2012). http://blog.backyardbrains.com/2012/08/insan e-in-the-chromatophores/. 4. Ryo Adachi. 2017. “Frequency Response of Chromatophores to Electrical stimuli in Uroteuthis edulis.” Bachelor thesis, School of Design， Kyushu University. (in Japanese). Biography Juppo Yokokawa is in the 1st year of a Master’s degree in Graduate School of Design, Kyushu University. He has received the Bachelor in visual communication design from the School of Design, Kyushu University in 2018. His research interests include media art, especially bio art and kinetic art, under the supervision of Kazuhiro Jo. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 119 VR and AI: The Interface for Human and Non-Human Agents Lukasz Mirocha School of Creative Media, City University of Hong Kong lukasz.mirocha@my.cityu.edu.hk Abstract The paper analyses how real-time 3D graphics, VR and AI, allow users to create a new type of interface/environment for human and nonhuman agents’ collaboration and learning. It argues that spatial media (VR, AR) should be considered as real-time software and media interfaces, rather than multi-media projections. The study is informed by software and platform studies, critical theory and media studies perspectives. The Ultimate Display as an Interface In 1965, Ivan Sutherland suggested that "the ultimate display would be a room within which the computer can control the existence of matter.” [1] Nearly a decade later, in one of the Star Trek: The Animated Series' episodes, a peculiar technology appeared – the holodeck. Today, we could describe it as an ultimate VR environment designed for work and entertainment. Lately, thanks to the combination of latest developments in AI, computer graphics and VR – we seem to be closer to turn these 20th century dreams into reality. Berry and Dieter write that in the last decade, “computation [has become] experimental, spatial and materialized in its implementation, embedded within the environment and […] even within the body.”[2] Following Grau, we could ask about affordances and limitations of immersive and real-time software media, and objectify them "through knowledge and critique of the image production methods.” [3] Analysing a new software and media ecology for the creation of virtual or hybrid environments that open new dimensions in human-machine interaction can help us to understand not only the conditions behind these phenomena but also their wider cultural impact. 120 Simulated Reality – NVidia’s Applications At Siggraph 2017, NVidia showed the Isaac Robot that had been trained in a virtually created world to play dominos with human players. Isaac Robot was firstly trained in Isaac Sim, a virtual training environment. [4] The environment was based on a modified version of a game engine – Unreal. Isaac Sim offers fully integrated and high-fidelity visuals and physics simulation. Thanks to a set of AI algorithms for deep reinforced learning in a virtual environment, a virtual robot can iterate and learn much faster than in a real world. The same rationale lies behind NVidia Drive Sim. [5] It is a virtual training environment that utilizes high fidelity visuals and physics to simulate realworld driving in different weather lightning and traffic conditions. The photorealistic data streams generated by the software are compatible with the same sensors and chips that are used in physical autonomous cars currently tested by the company. Ultimately, a physical testing car can be firstly trained in Drive Sim and then use its knowledge in a real-life situation. At a certain level of generalization, we can then conclude that the rationale behind NVidia’s experiments is to have two instances of agents (robots and cars), the virtual one that is trained through reinforced learning techniques, and the physical one, that consists of a physical "body" and makes use of data gathered by its virtual counterpart to perform tasks in a non-virtual environment. By "body" we mean the same array of sensors and actuators for perception, navigation and manipulation both in the virtual and in the physical world. NVidia’s applications of real-time graphics and AI is a continuation of research conducted by other companies and Proceedings of Art Machines: International Symposium on Computational Media Art 2019 VR and AI: The Interface for Human and Non-Human Agents. Lukasz Mirocha researchers, e.g. Xerox Research (pre-training of computer vision algorithms for autonomous vehicles in a virtual environment created by Unity 3D engine) [6]; or Princeton, Darmstadt University and Intel (real-time recognition of objects, such as road signs, people, and cars, by a machine learning system in a modified video game environment (GTA V). [7] VR as an Approximate and Simplified World Simulation and Interface NVidia’s achievements prove that today we can use sophisticated software/hardware ecologies to create virtual environments. Conceptually, these environments take advantage of the principles of "approximation to" and "simplification of" when simulating selected properties of the physical world - visuals, audio, physics etc. [8] As a result, at a very basic level, these environments function as streams of data and algorithms processed in real-time by efficient hardware platforms. These data streams can be converted into output material (cues) that can be delivered at the same time both to human agents (e.g. as a dimensional 3D space visualized and interacted with as a VR experience), and to non-human agents. The simplification of the physical world to several data streams (for instance video feed, radar, and proximity sensor) that would not be suitable for human agents, is sufficient for the robot to operate in a physical environment. We can observe a comparable situation when the roles are reversed. Human agents can operate in a VR environment that offers an approximate simulation of physical world by stimulating human senses with simplified cues (visual, auditory, and haptics). If we were to assess the conceptual status of virtual environments used in the examples presented above, we could follow Galloway's idea of interfaces as "processes" and “zones of activity.” [9] Immersive CGI-based environments could be considered, not as multisensory projections, but rather as interactive, real-time interfaces. Grau observed that technological developments, like VR, bring us closer to “images as dynamic virtual spaces." [10] In fact, the key characteristic behind VR is that it is a real-time (dynamic) and multi-sensual (multi-cues) medium, where, thanks to a projection of convincing stimuli, an immersant (human or non-human) can feel a sense of presence inside a virtual space. Bolter and Grusin explicitly say "the responsive character of the environment, gives VR its sense of presence." [11] The presented examples show that VR environments are in fact zones of activity that simulate ontologies, create horizons of possibility – defined by affordances of systems that can deliver specific visual, auditory, haptic and data cues to the agents involved. The unique design affordances and constraints implemented in VR environments shape their status as cultural software that today mediates people's interaction with media and other people. Soon, as the Isaac example shows, they will also mediate human-non-human interaction and communication. Therefore, if we consider VR environments as media interfaces, we are getting access to yet another perspective for analysing different models of representing and accessing digital information in today's media ecology, populated both by human/non-human agents. References 1. Ivan E. Sutherland, “The ultimate display,” Proceedings of the IFIP Congress (1965), 506508. 2. David Berry and Michael Dieter, eds. Postdigital Aesthetics: Art, Computation and Design (Basingstoke: Palgrave Macmillan, 2015), 3. 3. Oliver Grau, Virtual art: From Illusion to Immersion (Cambridge, Mass: MIT Press, 2007), 202. 4. Voices of VR website, accessed August 15, 2018, https://bit.ly/2A7N44w [5] Nvidia website, accessed August 15, 2018, https://bit.ly/2pJv8r9 [6] Adrien Gaidon et al., Virtual Worlds as Proxy for Multi-Object Tracking Analysis (2016). [7] Princeton University website, accessed August 12, 2018. https://bit.ly/2vbr1YO [8] Jason Gregory, Game Engine Architecture (Boca Raton: Taylor & Francis, CRC Press, 2018), 9. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 121 Part II. Scholarly Abstracts [9] Alexander Galloway, The Interface Effect (Cambridge, UK: Polity, 2012), VII, 36. [10] Oliver Grau, VirtualArt: From Illusion to Immersion (Cambridge, Mass: MIT Press, 2007), 345. [11] Jay David Bolter, and Richard Grusin, Remediation: Understanding New Media (Cambridge, Mass.: MIT Press, 2003), 16. Biography Lukasz Mirocha is a PhD Candidate at SCM, CityU. He is interested in media aesthetics, design (particularly VR, AR, MR) and software studies. 122 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Part III Artistic project abstracts 123 SHAPES of the Future: When Art Machines Pass the Turing Test Terry Trickett Trickett Associates terrytrick@mac.com Abstract SHAPES provides opportunities for me to express the intrinsic relationship that exists between music and abstract imagery. In creating animated imagery which reflects, visually, the six movements of Bach’s suite for cello (arranged for solo clarinet), I produce apparently coincidental similarities between some of my computer-based images and the work of certain abstract artists. Why did it happen? For the moment, human hands guide both types of results; Art Machines cannot produce works of art unaided. But this situation will change; once a computer has passed the Turing Test, proving it has achieved the equivalent of human-like intelligence, it will become possible for the ‘consciousness’ of an Art Machine to match, or exceed, human sources of creative energy. Digital Simulacra Two years ago I produced SHAPES, a piece of Visual Music based on an arrangement of J S Bach’s first cello suite, which provided opportunities for expressing the intrinsic relationship between music and abstract imagery. Why did I do this? I think, perhaps unconsciously, I was reflecting the cybernetic idea that communication, for the most part, consists of harmony and counterpoint – simultaneous or parallel signals, images, tones, feelings, environmental factors that are continually blending and modifying each other. I’m seeing this from the point of view of a chamber musician when, during performances, I inhabit “an indissoluble environment of information.” [1] It’s a view I share with Stephen Nachmanovitch (like myself an artistmusician) whose mentor was Gregory Bateson, a key contributor to the science of British 124 cybernetics from the 1960s onwards, although for much of his life he worked in the USA. Where did my visual communication of musical harmony and counterpoint take me? The results surprised me; what I had discovered, as it turned out, were apparently coincidental similarities between my images and the styles adopted by nine well-known abstract artists. Somehow, through a process of continuous feedback, recursive self-modifying behavior and on-going interactional adjustment, I had produced artworks or, at least, taken in the artworks that others had made. Retrospectively, I recognize this as a cybernetic process where action is constantly conditioned by feedback, by the performance environment, and by finely differentiated systems of both the brain’s longterm and short-term memory. My aim had not been to imitate but, in fact, I found that I had created, almost inadvertently, a series of digital simulacra of specific abstract works of art. Figures 1 – 9, show the outcomes of SHAPES as they occur, either once or twice, in each of the six movements of Bach’s suite. Fig 1. The repetitive motif that occurs in the opening bars of the Prelude brings a photograph by Aleksandr Rodchenko to mind. Fig 2. At a midway point in the Prelude, my images jump ahead half a century to reflect the Op Art works of Victor Vasarely. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 SHAPES of the Future: When Art Machines Pass the Turing Test. Terry Trickett Fig 3. In the opening section of Allemande, I project 3D forms Fig 9. The Gigue generates a changing kaleidoscope of images on to a 2D surface. A method also used by Lybov Popova. where the geometry of Ben Nicholson’s work is evident. Fig 4. In the second part of the Allemande, rectilinear blocks of color produce effects similar to those of Giacomo Balla. Fig 5. Black and white patterns in the Courante conjure up the mesmeric effects of Op Art works by Bridget Riley. Fig 6. In the Sarabande, SHAPES of colour expressing the intensity of Bach’s music reflect those of Ivon Hitchens. Fig 7. As the Sarabande develops in complexity and texture, it’s abstractions similar to Paul Klee’s that begin to emerge. Fig 8. Minuets I & II, produce a spontaneous eruption of SHAPES resembling the designs of Hans and Sophie Arp. As Bateson opined in a lecture, Simple Thinking, given in 1980 (he died the same year), “creativity finds a simple pattern that can contain great complexities and contradictions without diminishing them.” [1] I like to think that just such a pattern enabled me to simulate, in my digital simulacra, the work of nine abstract artists without in any way belittling the originality of their images. Let’s call this, matching art with art but, even here, the cybernetic processes involved place it well beyond the capabilities of any unaided Art Machine. Such a task needs the conscious mind of an artist “looking from the inside in the knowledge that everything is new.” [2] No nonhuman mind can, as yet, achieve this level of objectivity. It will happen only after a computer has passed the Turing Test thus proving that it has achieved the equivalent of human-level intelligence. [3] Alan Turing’s own prediction for when this might occur was the year 2052. Significant inroads on this date have been predicted by Ray Kurzweil who believes that ‘singularity’ (where artificial intelligence triggers runaway technological growth) will be achieved by 2029! We’re almost in sight of a time when a machine with human intelligence can become a source of creative energy equal to anything that can be achieved by today’s artists. But, until that time, we cannot expect a machine to reach (or exceed) human levels of creativity. References 1. Stephen Nachmanovitch, "Bateson and the Arts", Kybernetes, 36, no. 7/8 (2007): 11221133, accessed 14 October 2018, https://doi.org/10.1108/03684920710777919. 2. Carl G. Jung, The Red Book, Edited by S. Shamdasani (New York: WW Norton & Co.). 3. Alan Turing, “Computing Machinery and Intelligence” (1950), Mind, Vol. LIX, Issue 236. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 125 Part III. Artistic project abstracts Biography Terry Trickett produces and performs visual music. The subjects he chooses range far and wide, often taking him into unchartered territory – places where, sometimes, he invades the realm of science and, with the aid of music, brings the worlds of science and art closer together. In creating a piece of visual music, his aim is to share and communicate an idea through a process that combines animated visual imagery with musical performance, usually on solo clarinet. 126 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 “Opinions” – Body Movements and Sound Yanbin Song Parsons School of Design songy941@newschool.edu Abstract "Opinions" is a project that collects hand movements in conversations and turns them into sounds. Under the context of having a conversation, this project creates an experience that enables people to pay more attention and appreciation to hand movements through converting movements into sound. A website is then produced collecting all the conversation footages and sound recordings, forming a space for hands/arms “expressing” their own opinions and perspectives with sound outputs. Many scholars and artists focus on the body as an instrument and the body as a support for expressing opinions. This proposal links these elements together and argues that body language/movements convey the same opinions for the mind and should be given as much attention as oral expressions, and this can be achieved by turning body languages to sound (which is the same form as oral expression – sound as output) based on the movements. Through this process, the audience will have more attention and appreciation towards their body movements. In the same way, this project may inspire one to think from another perspective and start to understand more about others’ perspectives. Research Body Movements The importance of body movements can be illustrated from several aspects. Using the body to express opinions in protests is one of the strongest cases. It is studied that using body gestures and postures to express political views supports the articulation of moral intuitions. [1] The power of body expression is that it is visual; bodies in protests stand for political opinions and can greatly affect the surrounding environments and represent solid opinions. The importance of the vulnerability of a body is also one of the reasons protests have always been a popular way of expressing opinions despite of its dangerousness. For example, during Tiananmen Square Protests of 1989, an autonomous man stood out using his single body and posture against the tanks. This body and its act was so powerful that it changed the whole consequence of the protest and has been spread as a historic moment. Demonstrating vulnerability of human bodies in a protest will inevitably trigger the self-reflections and rethinking of other bodies and parties in the protests. Parviainen calls such body behaviors as “resisting choreographies.” [1] More specifically, we use our body to demonstrate opinions in daily life. It is studied that humans tend to use not only verbal expression but and also “symbolic” expression, i.e. body languages such as hand gestures along with facial expressions, to deliver opinions and attitude towards the content of communication when having conversations. [2] However, it is also argued that such expressions are often paid little attention in themselves. During a conversation people generally focus most on the idea and content that are being communicated, thus, there lacks awareness of the iconic facial expressions and hand gestures used in conversation. [2] Body and sound A rising amount of applications that use the body as an instrument is emerging, especially in performance art. In the study of relations between body, instrument and technology, Schroeder emphasizes that in some interactive music technologies, movements by the body can be given back as sound. He suggests that the body serves as the most important role in a Proceedings of Art Machines: International Symposium on Computational Media Art 2019 127 Part III. Artistic project abstracts performance environment, it moves and also can listen. [3] Brown argues that body in motion should be converted to sound and reconnected to the ears. [4] In addition, Iddon in his study about body/instrument relations blurred the boundaries between the body of the performer and body of the instrument, providing a new direction which proposes the integration of performer and instrument for a musical entity. He also tends to blur the distinctions between man and machines, which brings Haraway’s cyborg model to the discussion. [5] Project Form Experience flow of participants In an indoor environment, one visitor at a time is asked to have a conversation with the author. A monitor detects the hand movements, meanwhile the conversation is recorded in audio-visual form with participants’ agreements (their voice and face are not published and only serve research and documentation purposes). After the conversation, the recorded footage focusing on the hand and the sound generated is played back to the visitors. This material is then uploaded to the project website where people can visit to listen to sound being produced by the other participants on various topics. https://songyanbin1996.wixsite.com/opinions Mechanism and setup Fig 1. “Opinions”, 2018, Yanbin Song, New Media Interactive Sound Art Project, Copyright Yanbin Song. An Arduino board is set up with two photocell sensors that detect the amount of light being blocked by arms during a conversation. The 128 data collected by the two photocells is sent to the software Max msp. Max generates sound according to the movements that is played and recorded inside the laptop using extension software, Soundflower. The resulting sound output has a high quality and is in the meantime non-interruptive to the conversation. A larger monitor screen is also set up for visitors to watch the recordings. Leap motion sensor replaces the Arduino set to serve as a sensor and to collect finger positions. Max msp is still used to generate the sound. References 1. J Parviainen, “Choreographing resistances: Spatial–kinaesthetic intelligence and bodily knowledge as political tools in activist work,” Mobilities, 5, no.3 (2010): 311-329. 2. J. Allwood, “Bodily communication dimensions of expression and content.” In Multimodality in Language and Speech Systems (Springer, Dordrecht, 2002), 7-26. 3. F. Schroeder, “Bodily instruments and instrumental bodies: critical views on the relation of body and instrument in technologically informed performance environments” (2006). 4. N. Brown, “The flux between sounding and sound: Towards a relational understanding of music as embodied action. Contemporary Music Review, 25, nos. 1-2 (2006): 37-46. 5. M. Iddon, “ On the Entropy Circuit: Brian Ferneyhough's Time and Motion Study II,” Contemporary Music Review, 25, nos 1-2 (2006): 93-105. Biography Yanbin Song is a designer and an adventurer. She studied in London UCL for a bachelor’s degree in Urban Planning, Design & Management, and is currently studying in New York, Parsons for the MFA program Design and Technology. Spending time in different cities and countries makes Yanbin a more global citizen and care more about lives and societies. She is attempting to make a social impact by bringing provoking thoughts through Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Opinions – Body Movements and Sound. Yanbin Song her multi-media yanbinsong.com and interactive works. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 129 Constellation — Call Your Personalized Constellation Constellation — 呼唤你的星系坐标 Nan Zhao 赵楠 New York University Shanghai nanzhao@nyu.edu Constellation — Abstract Call Your Personalized Constellation (Constellation — 呼唤你的星系坐标) is a website that generates a personalized constellation for you based on the Chinese Taoism BAGUA interpreting your birthday and voice. The idea behind the project is "Design for Everyone" and to "redefine the Relationship between individual life meanings and the vast universe.” It links generative arts, personalized design, and voice recognition together. It is also the process of exploring the beauty of nature through codes. To experience the journey, you first type in your name and birthday to see the initial star positions. Then you input your voice by reading a poem. The voice information will be recognized with voice recognition API. All the personal information is further conversed into a systematic, personalized constellation through WebGL technology, generative art algorithms, and BAGUA philosophy. You will see how your personal information generates a constellation system, step by step, through the well-designed user experience journey. Entirely different from those static twelve existing constellations in the world, this constellation is dynamite and feels alive. Fig 1. Title picture and marketing material of Constellation-Call Your Personalized Constellation, 2018, Nan Zhao, digital/print, Creative Commons Attribution-NonCommercial-ShareAlike 2.0 Generic license Heading Fig 2-3. Photos of an exhibition and people experiencing Constellation-Call Your Personalized Constellation, 2018, Nan 130 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Constellation – Call Your Personalized Constellation. Nan Zhao Zhao, digital/photo, Creative Commons NonCommercial-ShareAlike 2.0 Generic license Attribution- Fig 3. A collection of users’ constellations gathered from Constellation-Call Your Personalized Constellation, 2018, Nan Zhao, digital, Creative Commons Attribution-NonCommercialShareAlike 2.0 Generic license Project Details This is a work of screen-based generative art, programming art, reactive interface, and user experience design. The technology is WebGL, React.JS, Voice Recognition API, and HTML/CSS. The project is displayed at https://vimeo.com/269553307 and documented at https://nanzhaoportfolio.wordpress.com/portfol io/webgl-ued-constellation/ Biography Nan Zhao is an interaction designer and a creative technologist who just graduated from Interactive Media Arts, New York University Shanghai. Nan’s practice ranges from UX design, algorithmic arts, and interactive installations. She is driven by the motivation of exploring interaction design, new media, and arts. Offering people with delightful experiences is her dream. Her portfolio website is here. She is currently working as an experience designer at HUAWEI User Experience Design Department in Shanghai. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 131 The Dancer in the Machine Simon Biggs University of South Australia Simon.Biggs@unisa.edu.au Sue Hawksley Samya Bagchi Mark D. McDonnell Independent dance artist sue@articulareanimal.org University of Adelaide samya.bagchi@adelaide.edu.au University of South Australia Mark.McDonnell@unisa.edu.au Abstract The title 'The Dancer in the Machine' evokes Gilbert Ryles critique of René Descartes mindbody dualism as the "ghost in the machine." [1] Ryle argued that Cartesian dualism depends on a model of the body-mind relationship that posits the mind as a 'ghost' within, or 'puppeteer' of, the physical body. Ryle's is an embodied concept of cognition, where agency is considered enacted not from a central control system but as distributed, akin to what Gregory Bateson subsequently described as an "ecology of mind". [2] In the recent artistic project, “Double Agent,” the authors have been exploring dual modalities of agency in the moving body. “Double Agent” employs machine-learning and the computational representation of human movement alongside algorithmic interaction with, and responses to, live human movement. [3] Double Agent is an interactive augmented environment where people (interactors) physically interact with a virtual “agent” within a large-scale, three-dimensional projection. The “agent” is an emergent phenomenon determined by the behavior of numerous small invisible, virtual elements that are both drawn to and repelled by the movement of human bodies in the installation space. The “agent” is formed from the totality of this behavior as a complex three-dimensional visual structure that is both tensile and fluid. Interaction with the “agent” encourages exploration by interactors 132 of the system's tensional polarity and the sense of physical extension it allows. A novel innovation in Double Agent, developed through a collaboration between artist Simon Biggs, computer scientists Mark McDonnell and Samya Bagchi, and dance artists Sue Hawksley and Tammy Arjona, is a software agent embedded within the system that has learned how to dance. Fig 1. Double Agent, 2018, Simon Biggs, interactive installation, Museum of Discovery, Adelaide, Australia. The title Double Agent evokes the two-fold agency of the work, wherein a computationally generated agent interacts with a live interactor whilst another computationally generated agent simultaneously 'dances' based on what it has learned. Employing over 8 hours of recorded dance data, acquired through the live motioncapture of two dancers improvising within the work, the software agent has learned to improvise dance movements in response to the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Dancer in the Machine. Simon Biggs, Sue Hawksley, Samya Bagchi, Mark D. McDonnell live actions of interactors. The software agent moves in ways similar to the dancers but also possesses a host of novel moves. This novelty could be considered a form of creative agency emergent from the machine-learning process. Double Agent employs a Long Short-Term Memory Recurrent Neural Network (LSTMRNN). [4] LSTM-RNNs allow computational systems to evolve models of complex behavior in an unsupervised manner, without reference to pre-existing datasets. The system learns by identifying patterns in the data in what could be conceived of as an idealized non-verbal or nonlinguistic experiential framework. Such computational systems can acquire the capacity to generate novel data-sets that follow similar patterns; in the case of Double Agent, humanoid movement data replicating similar, but not identical, behavior as found in the original motion-capture data. In Double Agent, we witness the emergence of a software generated co-interactor, that cohabits a virtual installation space with human interactors, contributing to the collective construction and experience of the work. This software agent is not unaware of its immediate environment. The agent monitors the activity of human interactors and conditions its own behavior in response, as an inverse correlate: the more active the human interactors the less active the software agent, and vice versa. Here the installation, the software, computers, sensors and interactors (both human and computer-generated) function as a contingent assemblage that, from moment to moment and state to state, instantiates itself as a dynamic heterogeneous subject. Double Agent raises questions about the role of agency within complex distributed systems, whether human, machine or hybrid. In Double Agent there is no “dancer in the machine.” The system as a whole, including the machine and the human, is the dancer. References 1. Gilbert Ryle, The Concept of Mind (London: Hutchinson, 1949). 2. Gregory Bateson, Steps to an Ecology of Mind (Chicago: University of Chicago Press, 1972). 3. Simon Biggs, Double Agent (Adelaide: http://littlepig.org.uk/installations/doubleagent/i ndex.htm, 2018), accessed July 20, 2018. 4. Sepp Hochreiter & Jurgen Schmidhuber, Long Short-Term Memory, in Neural Computation 9, no 8 (1997). Biographies Simon Biggs (b. 1957) is a media artist, writer and curator. His work has been widely presented in international exhibitions and festivals and he has spoken at numerous conferences and universities. Publications include Remediating the Social (ed, 2012), Autopoeisis (2004), Great Wall of China (1999), Halo (1998), Magnet (1997) and Book of Shadows (1996). He is Professor of Art at the University of South Australia and Honorary Professor at the University of Edinburgh. http://www.littlepig.org.uk Sue Hawksley (b. 1964) is an independent dance artist and artistic director of articulate animal. Her practice is concerned with embodiment, presence, improvisation, ecology, and technology. Her work has been presented in theatres, galleries and academic contexts internationally. Sue has previously performed with Rambert Dance, Mantis, Scottish Ballet and Philippe Genty. She holds a PhD from the University of Edinburgh. http://www. articulateanimal.org Mark McDonnell (b. 1975) is Associate Professor and Director of the Computational Learning Systems Laboratory at the University of South Australia. His interests lie at the intersection of data science, electronic engineering and neuroscience, including machine learning applied to computer vision, autonomous decision making, and sequence recognition and the computational and mathematical modeling of learning in the brain. Samya Bagchi (b. 1989) is currently completing his PhD in Computer Science at the Adelaide University. His research interests are in deep-spiking neural networks and eventdriven computing. Prior to this Samya has been an entrepreneur and worked with Siemens Proceedings of Art Machines: International Symposium on Computational Media Art 2019 133 Part III. Artistic project abstracts Research after receiving his M.Tech in I.T. from IIIT Bangalore in 2013. 134 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 “I’m Evolving into a Box:” The Paradoxical Condition in AI. Wei-Yu Chen The Department of New Media Art, Taipei National University of the Arts Email: fredy0219@gmail.com Abstract “I’m evolving into a box” is an iron box with an irrelevant mechanical arm and brain. The mechanical arm has been manufactured with aluminium pipes and two servo motor. Using Raspberry pi as brain which runs NEAT algorithm in real time, the iron box, just like a newborn life, learns how to use the unknown arm. The whole exhibition period demonstrates how artificial intelligence drives daily objects. Through this process, the installation transmits a paradoxical condition. Video Link : https://youtu.be/P6GfyQsixwE Introduction The main discussion of this artwork is about the transition period in the evolution of artificial intelligence. What can we see in this transition period? Nowadays, lots of artificial intelligence products have been created. Many incredible research projects and developments, like developed by DeepMind, robot dogs by Boston Dynamics, and other great emerging technologies have shown us some new dimensions of technology. However, they are not the final goal of artificial intelligence. They are simply a transitional period in artificial intelligence evolution. What can we see beyond those technologies? Engineers try to make things work like biological entities, but it doesn’t seem to be so simple. Actually, engineers sometimes create artifacts with a status that exists between biological and non-biological and this status can feel strange. According to the “Chinese Room Argument,” the logic of science and technology is contradictory when it comes to a final hypothesis about AI. [1] Therefore, I try to demonstrate this subtle status in my work. When I created this work, I was wondering what would happen if an artificial intelligence algorithm was installed into a lifeless object? After simulating what it would look like, the answer was unimaginable, and that is why the algorithm works. The end result was that I combined an iron box and a machine learning algorithm: training a box to act like a box (Fig. 1) Fig. 1 I'm evolving into a box., 2017,Wei-Yu Chen, Copyright © 2017 Wei-Yu Chen All rights reserved. Machine Learning Algorithm There have been hundreds of types of machine learning algorithms. In this work, I try to find the algorithm that is closest to the theme of “biological evolution,” rather than mathematical feasibility. What I was searching for in this algorithm is the essence of complex operation. Neuro Evolution of Augmenting Topologies (NEAT) algorithm is composed of a genetic algorithm and a neural network (NN) algorithm. [2] In the original NN, the neurons are fully connected, and compute in a single topology. The genetic algorithm imitates the concept of cell evolution, like crossover, reproduction and mutation, trying to keep better genes. As the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 135 Part III. Artistic project abstracts combination of the above two algorithms, NEAT algorithm considers multiple NN topologies as genomes. Through crossover, reproduction, mutation in each generation, those well-behaved topology will keep evolving. System Architecture The system was constructed on Raspberry Pi, and NEAT algorithm was implemented by Python. The outputs and inputs for the machine learning algorithm were the rotation angle of two servos and the distance of installation movement calculated by two rotary encoders (See Fig. 2). foundation of the algorithm, he seeks to delve into the essence of technology and attempts to find some subtle phenomena within it. He uses human–computer interaction and creative coding to intervene in daily space, in order to explore imaginations of the future in everyday reality. Fig 2. The system is constructed on Raspberry Pi References 1. Searle, John. R., “Minds, brains, and programs”, Behavioral and Brain Sciences 3, no. 3 (1980): 417-457. 2. Kenneth O. Stanley and Risto Miikkulainen, “Evolving Neural Networks through Augmenting Topologies,” Evolutionary Computation, 10, no. 2 (2002): 99-127. Biography Wei-Yu Chen was born in 1993 in Taipei, Taiwan. His artworks derive from the exploration of Computer Science and Engineering, and focus on the contradictory situation of how technology affects the human environment. Extending the theory and 136 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Volumetric Black Triton Mobley University of Southern California, Media Arts + Practice triton.mobley@gmail.com Abstract I have given considerable thought to the image of black bodies in cinema and their reproductions across digital media in general. I am specifically concerned with the digital manifestation of the varying shades and skin tones of blackness represented in everyday life and whether cinematic productions, and the technological apparatus employed to encode the images, offer an accurate representational portrayal. This research series reimagines the question and challenges the trite charge of oversimplification made against those who claim that technology is inherently biased. And in this case, I am referring specifically to racial bias. Instead, it asks whether technology has inherited its bias from the homogenous workforce that created and uses it. Coded #000000 [Black] is a java programmed image processor built in Processing. The code analyses video pixels retrieving the W3C's established seventeen colors of hexadecimal browns, magnifying the pixels by appending them within the frame of the video for closer comparison. The program is in its third working iteration currently analyzing a 26-minute video of prerecorded black and brown skin tones. The Coded #000000 project developed from a series of inquiries stemming from my dissertation research that examines socioeconomic disparities and racial representations on both the front and back– ends of technology. Created as a practical exercise for furthering theoretical research, Coded #000000 is part of a wider research project, Volumetric Black, which imagines digital representations of equity both as a speculative history and a technologically obtainable future. This research investigates the history of the chemical, mechanical, and digital productions of black skin tones in cinema and digital media, reexamining the histories of media–technological bias and discrimination. The fourth iteration of Coded #000000 [Black] currently in progress, will digitally reimagine an episode of Friends, the popular 1990’s American television program, with an all “#000000” cast. This project functions as part of an interventionist practice continuum that aims to foster new conversations on the future of computational media design and digital technology. These are the questions, as researchers and technologists we must not only ask ourselves, but thoughtfully respond to: Will the digital media future have diversity coded into its systems? Can we debug existing digital systems and platforms of their inheritance and perpetuation of socioeconomic and racial bias? Volumetric Black Is it too forward thinking to imagine a mediated experience that could represent and telecast the black body in its fullness? Is it a futile exercise to wonder what the dominant visual culture and its corresponding industry could have been if black people had had the opportunities to define the production of cinematic experiences? To have been present in the process of the mixing of chemicals that brought us the product of Proceedings of Art Machines: International Symposium on Computational Media Art 2019 137 Part III. Artistic project abstracts celluloid that we know today and its photosensitive reactions to light. Could we envision the birth of a material substrate developed in place of, or simultaneously alongside photosensitive film stocks, a celluloid that embellishes the representation of black bodies in low light? A material substrate that distinguishes between the many hues of blackness and registers them as seen in true life. What effect would an alternate history of this magnitude have on the way we see ourselves? I can’t stop imagining the endless possibilities of a visual richness that could have been. “When I meet a German or a Russian speaking bad French I try to indicate through gestures the information he is asking for, but in doing so I am careful not to forget that he has a language of his own, a country, and that perhaps he is a lawyer or an engineer back home. Whatever the case, he is a foreigner with different standards. There is nothing comparable when it comes to the black man. He has no culture, no civilization, and no “long historical past.”—Whether he likes it or not, the black man has to wear the livery the white man has fabricated for him.” [1] This is by no means a suggestion to reduce the vibrant visual culture that has been established by the black diaspora. Not at all. This is a speculative desire to see what the diasporic aesthetic might have been if it wasn’t largely predicated on the remixing and improvisations of the fragments from a euro-aesthetic left behind in the new world. Post-Cinematic Blackness In the early 1900’s Oscar Micheaux, a filmmaker and producer of “Race Films”, created and operated the Lincoln Motion Picture Company. [2] With his eyes set on creating black films for black audiences, starring all black casts, Micheaux made a name for himself throughout segregated black communities and urban centers. He even made a name for himself with many of the states' film advisory boards for the overt 138 racial themes depicted in his films. Although Micheaux was successful in constructing his own narrative aesthetic for black productions—the filmic resources that he employed for these productions were the same mechanical cameras and light-sensitive film stocks used in this early time period, devices that were never intended to film and expose black bodies as they live. In my expansion on the speculative possibilities of a post-cinematic blackness, I lean on Tanizaki’s In Praise of Shadows (1933) among others. [3] This is a document whose pointed gaze appears determined to disrupt the gilded plumage of a western aesthetic through a mix of cultural sensibilities and impish impulse. References 1. Frantz Fanon, Black Skin, White Masks trans. Richard Philcox (New York NY: Grove Press, 2008), 17. 2. Mary Carbine, “The Finest Outside the Loop: Motion Picture Exhibition in Chicago's Black Metropolis 1905–1928,” Camera Obscura: Feminism, Culture, & Media Studies Vol. 8 No. 2 (1990) 23. 3. Jun’ichiro Tanizaki, In Praise of Shadows, trans. Thomas J. Harper & Edward G. Seidensticker (Sedgwick ME: Leete’s Island Books, 1977). Biography Triton Mobley is an artist, educator, and researcher in new media. His research and practice studies the socioeconomic disparities of emergent technologies on marginalized communities. Triton’s interventionist and guerrilla campaigns have been exhibited at Art Basel, Miami, Boston, New York, and Japan. For over 15 years, Triton has been a new media educator, advancing its technological modernization. In 2014 he was awarded a S.T.E.A.M education research grant to the Museo Nazionale Scienza e Tecnologia Leonardo da Vinci in Milan. He was recently invited to Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Volumetric Black. Triton Mobley give artist talks at UCLA and the AADHum Conference in Maryland. Triton received an MFA from RISD in Digital+Media. He is currently a PhD candidate and Annenberg Fellow in Media Arts + Practice at USC. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 139 AIBO – Artificially Intelligent Brain Opera – An Artistic Work-inProgress Rapid Prototype Ellen Pearlman RISEBA University/Parsons/New School University Abstract Cloud-based analytic engines for emotionally intelligent artificial intelligence like Google API, IBM Watson, and others function through semantic analysis of speech-to-text input. They apply weighted values based on magnitude or strength of an emotional statement, and score an overall emotional analysis of the statement’s positive, negative, or neutral qualities. These types of analyses can also be used by both speech to text and text to speech specialized chatbots, and incorporated into analytic engines tasked with making critical decisions on customer service, healthcare, jurisprudence, social sorting, employment, and migration among others. DARPA and Facebook Building 8 are developing initiatives for semantic analysis of thoughts in the brain that interact directly with computers and other devices that also rely on specialized types of semantic analysis. [1][2] This AIBO work-in-progress opera depicts a proof of concept, initial rapid prototyped interaction between an emotionally intelligent artificial intelligence entity powered by machine learning and the human brain. It represents the sterility of algorithmic decisions versus a sentient human being’s emotions, with a subject’s brainwaves visible on their body highlighting inherent tensions between implicit mathematical analysis, and complex human irrationality. Rapid Prototyping Proof of Concept Over the course of four Saturdays an Art-AHack™, rapid prototyping collaboration was held, focusing on emotionally intelligent artificial intelligence and EEG wireless brain computer interfaces. [3] Two main aspects of 140 the AIBO were developed. The first, written in Python software translated a person’s speech into text that underwent emotional semantic analysis in the Google Cloud API, returning values of magnitude and score. Emotional sentiment analysis looks at all the input text in a sentence and decides the strongest emotion in order to determine if it is positive, negative or neutral. It does not indicate subtle differences between an emotion like “happy” and “joyful,” determining both to be “positive.” Neutral scores are texts with low emotion, or conflicted emotions that cancel out their respective weighted values resulting in a reading of 0. Magnitude is defined as the strength of an emotion, either positive or negative between 0.0 and +infinity that is not normalized. Score is then calculated as the overall emotion of a statement, positive or negative. [4] Fig 1. AIBO Proof of Concept, 2018, Brainwave headset/LED bodysuit performance demonstrating the relationship between the brainwave of attention and a positive emotional sentiment analysis AI. Copyright Ellen Pearlman For the proof of concept performance a subject wore a NeuroSky brainwave headset, and a LED display of an oversized necklace hooked up to an Arduino, which received data from a Proceedings of Art Machines: International Symposium on Computational Media Art 2019 AIBO – Artiﬁcially Intelligent Brain Opera – An Artistic Work-in-Progress Rapid Prototype. Ellen Pearlman NeuroSky headset. Simple questions were asked about feelings such as “What is something you hate?” or “What is something you love?” The verbal response lit up with the colors aqua for the brainwave of meditation, and magenta for attention. Concurrently the speech-to-text semantic analysis function analyzed the reply, and it was projected as a java script graphic also connected to their brainwaves. The projected graphic displayed attention as a magenta lattice and meditation as an aqua lattice. The size of the graphic would change according to the emotional score of the subject’s response. A positive response would display a large lattice. Negative scores would display a small lattice. The change in brainwaves and the weighting of emotional scoring occurred simultaneously. Fig 2. AIBO Flow Chart, 2018, brainwave headset/LED Bodysuit, Copyright Ellen Pearlman Conclusion A proof-of-concept rapid prototype was built in just four days to demonstrate the relationship between a subject’s brainwaves consisting of attention and meditation, and an analysis of an emotionally intelligent artificial intelligence parsing of a verbal statement from speech to text using a NeuroSky headset, an LED necklace and the Google cloud-based API. This prototype is the first step in developing AIBO, an artificially intelligent emotionally intelligent brain opera between a human being and an algorithmic machine learning entity. This sample demonstrates conclusively that a further build out is possible, including a feedback loop between various EEG brainwave states; an artificial body of light; speech-totext, and text to speech customized repositories; and an AI analysis in the computing cloud. References 1. Eliza Strickland, Director of Typing-byBrain Project Discusses How Facebook Will Get Inside Your Head, IEEE Spectrum: https://spectrum.ieee.org/the-human-os /biomedical/bionics/facebooks-director-oftyping-by-brain-project-discusses-the-plan. 2. National Research Council, Emerging Cognitive Neuroscience and Related Technologies: https://www.nap.edu/catalog /12177/emerging-cognitive-neuroscience-andrelated-technologies. 3. Art-A-Hack, Special Edition 2018: https://artahack.io/projects/sentimental-feelingsecond-skin/. 4. Google, Natural Language: https://cloud.google.com/natural-language /docs/basics. Biography Ellen Pearlman, an Assistant Professor, Senior Researcher at RISEBA University, Latvia and faculty at Parsons/New School University, New York, is a new media artist, critic, curator and writer. She is Director of ThoughtWorks Arts, President of Art-A-Hack ™ and Director of the Volumetric Society of New York. This prototype was made with the assistance of programmers Sarah Ing, Doori Rose, Danni Liu, and LED necklace builder Cynthia O’Neill. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 141 Artificial Digitality Kuldeep Gohel American Museum of Natural History, NYC Fidelity Investment, NJ kuldeepgohel.com Abstract This paper is about the technical process and artistic intent of a musical album co-led by a human and A.I. The project aims to make several compositions. The album begins with a composition that is generated by me alone. The compositions that follow are co authored by an open source neural network and me. The NN is trained by me, using the mathematical pattern from my compositions. The album ends with a composition that is completely generated by the neural network. The goal of the project is to express the rise of AI in a musical way and speculate on the future of A.I. I use music, mathematics, and machine learning to create a musical story. It aims to question the future where automation takes over human labor in various fields including creative areas. [1] Context The project is driven by two forces: the love for music composition and A.I. Despite constant efforts to make the first music album, I have not been able to do so because of limited time, and the lack of collaboration and feedback. Five years have passed by where I have constantly evolved but a concrete output is absent. In these five years of music learning I have been involved in emerging technologies and art. I then heard about A.I. and fell in love with an idea that a machine can replicate and help me to produce music that I am unable to give time to. I consider machine learning as a tool to replicate my ideas and generate the other that I can share my soul with. I aim to generate an album with machine learning to make music and compositions that are used as a medium to express the rise of A.I. 142 Along with this I aim to speculate upon the future of A.I. and potential A.I. assistance. [2] Process The process involved an analytical approach to the art of music making. I analyzed the process of making music, then converted the process into data; which can be used to generate a system that will mimic my music making. I started by dividing the music album into three compositions. The content of each composition draws inspiration from the story of the development of A.I. to date. The story involves the “World before A.I.,” “Current World” and “Future (Singularity).” For “Current World” and “Future (Singularity),” C# Melodic Minor (111 bpm) and G# Hungarian Gypsy (128 bpm) scales were used to emote intelligence, while the “World before A.I.” used C Natural Minor to narrate Sentimental and Tragic. Each of the compositions were generated from the total of 15 keys offered in two octaves of their scale. First Composition Composer: Only human composer Scale and bpm: C Natural Minor (90 bpm). Chord, Melody: The very first composition was created by me with no help from the A.I. The data extracted from this composition was then used to train the three NN and get the A.I. assisting me in the other compositions. The three NN were assisting me with the chord sequence, the note sequence for melody and the time between each note in the melody. [3] Second Composition: Composer: Human and A.I. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Artificial Digitality. Kuldeep Gohel Changes: Scale and bpm: Altered Scale: Csharp/D-flat melodic minor. (111 bpm). Chord: Chords played alternatively by me and the A.I. The first neural network assisting me with the chord sequence. Melody: Note Progression: First and second note played by human, the second and third notes played by the neural network, and so on. Time between the note’s progression: The third neural network is used to get the time intervals between the notes. So the time interval between the 1-2, 2-3 note is decided by the human; then this data is fed to the neural network to get the time interval between the 3-4 and 4-5. And so on. Third Composition: Composer: Only A.I. Changes: Scale and bpm: Hungarian Gypsy Scale: TSTSTTS: G #. (128 bpm). Chord: Feeding value of first chord played by neural network in the neural network to suggest the next chord. And so on. Melody: Note Progression: Feeding the first two notes played by neural network in the neural network to suggest the next two notes & so on. Time between the note’s progression: Simultaneously with the notes progression, the third neural network is used to get the time intervals between the notes. So the time interval between the 1-2, 2-3 note is decided by the neural network and fed to the neural network to get value of the time interval between 3-4, 4-5. And so on. Biography Kuldeep Gohel is a self-taught musician, creative technologist and a 2018 graduate of Design and Technology (MFA) from Parsons School of Design, NYC. This project was part of his Master’s thesis involving Machine Learning and Music, done under the guidance of Sven Travis and Louisa Campbell. Before Parsons, he did his bachelors from NID, India in Exhibition Design, along with a semester exchange at RMIT, Australia; where he got major exposure to the power and various faces of weaving art, design and technology. He has made art and design shows in Europe, Australia and USA since 2010. And along with this, he has been an educator. His journey as an educator began from HS OWL, Germany in 2015, and is currently working as a Digital educator at AMNH, NYC. Along with this he works at Fidelity Investment, NJ as an UX Designer. All Compositions soundcloud.com/psychoactive13/sets/ad-1 Full Documentation kuldeepgohel.com/artificial-digitality References 1. E. Alpaydin, Machine learning: The new A.I. (Cambridge, MA: MIT Press, 2016). 2. S.J. Russell and P. Norvig, AI: A Modern Approach. (Boston: Pearson, 2016). 3. J. Perricone, Melody in Songwriting: Tools and Techniques for Writing Hit Songs (Boston: Berklee Press, 2007). Proceedings of Art Machines: International Symposium on Computational Media Art 2019 143 Specimens of the Globe: Generative Sculpture in the Age of Anthropocene SHIN, Gyung Jin School of Creative Media, City University of Hong Kong gjinshin@gmail.com Abstract Specimens of the Globe is a project that converts statistical data collected from the Internet into three dimensional sculptural objects through a generative procedure and digital fabrication. Referring to a traditional casting method, which ironically results in replicas slightly different from each other, I aim to redesign a replica-making system that creates constant differences out of the same original version. I 1) collect the statistical data about current global issues, including war, violence, poverty, terrorism, famine and the environment, from wikis or the intelligence agencies on the Internet (e.g. Wikipedia and the CIA’s World Factbook); 2) relate this data to a list of key cities; and 3) digitally fabricate geometric shapes that point to the cities on a virtual globe; in order to 4) physically reproduce the data with semi-transparent material. The outcomes of the system generated from the original, a 30cm diameter plastic globe, are crystal-like abstract pieces of sculpture in various geometrical shapes and colors. By crystallizing the issues that we do not insist on knowing about or which are otherwise overlooked, this research-based, interdisciplinary project aims to redefine the concept of “sculpture” under the influence of the post-internet environment and to question the societal role of art in response to the age of Anthropocene. 2. Jacques Rancière. The Politics of Aesthetics: The Distribution of the Sensible, trans. Gabriel Rockhill (London: Continuum, 2004). 3. The World Factbook, Central Intelligence Agency, CIA website (the United States), https://www.cia.gov/library/publications/resour ces/the-world-factbook/index.html. Biography Gyung Jin Shin is an artist, researcher, and PhD candidate in the School of Creative Media, City University of Hong Kong. She received a MFA from Columbia University in 2010 and a BFA from Seoul National University. Her art work has been exhibited in the US, Europe, and Asia. Her research interest includes critical theory, art’s social engagement, aesthetics and politics, contemporary art and new media art, postmedia discourse, post-internet art, and media archaeology. References 1. Alan Dorin, et al. "A framework for understanding generative art." Digital Creativity 23, nos. 3-4 (2012): 239-259. 144 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Machine Learning for Performative Spaces Alex Davies, Brad Miller, Boris Bagattini UNSW Australia, Art & Design alex.davies@unsw.edu.au, brad.miller@unsw.edu.au, boris@soma-cg.com Abstract This paper discusses the development of a large scale permanent public interactive media platform situated in Southport, Queensland, Australia, and specifically, how machine learning has been implemented to enhance and co-create the delivery of live performance presentations by artists at the site. The media façade is located at the Telstra Network Exchange in Southport Queensland at a busy public intersection. It comprises of 8 audio-visual displays, live camera inputs, computer vision hardware and LED lighting. The project aims to create a playful activated urban space and provide circumstances and infrastructure to foster and support live performance in the city. [1][2][3] Fig 1. Telstra Interactive Hub Southport 2018, Concept Drawing. To this end, we see the media façade as an interactive hub designed to encompass several modes of operation including interactive games, embodied music composition tools, and a performance mode in which the hub acts as a sophisticated electronically mediated stage that offers street performers and buskers a dynamic lighting and visual accompaniment to their shows. The design approach was to consider these as distinct goals. Firstly, the design of the space created a flexible platform for all interaction modes via transparent so-called Natural User Interfaces including 14 camera’s for computer vision and image acquisition, and 8 distributed microphones. This hardware array supports rich acquisition of overlapping data at depth. [4] [5] Secondly, machine learning was used as a way to address the challenge of creating a system that coherently supports the activities of a wide spectrum of unknown future performers utilizing the site. Rather than a generalist approach to creating visual and lighting content, machine learning was chosen to tailor the lighting and video content to the specific characteristics of the individual performer, and as the interactive hub is a permanent public art work, the more performers the work is exposed to over time, the more sophisticated and refined the system will become. [7] [8] [9] Live performance mode uses an implementation of TensorFlow within the Touchdesigner software environment to classify and choose procedural parameters that drive a generative visual and lighting environment that is displayed on 8 screens and a lighting system that spans the 19 meter ‘stage’ area. The system has been initially trained prior to the launch through the creation of a library based upon audio of buskers and street performers from YouTube. This data was gathered to create a base library of genres to construct the architecture of the system. Following this, all Proceedings of Art Machines: International Symposium on Computational Media Art 2019 145 Part III. Artistic project abstracts live performances on site will be recorded and converted into soundprints. Once the performance has been labeled, the Tensorflow is updated to extend its knowledge of a current category or to integrate a new category. In this way each performance improves the system creating a tailored reactive installation that continually improves over time. References 1. Luke Hespanhol and Martin Tomitsch, “Strategies for Intuitive Interaction in Public Urban Spaces.” Interacting with Computers. doi: 10.1093/iwc/iwu051, 2015. [2] Luke Hespanhol, Martin Tomitsch, Kazjon Grace, Anthony Collins, Judy Kay, “Investigating intuitiveness and effectiveness of gestures for free spatial interaction with large displays” PerDis '12 Proceedings of the 2012 International Symposium on Pervasive Displays, Article No. 6. [3] Niels Wouters, John Downs, Mitchell Harrop, Travis Cox, Eduardo Oliveira, Sarah Webber, Frank Vetere, Andrew Vande Moere “Uncovering the Honeypot Effect: How Audiences Engage with Public Interactive Systems.” Proceedings of the 2016 Conference on Designing Interactive Systems (DIS '16). [4] Joerg Muller, Robert Walter, Gilles Bailly, Michael Nischt, Florian, “Looking Glass: A Field Study on Noticing Interactivity of a Shop Window,” Alt. CHI’12, May 5–10, 2012. [5] Jörg Müller, Dieter Eberle, Konrad Tollmar, “Communiplay: a field study of a public display mediaspace,” CHI'14 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1415-1424. participatory urban media architecture, software development and expanded photography. Boris Bagattini is an Artist and Programmer. He has directed and led visual effects teams on a myriad of film, TVC and broadcast projects. Since 2011 Boris has been working primarily in large and small scale theatre, projection mapping, event video, live television and interactive artworks. In the last two years he has been engaged as Screen Graphics and InCamera Interactives Programmer for Ridley Scott’s Alien Covenant, Guillermo Del Toro’s Pacific Rim Uprising, and the DC Comics production of Aquaman. Biographies Alex Davies is a media artist and Scientia Fellow at the SW, Australia Creative Robotics Lab. His practice spans a diverse range of media and experiments with interaction, technology, perception, mixed reality and illusion. Brad Miller is a visual artist, curator and academic who works with technology and networks to create moving pictures and largescale interactive installations about memory and time in an exploration of identity. His artistic practice bridges the fields of media arts, 146 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Penelope Alejandro Albornoz University of Sheffield aalbornozrojas@sheffield.ac.uk Roderick Coover Scott Rettberg Temple University roderick.coover@temple.edu University of Bergen scott.rettberg@uib.no Abstract Penelope is a combinatory sonnet generator film based on Homer’s The Odyssey that addresses themes of longing, mass extinction, and migration, which are not simply relegated to the past. [1] Re-combinations of lines of the poem, video clips, and musical compositions produce a different version of the project on each run. Penelope was co-produced by Alejandro Albornoz (Sound), Roderick Coover (Video), and Scott Rettberg (Text and Code). Other contributors to the project include Kristiansand Symphony Orchestra oboist Marion Walker, voice actress Heather Morgan, and actors Helen Amourgi, Kostas Annikas Deftereos, and Sophia Kagadis in non-speaking roles. The video and the text were developed by Coover and Rettberg during 2017 residencies at the Ionian Center for Arts and Culture in Kefalonia, Greece. Kefalonia is reputedly the historic home of Homer. situation of the narrative is that of Odysseus’s wife Penelope from Homer’s epic, left behind on Ithaca for many years when Odysseus went off to fight in the Trojan wars and struggled to return. While Odysseus is off on his heroic adventures, Penelope must struggle to fend off the advances of a band of parasitic suitors vying for her attentions, hand, and Odysseus’s throne. She distracts these suitors through subterfuge, delaying the arrival of the day when she will be forced to choose another to replace Odysseus, even as she struggles to believe that he will in fact return to rule at her side. Penelope is able to delay the decision of choosing a new mate by making them wait before competing for her hand until she has finished weaving a tapestry. Each day she can be seen working to complete it, but each night she returns to the loom to unweave the threads from the day before. Although it is set within a particular Homeric frame, the human concerns and emotions involved in Penelope’s story are essentially universal ones of longing for loved ones, doubts for the future, struggle, loss, and perseverance in the face of adversity. These are themes which apply equally well in contemplation of contemporary struggles with catastrophic climate change, extinction, and mass migration. Penelope filters Penelope’s story from the epic through the form of the Shakespearean sonnet. Pulling from a database of ten-syllable lines primarily written in iambic pentameter, the computercode-driven comb-inatory film can produce millions of variations of a sonnet that weaves and then unweaves itself. The program writes 13 lines of a sonnet and then reverses the rhyme scheme at the center couplet. The Fig 1. Penelope, 2018, image © 2018 by Roderick Coover, CRchange. The Combinatory Poetics of Penelope Penelope engages with ancient narratives and poetic forms, and contemporary technology and poetic methodologies. The central Proceedings of Art Machines: International Symposium on Computational Media Art 2019 147 Part III. Artistic project abstracts program thus produces Shakespearean sonnets that weave and then unweave themselves according to the same rhyme scheme, resulting in a 26-line poem. Penelope’s generativity is not based on the operations of a complex AI or neural network, but instead hearkens back to early forms of combinatory poetics. The algorithms here are not generating the lines from scratch or building them on the basis of machine learning, but instead are recombining texts and media elements in an aleatory but formally structured manner. An important inspiration for Penelope is Oulipian writer Raymond Queneau’s Cent mille milliards de poèmes (One Hundred Thousand Billion Poems), a book of ten pages of a 14-line sonnet, with each line cut as a strip, so that the reader could substitute a line in any given position of the poem and still read a sonnet that worked metrically and semantically, resulting in 1014 poems. [2] Penelope is similarly factorial, if using a slightly more complex algorithm that results in a more varied end-rhyme scheme in successive runs of the work. Penelope is programmed to produce three 26-line iterations of the combinatory sonnet without repeating a line. The system produces each sonnet as an audiovisual composition before printing it to the screen. Combinatory Sonnet, Film, and Score Penelope not only generates combinatory sonnets but also recombines videos by Roderick Coover and the sound compositions by Alejandro Albornoz in a parallel algorithmic structure. Borrowing from traditions in avantgarde cinema and digital musical composition as well as experimental writing practice, the collaborative project thus brings three strands of practice together in one protean digital work. Imagery The imagery for Penelope was filmed in and around islands of the Ionian Sea. The cinematography and art direction follow two primary themes. Images from the natural landscape evoke ancient and enduring elements of the Odyssey's sensorium, tying the present to the past in a cyclic expression of time. This 148 includes human relationships to the land, weaving, storytelling, olives, seafaring and goats described by Homer that continue today. Other images illustrate human use and abuse of the natural landscape, recasting enduring poetics in relation to contemporary crises of environmental destruction, waste, and mass extinction. Loss and memory in collective consciousness is also expressed through visual forms of underwater imagery of Roman shipwrecks, above ground images of earthquake destruction and ancient open tombs. Generative Audio Composition The sound composition was addressed under the procedures of acousmatic music tradition, which in turn continues the aesthetic guidelines and techniques of musique concrète; this background involves the use of collage techniques to create sound structures and discourses using pre-recorded materials which are usually subjected to various transformations. Starting from some recordings of oboe improvisations performed by Marion Walker alongside other sources, the resulting materials were 80 acousmatic miniatures with a duration of 20 seconds each, and 10 transitions. All these small compositions are subsequently combined by the algorithms in the same way as the texts and video clips. Each audio clip was individually composed to create a balance between diversity and coherent unity; this produces a unified sonic environment and at the same time provides contrast between the clips. References 1. Homer, The Odyssey, trans. Robert Fitzgerald. (New York: FSG, 1998). 2. Raymond Queneau, Cent mille milliards de poèmes (Paris: Gallimard, 1961). Biographies Alejandro Albornoz is a Ph.D. candidate in the Dept. of Music at the University of Sheffield. Roderick Coover is a Professor of Film and Media Arts at Temple University. Scott Rettberg is a Professor of Digital Culture at the University of Bergen. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 “Hypomnesia,” Game of Memory Li Wanqi Anna Guan Jian Focus anna_22_li@hotmail.com jguan0525@gmail.com Abstract Hypomnesia is created in Blender with Neurosky Brainwave sensor. By obtaining the data of one’s attention level, in this project, participants try to visualize the abstract experience of reminiscence. Viewers are allowed to “intrude” into memory; simultaneously, there are possibilities of their memories being distorted without our consciously knowing it. Concept Human memory is a topic we had grown greatly interested in. Collaborating with department of psychology, City University of Hong Kong, we learned some interesting facts about human memories, which led to some critical thinking towards the disease Hypomnesia. Hypomnesia is a disease of having an abnormally poor memory of the past. Decay of memory, with any doubt, is fearful and dreadful. By reading some of the chapters in Professor Robert A. Bjork's “Successful remembering and successful forgetting,” we learned that actually, lost memories can live again. [1] That is to say, things that we have no conscious memory of still live in our minds, waiting to be woken. However, when we try to recall them, and actively try to reconstruct the past, it seems that we have the possibility to create the stories by choosing which memories to recall. Is the fact of the matter that even as we try as hard as we can to bring something that happened long ago back to our minds, nevertheless, our brain might have already altered it based on our subconscious preferences? That's why we came up with the idea to visualize the abstract experience of reminiscence. To recall the loss of the collective memory of Hong Kong people, we decided to use the traditional buildings of Hong Kong as the scenes of our game. Collective memory, as a kind of cohesive power of the society, acts as common beliefs and shared moral attitudes of the public. [2] Collective memory can be related to many things like experience, images, texts, etc. However, buildings are always the most conspicuous scenes to bear collective memory. [3] We didn’t realize the importance of old buildings until they had been demolished. As the development and reconstructions of the modern cities, our collective memories are fading along with the ancient buildings: it is a collective Hypomnesia. Overview We started to do photo scanning of the old buildings which gave us a sense of Hong Kong déjà vu: temples, outdated wagons, old seafood restaurants, scruffy cabins, etc. Then we did modelling in Blender to bring these fragments of memory together into a nonexistent old village. By getting the data of the attention level through Neurosky Brainwave sensor, we want to intimate the experience of “thinking hard,” since this is what we all do when we want to recall something. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 149 Part III. Artistic project abstracts Fig 1. Hypomnesia, 2017, Li Wanqi / Guan Jian, Interactive game installation. Wearing the brainwave sensor, the viewers became the “intruders” into memory. However, remember that it’s definitely not a trivial matter to explore the surroundings. In order to penetrate the obscurity, people need to concentrate to find out what’s in front of them. Through this process of discovery, some of the viewers may feel the environment to be strange, while the others may find it familiar. If they recognize it, what kind of memories will be jogged? We have finished our prototype in the form of an interactive game installation. Believing in the potential of this project, we will continue to research in the fields of human memory and Hong Kong historical architectures. At the same time, we are also thinking about the possibilities of applying VR technologies to this game. With a more immersive environment, the experience could be much more impressive and vivid. Project’s link https://www.annaliwanqi.com/hypomnesia References 1. Robert. A. Bjork, Successful Remembering and Successful Forgetting (New York: Psychology Press, 2011). 2. Maurice Halbwachs, On Collective Memory (Chicago：University of Chicago Press, 1992). 3. Walter Benjamin, “The Art of Work in the Age of Mechanical Reproduction,” Illuminations (New York: Schocken Books, 1968). 150 Biographies Li Wanqi, Anna received an art education in piano performing and dancing from childhood that enriched her life path and made her an imaginative and observant person. Through careful observation, a desire for self-expression was aroused, sometimes emotional, sometimes critical, which all turned into her motivations of creating films and other works, including instruments, installations and performances. During her study at the School of Creative Media, except for making great efforts in reaching proficiency in techniques of cinematography, software and hardware, she also learned not to conform to stereotypes and enhanced her independent critical thinking, reflected in works, which somehow also gave a hint of her personality, being vivacious and playful, and at the same time, interactive and thought-provoking. Recently, human memories and urban studies serve as inspirations and play crucial roles in her projects. Abstract emotions and thoughts were conveyed mainly through the forms of documentaries, installations and multimedia performances. Guan Jian, Focus, arrived in Hong Kong in 2010 and set to study and work in the field of media and art. Bachelor degree study in media and communication gave him a deep sense of social study and research. He started to be interested in finding the underlying causes of the surface phenomenon. By traveling more than 50 places in 30 countries all over the world, he made a number of documentaries and also gained interesting ideas that can be applied into future new media projects. After working as a multitask videography producer in a local media company for 2 years, he decided to engage in advanced studies in creative media to improve himself both technically and conceptually in the field of new media. During and after the study, he has created and participated in various new media projects, mostly films and installations. His experience of studying and working in both traditional and new media fields help him better understand “the old” and “the new.” Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Up-Close Experiences with Robots Louis-Philippe Demers School of Art, Design and Media, Nanyang Technological University lpdemers@ntu.edu.sg Abstract This paper reports on singular encounters with robots in the context of artistic explorations. These artworks investigate the vast dimensions of the human-robot interaction: multiple layers of embodiments, mechanisms of identification and empathy, thaumaturgical and dramaturgical techniques and morphological computing. Several case studies are reported to explore their potential impact in Social Robotics and to develop alternate human-robot scenarios. Introduction This paper pinpoints a non-exhaustive list of concepts and perceptual observations about upclose experiences. A major common thread of all these robots is that they do not utilize human spoken language and rarely any facial expressions. The limitation of this non-verbal interaction means robot agency is located in the successful embodiment of intent and actions. Hence, the context of the scenario and mise-enscène are key to the experience. In contrast to social robotics where researchers strive to define models for the functionality of a robot, I aim to bring together the real and the unreal, fact and fiction and as Jean Cocteau suggests something “not to be admired, but to be believed.” In this sense, Up-Close Robots are about how to make unbelievable agents, believable. The most recent lineage of projects deal with what I would describe as more radical experiences and encounters, where the coexistence of the robot in the shared space with the human addresses intimate and uncomfortable body proxemics. Projects La Cour des Miracles (1997, 2012) Staging robotic misery, the many layers of embodiment (from the physiological to the social) trigger viewers’ own bodily reception and encourage them to consider these characters not as objects that mechanically reproduce signs of pain but as bodies that actually experience pain. [1] Fig 1. La Cour des Miracles, 2012, Demers/Vorn, ©Kennedy Devolution (2006), XLimbs (2017) These projects engage the audience into imaginative alterations of our original bodyschema. [2] In turn, these robotic wearables for stage performers lead to transformed motions and revised stage presence. Exploring empathic reactions, the viewers are gazing upon these unprecedented bodily sensations felt by the performers. Stemming from scientific research on supernumerary limbs and adapted to the dramaturgical needs of the performance, the machine extension becomes a variation of the object “human dancer.” [3] Being corporeal, it becomes a factual variation of the body. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 151 Part III. Artistic project abstracts paradoxical sense of pleasure emerges through this transformed corporeal experience of coerced movements. [5] Fig 2. XLimbs, 2017, wearable robotics, © Demers. The Blind Robot (2012) This project empowers the qualia of being touched by a robot in what is for most participants the very first time. It enables the audience to take part in a sensual experience, as opposed to one of solving the intellectual, ontological issues of the quasi-living. This scenario incarnates the pivotal role of ‘nascent movements’ in our bodies and also deals with the perception of intentionality. My analysis of the Blind Robot demonstrates the suggestive power of the afflicted agent. [4] Fig 4. Inferno, 2015, Demers/Vorn, exoskeletons, © Gridspace. I Like Robots, Robots Like Me (2018) The radical alterity and the perceived ‘humanness’ of the animal serve as a platform to depart from the expected behaviours of (social) robots and the anthropocentric dialogue imposed on them. Central to this project is the parallel we can establish between the boundaries of human-machine and the humananimal. This process is imploding by simultaneous confusing and reasserting the human/non-human (species) boundaries. The visitors are tracked with physiological sensors. With this information, the robot knows if the visitor is afraid or at rest, asserts where to charge or to flee, or when to stop or stand still. Fig 3. The Blind Robot, 2012, robotic arms, © Demers. Inferno (2015) Inferno is a participative robotic performance project rooted in the ambiguity of control. Playfully framed as a representation of Hell, Inferno offers an intimate experience with exoskeleton technologies and highlights the contradictions found in humans becoming cyborg. Exoskeletons are retrofitted on untrained audience members cum performers. This select group of the public becomes an active part of the performance, giving a radical instance of immersive and participative experience. The human subject is simultaneously master and slave, agent and object, in this transgressive assemblage. A 152 Fig 5. I Like Robots, Robots Like Me, 2018, UAV, © Demers References 1. L-P Demers, “The Multiple Bodies of a Machine Performer,” Robots and Art (Springer, 2016), 273-306. 2. V. Gallese, “The roots of empathy: The shared manifold hypothesis and the neural basis of intersubjectivity,” Psychopathology 36, no. 4 (2003): 171-180. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Up-Close Experiences with Robots. Louis-Philippe Demers 3. Mason Bretan, et al, “A Robotic Prosthesis for an Amputee Drummer” (2016). 4. L-P. Demers, Machine Performers: Agents in a Multiple Ontological State (2015). 5. E. A. Jochum, L-P. Demers, and E. Vlachos, “Becoming Cyborg: Corporeal Empathy, Agency and Politics of Participation in Robot Performance,” EVA-Copenhagen (2018). Biography Demers makes large-scale installations and performances that can be found in theatre, opera, subway stations, art museums, science museums, concerts and trade shows. He has built more than 375 machines and his works have been featured at major venues such as Theatre de la Ville, Lille 2004, Expo 1992 and 2000, Sonambiente, ISEA, Siggraph and Sonar. He received six mentions and one distinction at Ars Electronica, three prizes at VIDA, recommendations at JMAF and six prizes for Devolution including two Helpmann Awards. Demers was Professor of Scenography at the HfG Karlsruhe, affiliated to the renowned ZKM. Since 2006, he joined the newly founded School of Art, Design and Media at the Nanyang Technological University. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 153 Membrane or How to Produce Algorithmic Fiction Ursula Damm Peter Serocka Bauhaus University Weimar ursula.damm@uni-weimar.de pserocka@math.uni-bielefeld.de Algorithmic Precedents in my Oeuvre Membrane is an art installation to be exhibited in Berlin next spring. It builds on a series of generative video installations with real time video input. [1][2] audience can interfere with the temporal alterations of the image by an interface. Membrane allows the viewer to interact directly with the generation of the image, as it was tested in Chromatographic Ballads. [3] Technical conception of Membrane On a technical level, Membrane controls image “features” which are learnt, remembered and reassembled. The characteristics of the features are delegated to a neural network. TGANs (Temporal Generative Adversarial Nets) implement “unsupervised learning” through the opposing feedback effect of two subnetworks. A generator produces short sequences of images and a discriminator evaluates the artificially produced footage. [4] Our algorithm allows us to “invent” images in a more radical manner than classical machine learning would allow. The installation shows images from unchanged street views to purely abstract images, based on the found features of the footage. Fig 2. Chromatographic Ballads [3], explaining the interface 2013 Damm/Schneider Fig 3. First animated video features for Membrane 2018 Damm/Serocka, This setting allows to experience the ‘imagination’ of the computer according to curiosity and personal preferences. Membrane operates on images derived from a static video camera observing a street scene in Berlin. Our Algorithmic Fiction The fictional potential of machine learning has become popular through Google’s deep-dream algorithms. From an aesthetic perspective, these images look paranoid; they tail off in Fig 1. Transits 2012 [2] Damm, Screenprint 154 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Membrane or How to Produce Algorithmic Fiction. Ursula Damm, Peter Serocka formal details and reproduced previously found artefacts (through searching the internet). From an artistic point of view, the question now arises, how can something original and new be created with algorithms? This is the question behind the software design of Membrane. Unlike Google’s deep-dream algorithms and images, we don’t want to identify something specific within the video footage (like people or cars). Our software exposes the visitors to intentionally vague features: edges, lines, colours, geometrical primitives, movement. Interestingly, the resulting images resemble pictorial developments of classical modernism (progressing abstraction on the basis of formal aspects) and repeat artistic styles like Pointilism, Cubism and Tachism in a uniquely unintentional way. These styles fragmented the perceived as part of the pictorial transformation into individual sensory impressions. Motifs are now becoming features of previously processed items and are successively losing their relation to reality. Are these fragmentations of cognition proceeding in an arbitrary way or are there other concepts of artistic abstraction and imagery ahead of us? http://ursuladamm.de/nco-neuralchromatographic-orchestra-2012/. 4. Masaki Saito, Eiichi Matsumoto, Shuta Saito, Temporal Generative Adversarial Nets, ICCV 2017, accessed august 30, 2018, https://pfnet-research.github.io/tgan/, https://arxiv.org/abs/1611.06624). Biography Ursula Damm has become known for her installations dealing with geometry and its social impact on public space. In 2016, Turnstile, a permanent interactive public artwork in Düsseldorf/Germany was inaugurated. Ursula Damm’s works are shown worldwide in exhibitions and festivals. Since 2008 she holds the chair of Media Environments at the Bauhaus-University Weimar/Germany, where she established a Performance Platform at the Digital Bauhaus Lab as well as a DIY Biolab. Cultural perspective From a cultural perspective, we are questioning if the shift of the perspective from analysis to fiction can help to asses our analytical procedures in a different way – understanding them as normative examples of our societal fictions serving predominantly as a selfreinforcement of present structures? Thus, unbiased artistic navigation within the excess/surplus of normative options of actions might become a warrantor for novelty and the unseen. References 1. Ursula Damm, ‘Transits’ (2012) accessed August 30, 2018, http://ursuladamm.de/transits2012/. 2. Ursula Damm, ‘598’ (2009) accessed August 30, 2018, http://ursuladamm.de/598/. 3. Ursula Damm, ‘Chromatiographic Ballads’ (2013) accessed August 30, 2018, Proceedings of Art Machines: International Symposium on Computational Media Art 2019 155 The Fresnel Video Lens Steve Boyer California State University, Long Beach steve.boyer@csulb.edu Abstract The Fresnel Video Lens (FVLens) is a two dimensional array of video monitor/camera pairs that is intended to visually connect adjacent spaces through an optoelectronic medium that serves as both window and lens. It is an exercise in active optics, the term coined by Paul Virilio to refer to capabilities enabled by the optoelectronic decoupling of source (direct light) and signal (indirect light). [1] The FVLens borrows from the principle of the optical Fresnel lens which reduces the mass of a traditional glass lens by dividing it into multiple concentric thin sections with surfaces that match the refractive properties of the original surface geometry but with reduced thickness. Likewise the FVLens flattens the geometry of curved displays that require a depth equal to the sagitta (height) of the arc of the display (figs. 1, 2). This allows for it to be installed within a wall of standard thickness serving as a window between adjacent spaces. Although it could be used as a telepresence display by transmitting video streams from remote locations, the primary exercise is one of constraint, examining methods for reintegrating Fig 1. Arc Configuration Diagram, 2018, Steve Boyer 156 bifurcated spatial experience. Rather than traditional panoramic views the perspective distortions of the Fresnel Video Lens follow the lead of the fragmented imagery found in some of the photo collages of David Hockney such as Sun on the Pool Los Angeles 1982 (fig. 3), Kasmin Los Angeles 28th March 1982, and Brooklyn Bridge, 1982. Instead of stitching multiple sources into a single seamless image these constructions more closely match the realtime assembly of visual fragments into the cohesive perception of space that takes place when we see. Fig.3. Sun on the Pool, Los Angeles, 1982, David Hockney, composite polaroids, Fig 2. Fresnel Configuration Diagram, 2018, Steve Boyer Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Fresnel Video Lens. Steve Boyer Background Digital media tend to void space by drawing the viewer into the space of the media. My work aims to amplify space by drawing the media into the space of the viewer. Video screens of every scale are dominating the built environment from smartphone screens, television screens in restaurants, gas stations and train stations, to the skyline scaled LED images that are bringing the dystopian vision of Blade Runner to cities around the world. The vast majority of the content that appears on these screens is spatially decoupled from its environment. This amounts to the injection of invasive content which has the impact of drawing our attention away from the environment and into the content of the screen. This results in the formation of disintegrated spaces and bifurcated experiences in which we are torn between both worlds. Little effort is made to integrate these experiences by limiting content to audio and imagery that are spatially coherent. Invasive content is space negating. The FVLens is offered as a platform to examine optoelectronics that are space affirming by reintegrating content with environment. Perspective Distortions The FVLens serves as a window providing a link between adjacent spaces. Unlike with standard flat screen views the perspective distortion of the FVLens is a more natural one allowing viewers to see multiple perspectives simultaneously rather than the single planar projection of an image onto a flat surface. The current embodiment of the FVLens proposes a 5x7 array of 35 Rasperry Pi cameras and monitors with Processing and OpenCV installed to provide the ability to process the video streams (fig. 4). While the live feed from the cameras is passed through to the monitors mostly unaltered the platform allows for minor manipulation of the video signal, especially subtle distortions of time including frame skipping, expanding and compressing time, as well as some spatial modifications such as changing apparent focal length. Artificially imposed constraints of allowed and disallowed operations are designed to maintain the integrity of the FVLens as an optical device rather than a medium for invasive content. As these subtle manipulations are introduced to the live camera streams the boundaries of native versus invasive content can be explored and defined. The next iteration will add 2 servo motors to each camera/monitor pair. This functionality allows for moving the focal point of the FVLens, converting from a convex to a concave lens and other potential enhancements. The FVLens will be a platform for examining the complex relationships between our digital and physical presence. Fig 4. 5x7 Fresnel Video Lens, 2018, Steve Boyer References 1. Paul Virilio, Open Sky (London and New York: Verso, 1997), 35-36. Biography Steve Boyer is an artist, designer, inventor and educator with over 30 years of experience developing technology and creating content for a wide variety of interactive media including video games, electronic toys, musical instruments and installations. He has been on the faculty of leading art and design programs in the US including The School of the Art Institute of Chicago, Otis College of Art and Design, The University of California, San Diego and is currently Assistant Professor of Design at California State University, Long Beach. Mr. Boyer earned his Master of Architecture degree at The Southern California Institute of Architecture (SCI-Arc) where his thesis research addressed the growing tensions between digital media and architectural space. He also served as the Director of Research and Development for Interactive Entertainment at Vivendi Games and is the inventor of the volumetric LED display. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 157 MAC Check Scott Fitzgerald Industry Assistant Professor of Integrated Digital Media Co-Director of IDM Associate Director IDM Online Technology Culture and Society Tandon School of Engineering New York University PhD student Department of Media Study University at Buffalo shf220@nyu.edu Abstract MAC Check is an installation with a fictional companion RFC that imagines a group of networked devices that become sentient and rebel against the structures of a human-based network naming convention. A mock RFC written by the devices lays out the methodology the machines use to provide their canonical names. Their desired names, functioning as network addresses, are agreed upon by consent and stored in each device. While this enables fast one-to-one communication when the names are agreed upon, until the consensus on the names are reached by every device on the network all other information transmission is halted. The other side effect of this is that the network becomes unusable by humans. The companion installation is comprised of five devices connected to a local mesh network. OLED screens report the conversations held by the devices, reporting their internal states for observers to view. Creating a Canonical Name MAC Address concerns itself with the political implications of intelligent machines that learn behavioral models from humans. It questions ideas of sentience, responsibility, and power relations between humans and objects. The text and installation parts of the work are examples of speculative fiction. It starts with the question “What do these objects want?” and 158 attempts to answer from the perspective of the devices themselves. The physical installation introduces behaviors not addressed in the paper, though it still has the core ‘quirk’ of the system in that the devices ask for consensus when determining their names. At boot, each device chooses a name for itself from a randomly generated list, and asks the rest of the connected devices if it can use that name. If so, it can begin to communicate about other topics. If not, it needs to choose a new name and wait for it to be approved by the broader network. Text is broadcast across all nodes in the network, so that the internal status is rendered visible for any observers. Not only is the process of deciding on the names made transparent, so too are the internal states of the devices. Pulled from an online corpora of “interesting stuff,” the devices communicate various states of desire on their part, including emotional states they will never feel, and their desired function in society. [1] As an example of research oriented art practice, the piece draws on multiple sources for inspiration. The actual method of finding consensus in this fashion is inspired by the Occupy protests and the democratically fair, but often inefficient “Mic check” protocol employed by participants. [2] Proceedings of Art Machines: International Symposium on Computational Media Art 2019 MAC Check. Scott Fitzgerald Political Implications As a matter of control, DNS imposes a hierarchical structure on network naming that is bureaucratic in nature. [3] “Authoritative” machines are the resource we rely on to translate IP addresses to human readable names. Asking “what does the network want?” is the first step in pushing against this form of control and structure. Friedrich Kittler postulated that machines have taken over the path of history from mankind. [4] As we cede more agency of human affairs to machines, it’s not unreasonable to believe that the devices will have their own desires that are sometimes in conflict with ours. What is efficient for us is not necessarily efficient for these machines. How they come to decisions may mimic our processes, or it may be completely foreign to us. This work is an attempt to understand how these objects might behave and alter what works for us to suit their own needs. Supplementary Information and Documentation Video documentation of the work as it was developed can be viewed at https:// vimeo .com/281452624. The fictional RFC can be accessed at http://bit.ly/2wz4Jit. References 1. https://github.com/dariusk/corpora. 2. Zeynep Tufekci, Twitter and Tear Gas (New Haven: Yale University Press, 2017), 100. 3 Alex Galloway, Protocol (Cambridge, MA: MIT Press, 2004), 141. 4. Friedrich A. Kittler, Gramophone, Film, Typewriter (Stanford, Ca: Stanford University Press, 1999), 258. Biography Scott Fitzgerald is an artist and educator working with contemporary technologies. His recent work includes artistic applications of machine learning, networked devices, and temporary co-locative spaces. He is the co-Director of New York University's Integrated Digital Media program in the Tandon School of Engineering and working towards a PhD at SUNY Buffalo's Department of Media Study. He is also partner at lightband Studios, creating bespoke glass and dynamic lighting installations. Previously, Scott worked on documentation for the Arduino platform and was the founding head of NYU Abu Dhabi's Interactive Media program. Fig 1. MAC Check (detail of installation view), 2018, Scott Fitzgerald, electronics, code, battery, Photo courtesy of the artist. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 159 Visualizing Algorithms: Mistakes, Bias, Interpretability Catherine Griffiths University of Southern California, School of Cinematic Arts griffitc@usc.edu Abstract This design research project addresses the domain of obfuscation and ethical bias at the heart of machine learning algorithms. By opening the algorithmic black box to visualize and think through the meaning created by algorithmic structure and process, this project seeks to provide access to and elucidate the complexity and obfuscation at the heart of artificial intelligence systems. The questions being addressed include: Can tactics from the visual arts and digital humanities, including interaction design, generative design, and critical code studies, combine as an effective method to visualize ethical positions in algorithms, including bias, mistakes, and interpretability? How can visualization of algorithms be used as an alinguistic tool to re-engage with decisionmaking in prediction systems, where humans are at risk of being precluded? When considering bias augmentation, what can be learnt by temporarily isolating the meaning in data, to focus on the effect that structure and process play in the generation of bias? The work-in-progress prototype software visualizes a machine learning algorithm, a decision tree classifier. It simulates data flowing through the algorithm and predictions being made in real time. It is built procedurally as an interactive tool, so that any classifier of the same type can be loaded and visualized. The UI provides parameters to support the self-organization of the classifier structurally and to aid analysis. The loaded examples present different topologies of classifier based on machine learning data 160 sets with different feature to class ratios. The prototype can currently visualize mistakes in prediction, where the algorithm misclassifies data. It can also reverse engineer each data point’s path to visualize where in the algorithm an error was made. The most popular paths taken through the algorithm’s complex network of decisions are also visualized. The project is conceived using a conceptual approach to machine learning, to experiment with how aesthetics and design can be used as tactics for engagement with complexity. Tactics include: a move away from data visualization toward computational visualization to focus on real-time and even projected rule sets, rather than a retrospective and fixed approach to data. Adapted insights from programming games and animation are used to present both human-scale and emergent processing speeds, the flow of data through an algorithm, and how decisions are made in real-time. The research is working toward the use of visual arts tactics as a means of “ethical debugging”, in which complex terms, such as bias and interpretability can be presented visually, and algorithms can be engaged with aesthetically as socio-political systems. [1] As the research continues to develop, more speculative design avenues will be explored, alongside technical problems. The project so far has concentrated on developing a more robust visualization of a machine learning algorithm to engage and collaborate with computer scientists working in this field. As the research develops, the intention is to Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Visualizing Algorithms: Mistakes, Bias, Interpretability. Catherine Griffiths develop further scenes of this application that navigate more strongly, even contentiously, back toward the visual arts, to explore the potential for “novel models of relationality and connectivity.” [2] An overarching question asks, how artistic knowledge can contribute to the issues of the day, generating new ideas, proposals, and methods, using aesthetics as the primary paradigm of knowledge generation, without solely assimilating with traditional scientific methods. References 1. Catherine Griffiths, “Visual Tactics Toward an Ethical Debugging,” Digital Culture & Society: Rethinking AI, 4, no. 1 (2018): 217. 2. Simon O’Sullivan, “Inquiry,” in NJP Reader 1: Contributions to an Artistic Anthropology, ed. Youngchul Lee and Henk Slager (Seoul: Nam June Paik Art Center, 2010), 52. Biography Catherine Griffiths is a PhD candidate in Interdisciplinary Media Arts + Practice at the University of Southern California, School of Cinematic Arts. She researches at the intersection of visual art, computation and critical studies, focusing on the visualization of algorithms, in the context of machine learning and the ethics of algorithms debate. She has a bachelor's degree in Fine Art from the University of the Arts, London, and a master’s degree in Architecture from University College, London. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 161 Multimedia Art: The Synthesis of Machine-generated Poetry and Virtual Landscapes Suzana Ilić Martina Jole Moro University of Innsbruck Department for Linguistics io.suzanai@gmail.com University of Innsbruck Department for Architecture martina.moro.mjm@gmail.com Abstract Artificial Intelligence, Virtual and Augmented Reality are transforming multimedia art, offering the opportunity for novel creative human-machine collaborations and assisted design. In this work we demonstrate the synthesis of a character-level long short-term memory network for generating poetry and L.e.O. (Luci e Ombre; Lights and Shadows), a virtual landscape composed of dynamic architectural elements and surfaces, providing an immersive digital art experience. largely coherent from a semantic perspective, where expressions like soul of the storm can be interpreted as metaphors. The linguistic style matches approximately the requirements and aesthetics of poetry, however, there is a striking word-level repetitiveness (see Table 1). Generating poems with LSTMs Recurrent neural networks (RNNs) encompass high-dimensional hidden states and are able to iterate over sequences of arbitrary size, and process and memorize information. [1] RNN variants are commonly deployed in the field of natural language generation. [2] We trained a character-level long short-term memory network (LSTM) on a dataset of 1.3M characters of classical and contemporary poems, where the network receives an input at each timestep, updates its hidden state and predicts one character at a time. The model architecture comprises an LSTM layer with 128 hidden units, followed by a Dropout layer (0.2) as a regularization technique to avoid overfitting. [3] The best results were achieved with the Adam optimizer, a learning rate of 0.0005 and a categorical cross-entropy loss. The selected poem was generated during epoch 105 and was sampled from a range of diversity values for the temperature parameter in order to experiment with uncertainty. The poem shows errors in morphology and syntax, but seems 162 on a charred spinning wheel, the world was cold the soul of the storm, the shadow s soul where the strong she still, the stars that beautiful and strain, and the strange and the storm of the stars, and the stars of the storms of the stars, i say i shall be the made the stars of the storm, the stars when the wind of the stream of the shadow, the thing of the said the world was a sea, and the shadow of the sky Table 1. The curated machine-generated poem reveals interesting, novel metaphors. 3D-modeling: The virtual landscape L.e.O is an alternate reality composed of real light and shadows, where nine distinct silhouettes were extracted from the original structure. The selected objects were then deconstructed, analyzed and reassembled in a different manner. Subsequently, the pieces were scaled up and down depending on their role in the virtual environment. The entire digital island is made of the same selection of the original nine lights and shadows using a modular system and was developed in Rhinoceros 3D, a design application software, and then imported into Unity for adding a range of different textures to the environment (see Fig. 1). [5] As a final step, we blended the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Multimedia Art: The Synthesis of Machine-generated Poetry and Virtual Landscapes. Suzana Ilić, Martina Jole Moro human-read audio recording of the AI poem into the video sequence, which, experienced in VR, gives the illusion of exploring a surreal virtual environment, while hearing the machine-generated poem in the background. Fig. 1. The digital 3D-island L.e.O (Luci e Ombre), a virtual landscape composed of deconstructed light and shadow objects. Conclusion Creative design and visualization projects can be enhanced by Artificial Intelligence in various ways, such as leveraging deep learning models for image, video and text generation. Thus, it can be used for content creation as well as for assisting humans in the creative process. This multimedia art project demonstrates how two creative streams can be merged: The synthesis of (1) a 3D model of a virtual landscape, created through modular and patchwork assembly, and (2) a poem generated by a character-level LSTM trained on a dataset of 1.3M characters of poems. Future work can include models such as Generative Adversarial Networks for generating novel virtual landscapes. Acknowledgements We gratefully acknowledge the contributions for this project: Johannes Felder (video), Christian Anich (music) and Josiah Sampson (voice). recurrent neural networks." In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (2011): 1017-1024. 2. Gatt, Albert, and Emiel Krahmer. "Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation." Journal of Artificial Intelligence Research 61: 65-170 (2018). 3. Wojciech, Z., Sutskever, I., Vinyals, “O.: Recurrent neural network regularization.” arXiv preprint arXiv:1409.2329 (2014). 4. Lee, Ghang, Charles M. Eastman, Tarang Taunk, and Chun-Heng Ho. "Usability principles and best practices for the user interface design of complex 3D architectural design and engineering tools." International Journal of Human-Computer Studies 68, no. 1-2 (2010): 90-104. Biographies Suzana Ilić is a PhD student (Linguistics and Media Program) at the University of Innsbruck/Austria. Previously she was a visiting researcher at the National Institute of Informatics in Japan, where she worked on affect-sensitive deep learning models for text. Her research interests include sentiment analysis and text-based affective computing, as well as generative models for creative language output. She is currently working on conversational systems (NLU) in Tokyo, Japan. After a career in competitive sports and subsequent work in journalism, both as a writer and photographer, Milano born architect and artist Martina Moro started her studies in Architecture at the University of Innsbruck/Austria. She is currently working on art projects in the fields of design, computer technology and architecture. She contributed to numerous exhibitions in Austria and Italy, among them the Venice Architecture Biennale 2018. References 1. Sutskever, Ilya, James Martens, and Geoffrey E. Hinton. "Generating text with Proceedings of Art Machines: International Symposium on Computational Media Art 2019 163 Microbial Sonorities Carlos Castellanos, Ph.D. Department of Art, Kansas State University, U.S.A. ccastellanos@ksu.edu Abstract Microbial Sonorities explores the use of sound to investigate the bioelectric and behavioral patterns of microorganisms. The piece features a hybrid biological-electronic system wherein variations in electrical potential from an array of microbial fuel cells are translated into rhythmic, amplitude and frequency modulations in modular electronic and software-based sound synthesizers. Introduction Microbial Sonorities explores the use of sound to investigate the bioelectric and behavioral patterns of microorganisms. Based upon inquiries into emerging bioenergy technologies and ecological practices as artifacts of cultural exploration, the piece features a hybrid biological-electronic system wherein variations in electrical potential from an array of microbial fuel cells are translated into rhythmic, amplitude and frequency modulations in modular electronic and synthesizers. software-based sound Research Focus The research focuses on three primary areas: (1) Microbial Fuel Cells (MFCs): these are devices that generate electricity from the metabolic reactions of bacteria found in diverse environments such as lakes, compost and wastewater. [1] (2) Modular hardware and software synthesizers: The bioelectrical fluctuations of the MFCs are used as modulation and trigger sources for a Eurorack-based modular synthesizer and/or a custom-designed software synthesizer built in Max/MSP (cycling74.com). This entails building electronic circuits to amplify the electrical signals generated by the bacteria and software to translate the signals into control voltage (CV) sources appropriate for the synthesizer. (3) Machine Learning: Machine-learning Algorithms are used as a way of interpreting the shifting electrical patterns generated by the Fig 1. Microbial Sonorities installed at Washington State University, Pullman, Washington, USA in 2016. The modular synthesizers are shown in the center behind the microbial fuel cells. 164 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Microbial Sonorities. Carlos Castellanos bacteria. Pattern recognition/classification is used to trigger synthesizer presets and CV gate signals while statistical regression is used to predict variations in electrical potential. If a comprehensive understanding of the bioelectrical patterns can be attained, it will be used to inform the development of a sonic compositional system that is dictated by these patterns. In essence, allowing the bacteria to “express” themselves sonically. System Overview The current system set-up typically consists of four MFCs, a Eurorack modular synthesizer system, an Arduino microcontroller (arduino.cc) and the Max/MSP graphical coding environment (cycling74.com). The biomatter used for the MFCs is usually fresh compost or if possible, benthic mud from a local lake or other aquatic body. Voltage from each MFC is amplified and connected to an analog input on the Arduino. In some cases it may also be plugged directly into the control voltage input on one of the Eurorack modules. Fig 2. Typical voltage curves for a microbial fuel cell. The horizontal axis represents time (in hours) while the vertical axis represents voltage (in millivolts). Taken from [2]. The piece operates on two temporal scales. The first, which I call “immediate,” consists of a simple linear mapping of voltage to pitch for each MFC. Transient voltage spikes are also detected and mapped to sound. The second time scale, “longitudinal,” is a longer-term (24-48 hours) mixing of Eurorack synth patches. Each MFC is assigned a synthesizer patch according to its current “life stage.” A life stage is simply a point in the overall voltage curve over which a typical MFC travels over the course of 24-48 hours before it “dies” (i.e. when the bacteria run out of organic matter to metabolize; see fig. 2). [2] Four life stages have been identified and assigned a synthesizer patch. A regression curve, using a neural network, was then created to mix/transition between the four different sounds/patches. Training data for the network was created simply by drawing a curve in Max’s itable object that matches a typical MFC voltage curve. The x coordinates of the itable represent discreet time steps (0-50 hours), while the y coordinates represent voltages (0-1000 millivolts). While the piece is running, a running average of the voltage is kept for each MFC and sent out to the neural network application once every 30 minutes via OSC (opensound control.org). Conclusions & Future Work In addition to exploring different scales and construction materials for the MFCs, other features beyond voltage and electrical properties (e.g. chemical properties) are currently being investigated. Overall, the use of sound and machine learning as methods of bridging human and microbial lifeworlds and exploring the material agency of microorganisms continues to be an exciting area worthy of continued, playful investigation. More information on the project is available online at ccastellanos.com/ projects/microbial-sonorities/. References 1. Bruce E. Logan, Microbial Fuel Cells (Hoboken, N.J: Wiley-Interscience, 2008). 2. M. Azizul Moqsud et al., “Bioelectricity from Kitchen and Bamboo Waste in a Microbial Fuel Cell,” Waste Management & Research: The Journal of the International Solid Wastes and Public Cleansing Association, ISWA 32, no. 2 (February 2014): 124–30. Biography Carlos Castellanos is an interdisciplinary artist and researcher with a wide array of interests such as cybernetics, ecology, embodiment, phenomenology, artificial intelligence and transdisciplinary collaboration. His work bridges science, technology, education and the arts, developing a network of creative interaction with living systems, the natural environment and emerging technologies. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 165 Part III. Artistic project abstracts Castellanos is Assistant Professor and director of the Digital/Experimental Media Lab in the Department of Art, Kansas State University. 166 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The 360° Video Secret Detours as Case Study to Convey Experiences through Immersive Media and the Method of Presentation Elke Reinhuber School of Art, Design and Media ADM / NTU Singapore elke@ntu.edu.sg; eer@me.com Benjamin Seide Ross Williams School of Art, Design and Media ADM / NTU Singapore bseide@ntu.edu.sg School of Art, Design and Media ADM / NTU Singapore rawilliams@ntu.edu.sg Abstract Our recent work Secret Detours served as an immediate approach to digitally preserve a Chinese garden in Singapore and has been conceived as an immersive 360° video. We have investigated several different presentation modes in order to explore the screening possibilities. The constraints and limitations of each mode has necessitated a reconfiguration of visual and audio composition. The experience of the work and the aesthetic and technological decisions that informs it, varies significantly, depending on whether the work is collectively viewed in a hemispherical dome, a cylindrical panorama, a panoramic LED video wall or with a range of different VR headsets. Secret Detours Secret Detours was filmed with 360° spherical video in a Chinese garden in Singapore, which opened in 1956 – fairly old for the 53 years old city state. The garden is currently undergoing massive re-development, several old trees were logged, bridges and pavilions were removed. As it was important to act fast, myself and, my two collaborators, Benjamin Seide and Ross Williams, decided to capture the garden with 360° imagery, not only for artistic purposes but for conservation reasons as well. Four dancers acted out a choreography to represent the cardinal directions of Chinese Mythology, after which the garden was initially conceived. Fig 1. Secret Detours, 2018, Reinhuber, Seide, Williams Forking paths in the south-east of Yunann Garden, represented by the dancers dressed in vermillion and azure. 360° video in equirectangular projection on a flat screen. Although the visualization gives an impression of being inside the garden, it is still a very static experience and therefore, we are currently working on a room scale model for VR, based on photogrammetric assessments to restore the garden according to the floor plan in the virtual space. Fig 2. Different to the immersive experience in a surrounding projection, the dancers on the planar panoramic video wall NEXUS accompany passers-by. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 167 Part III. Artistic project abstracts standardization in channel configuration (outside of cinematic presentations) and the ever present issue of variable room acoustics and ambient noise. The recent resurgence of ambisonics and binaural techniques for headphones in VR offer a way to mitigate some of the standardization issues mentioned, but not without limitations. Fig 3. Larger than life-size presentation in the Digitalis dome with one projector invites the viewer to sit down and observe. However, considering the respective iterations we already produced, the perception of the 360° screenings differs hugely, depending on the particular presentation technique. Since the technology around spherical recording and displaying is still in flux, due to the rapid developments and along with particular improvements by the industry, competing for market penetration. Fig 5. The shared experience of viewing Secret Detours in a cylindrical panorama – the ideal set up for the mobile spectator. Table 1: Current screening formats of Secret Detours. Resolution Original footage VR headset Cylindrical panorama Panoramic LED video wall Fulldome Fig 4. Presentation of Secret Detours in a 7 metre Fulldome with four HD projectors. For 360° media, the facilitation of viewing techniques has only just began. After Morton Heilig’s and Ian Sutherland’s first approaches with ray-cathode tubes in front of the user’s eyes within a bulky set up, the facilities today range from DIY cardboard solutions, which immensely popularized the medium to high-end immersive environments. In particular standalone headsets for 360° media (including stereoscopic viewing experiences) appear to be a promising solution, even when the obvious limitations have to be contemplated. Sound presentation is similarly affected with little 168 MiniFulldome Flat Screen, VLC, GoPro or YouTube Audio Width in px 7680 Height in px 3840 Geometry equirectangular mute 7680 3840 spherical Binaural 5248 608 cylindrical 3840 480 planar 2048 2048 hemispherical 5.1 1200 1200 hemispherical 5.1 8 channel 5.1 Binaural screen resolution planar, scrollable Biography Elke Reinhuber, Benjamin Seide and Ross Williams currently teach and research in Media Art at ADM, School of Art, Design and Media at NTU Singapore. With their experience and expertise in the areas of sound design (Williams), special effects and imaging (Seide) as well as camera and concept (Reinhuber), they explore the fascination and possibilities of immersive media from different points of view, especially in regard to representations of culturally relevant subjects. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Parallax Relax: Expanded Stereoscopy Max Hattler City University of Hong Kong mhattler@cityu.edu.hk Abstract In recent years, stereoscopic films, virtual reality (VR) and augmented reality (AR) have matured and proliferated. This newly-emerging stereoscopic status quo operates within the same principles set out at the beginning of the technology: stereoscopy produces 3D depthperception from the stereoscopic fusion of left and right images. Yet, beyond the normative practice of emulating human vision, stereoscopy can be leveraged to offer new perceptions and aesthetics. While phenomena such as binocular rivalry are well researched within cognitive neuroscience and psychophysics, their artistic potential remains largely untapped. Artists such as Salvador Dali, Memo Akten and Blake Williams are among the few who have explored this territory. We propose the term expanded stereoscopy to describe stereoscopic processes which create spaces where depth relations are disjointed and paradoxical, where binocular rivalry is used to create unique visual effects or to guide viewer attention, or where new dimensionality and visual intensity are excavated from flat source material. Such expanded, technologically-aided uses of stereoscopy allow for ways of seeing that are impossible in the real world and can be seen as a true expansion of the senses. Parallax Relax presents a discussion of some of the challenges and findings of our ongoing arts-based research into expanded stereoscopy, across the fields of single-screen projection, audio-visual live performance, and 360-degree immersive media, which began with the creation of III=III for Animamix Biennale 2015-16. Fig 1. III=III, 2016, Max Hattler, stereoscopic digital animation. Biography Max Hattler is an artist and academic who works with abstract animation, video installation and audiovisual performance. He holds an MA in Animation from the Royal College of Art and a Doctorate in Fine Art from the University of East London. His work has been shown at festivals and institutions such as Resonate, Ars Electronica, ZKM Center for Art and Media, MOCA Taipei and Beijing Minsheng Museum. Awards include Supernova, Cannes Lions, Bradford Animation Festival and several Visual Music Awards. Max has performed live around the world including at Playgrounds Festival, ReNew Copenhagen, Expo Milan, Seoul Museum of Art and the European Media Art Festival. He is an Assistant Professor at School of Creative Media, City University of Hong Kong. Max’s current research focuses on synaesthetic experience and visual music, the narrative potential of abstract animation, and expanded artistic approaches to binocular vision. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 169 The Electronic Curator or How to Ride Your CycleGAN Eyal Gruss Mahanaim 134 Tel-Aviv University eyalgruss@gmail.com Abstract The Electronic Curator examines whether a computer can not only generate art, but also evaluate its quality. [1] The work uses a Generative Adversarial Network (GAN), which constitutes a dialog between two competing neural networks. Here one represents a painter, who turns a human face into a vegetable portrait (fig. 1). The other represents a curator, who evaluates whether the portrait indeed looks like vegetable faces and encourages the painter to improve. The dialog between the competing networks represents the artistic process. Training is unsupervised based on the cycleconsistent generative adversarial networks (CycleGAN). [2] Thus we require only a set of face images and an unpaired and unrelated small set of vegetable-faces collected from a Google search on the Internet (fig. 2). In order to avoid mode collapse and get diverse and interesting results, we use a modified loss function inspired by DistanceGAN. [3] In exhibition mode, the painter observes the spectator's face and turns it in real time into a vegetable-face. The curator then grades the outcome. If the outcome is good enough to confuse the curator, a curatic text is generated based on the vegetables and fruits found in the portrait by object detection (fig. 3). In a world of AI art and creative machines, will the art of curation remain reserved for humans? In the talk, we will review the techniques that helped in training and in inference, as well as those which did not help. Namely, we will discuss data collection and training strategies, modifications to the loss, and inference time normalization. 170 Eran Hadas Mahanaim 134 Tel-Aviv University ehadas@gmail.com Fig 1. A vegetable-face generated in real-time in inference. Fig 2. Unpaired samples from the training set in the two domains. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Electronic Curator or How to Ride Your CycleGAN. Eyal Gruss, Eran Hadas Fig 3. The first author's pretty face, its corresponding vegetable portrait, and the curatic text generated for it. From an exhibition at Heinz Nixdorf MuseumsForum, Paderborn, Germany. References 1. Project video: youtube.com/watch? v=4sZsx4FpMxg. 2. Zhu, Park, Isola and Efros, Unpaired Imageto-Image Translation using Cycle-Consistent Adversarial Networks, junyanz.github.io/ CycleGAN. 3. Benaim and Wolf, One-Sided Unsupervised Domain Mapping, arxiv.org/abs/1706.00826. Biographies Eyal Gruss is a machine learning researcher and an artist. He is based in Israel and holds a PhD in physics. His works include poetry, interactive installations and computer-generated art. Eran Hadas is an Israeli poet, software developer and media artist. Among his collaborative projects are a headset that generates poems from brainwaves (with Gruss), and a documentarian robot that interviews people about the meaning of being human. Hadas was the 2017 Schusterman Artist-inResidence at Caltech. He teaches in the New Media Program at Tel-Aviv University. Mahanaim 134 is Gruss and Hadas' tech-art collaboration. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 171 Das Fremde Robot Installation Michael Spranger Stéphane Noël Sony Computer Science Laboratories Inc., 3-14-13 Higashigotanda, Tokyo, Japan Michael.spranger@gmail.com Artist and Curator snoel@me.com Abstract We discuss a recent award winning Artificial Intelligence installation that deals with autonomous meaning creation in machines. The cultural identity of this micro-society faces foreign cultural elements - the visitors. Like any indigenous population gradually invaded by an outside population, its culture is forced to expand, to hybridize, withdraw or possibly surrender. The installation integrates recent techniques in AI: deep learning, deep reinforcement Installation In a dimly lit space, a tribe of robots is busy. The members of this colony observe the world around them and try to describe it using a language they create in real time. Each identifies the elements in its vicinity, invents a word for it, and communicates that name to its counterparts. Together they create a language that the whole village can understand, and thus build the common culture of this artificial species. Suddenly another species intervenes, disrupting the quiet atmosphere of this community. Humans enter the space. A person approaches, trying to grab the attention of the agents, which turn their camera-eyes and microphone-ears towards him or her. The visitor initiates a form of communication. The robots’ culture is put to the test. How do the robots deal with the novel objects? Will the culture resist these external interventions, will it adapt its vocabulary and evolve, or will it simply vanish, to be replaced by a dominant human culture that is totally external and unknown to it? Das Fremde immerses visitors as explorers who witness the birth of a language and the evolution of the culture of another, non-organic life form. The installation tries to capture the moment of discovery: the moment the audience turn into pioneers and ethnologists stumbling upon an emerging civilization. Das Fremde is a performative installation featuring a species of artificially intelligent entities that create their own language and culture through a cultural evolutionary process. 172 Fig 1: Installation in Zurich, CH (11/2016) learning as well as more traditional methods such as rule-based approaches. [2] It serves as a showcase for the abilities of current systems to generate symbolic culture and autonomous meaning. Concept and Discussion Das Fremde is German and refers to something between the strange and the stranger – or both at the same time. We apply this concept for the installation in two ways. On the one hand, Das Fremde examines the function of the foreign for the definition and self-construction of cultural identity. The robots see the visitors as foreign objects. Driven by curiosity or boredom, excitement and disinterest they interact with and about the visitors, which in turn shapes their culture and influences the Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Das Fremde Robot Installation. Michael Spranger, Stéphane Noel construal and conceptualization of reality. Concepts and words emerge about the visitors. Sights and sounds might be picked up, imitated and ultimately become frozen into concepts and cultural memory. On the other hand, the installation is a foreign object to us the visitors. For us, especially in Europe there is a cultural stream of seeing machines as foreign entities. Separated from us humans by foreign codes, strange behaviors and exotic sights and sounds. The installation questions our relationship with machines and makes the divide directly experienceable. This is especially important at a time where machines and machine intelligence are part of a heated discourse. What it is like to be among machines, which do not assemble cars behind fenced off areas but instead seem to demonstrate a level of autonomy and independence that casts the human being in the role of outsider. Das Fremde offers visitors the opportunity to immerse in a slow emotional process during which one can witness the birth of a language and the evolution of the culture of another, nonorganic life form. We capture the poetic moment of discovery: the moment the audience turns into explorers and ethnologists stumbling upon an emerging civilization. Consequently, visual and sensory aspects of the installation are designed to favor an intimate encounter, rather than to overload the visitors with escalating effects and placatory discourse. The installation follows in the footsteps and extends earlier artistic work at the interface between art and technology – such as The Talking Heads Experiment, conducted in the years 1999-2001 and recently published as a book. [1] This was the first large-scale experiment in which populations of embodied agents created for the first time ever a new shared vocabulary by playing language games about real world scenes in front of them. The agents could teleport to different physical sites in the world through the Internet. Sites, in Antwerp, Brussels, Paris, Tokyo, London, Cambridge and several other locations were linked into the network. Similarly, the ErgoRobots Experiment by Pierre-Yves Oudeyer and collaborators investigated artificial curiosity and language formation in robots as part of the exhibition “Mathematics: A Beautiful Elsewhere” at Fondation Cartier pour l’Art Contemporain Paris, France. Here robots were equipped with mechanisms that allow them to learn new skills and invent their own language. Endowed with artificial curiosity, they explore objects around them, as well as the effect their vocalizations produce on humans. References 1. L. Steels, The Talking Heads Experiment: Origins of Words and Meanings, volume 1 of Computational Models of Language Evolution. (Berlin: Language Science Press, 2015). 2. M. Spranger, The Evolution of Grounded Spatial Language (Language Science Press, 2016). Links Website: http://www.dasfremde.world Dossier: https://tinyurl.com/y9p5j5by Biographies Michael Spranger received a PhD from the Vrije Universiteit in Brussels (Belgium) in 2011 (in Artificial Intelligence). He currently holds a research position at Sony CSL Inc. Tokyo Japan. Michael has published more than 60 peerreviewed papers on AI, developmental robotics and computational linguistics. Michael has been producing various art works reflecting on the nature of Artificial Intelligence and our relationship with machines: including robot installations such as Confident Machines (2011), Das Fremde (2016). He was also a technical advisor for the opera Casparo (2011). Stéphane Noël served as director of Les Urbaines festival in Lausanne (1997-1998), and as co-director of Belluard festival in Fribourg (2004–2007). He has been on the artistic and editorial board of Gaîté lyrique in Paris (2009– 2011) and acted as an advisor for European.Lab. Stéphane Noël’s artistic work ranges from screenwriter to media artist. Das Fremde is an attempt at developing aesthetics concepts around artificial intelligence and humanism. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 173 Repopulating the City: Introducing Urban Electronic Wildlife Greg Nijs Guillaume Slizewicz Urban Species, Dept. of Architecture, ULB contactgregnijs@gmail.com Urban Species, Intermedia, LUCA School of Art Guillaume.slizewicz@luca-arts.be Abstract The artworks put forward in this presentation draw upon the idea of the cyborg from an ecological perspective. [1] In addressing the question of bio-extinction and electronics [2], we propose the introduction of 'urban electronic wildlife' in public space as a way to induce wonder and awareness in human urban dwellers. To that end, we introduce newly cre- ated hybrid species – i.e. physical devices with zoomorphic and spectral traits packed with ma- chine learning algorithms and exhibiting auton- omous behavior – as an act of 'applied' specula- tive fabulation. [3] [4] We currently have two projects in development in our studio: Capricious Ghost and Stray Peddler. Capricious Ghost is an installation which asks passers-by to show them an object and reacts to what it sees thanks to object detection algorithms. It is made of a raspberry pi with a camera running a detection algorithm trained on the COCO dataset, a button, a speaker, a RF emitter and a RF-connected plug socket. [5] The artwork “speaks” to the user using an espeak and asks to see a certain type of object. The detection is triggered by the push of a button. Once the button is pushed, the computer will describe what it sees and if the object is present, it will turn on the plug socket with Radio Frequencies. This plug socket can be used by any electrical device. The way the set-up is presented, what action it triggers and its appearance in public space are variable (much like ghosts' apparitions). Whatever form it takes on, Capricious Ghost resonates with concerns both about ubiquitous technology, the sentient city and animism as well as extinction, radiation and ecologically 174 haunted humanity. [6] [7] The Stray Peddler is a small robot that roams freely in the city and delivers messages to ur- ban dwellers. It was inspired by different experiences we had in the field, a public place in the center of Brussels. It is a mix between a Jehovah Witnesses’ trolley, an abandoned, quivering circular saw, stray dogs and small electric devices sold by street vendors. It also draws on the idea that peddlers helped create public spaces by conveying ideas, discussion, controversies and stories in cities and between cities. The peddler is made of a simple, off the shelf, autonomous robot based on an Arduino microcontroller, with an added bluetooth speaker to give him a voice. A raspberry pi is mounted on it to give it the ability to detect and follow people to deliver messages to them. In order to change the meaning of their pres-ence and enhance its zoomorphic attributes, we camouflage it with fake fur and fake eyes. We believe that both projects have the potential to question the relationship city dwellers have with now ubiquitous technology, while the underlying idea is to advocate for technologically generated life-forms as critters in their own right and existence, not opposing natural and technologically generated life-forms, but reinforcing their bonds in their struggle for survival on a damaged planet. References 1. Ursula K. Heise, “From Extinction to Electronics,” in Zoontologies, ed. Cary Wolfe ( Minneapolis/London: University of Minnesota Press, 2003), 59. 2. Stina Attebery, “Coshaping Digital and Biological Animals,” HUMaNIMALIA Vol. 6, Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Repopulating the City: Introducing Urban Electronic Wildlife. Guillaume Slizewicz, Greg Nijs No. 2, Spring 2015, accessed August 15, 2018, https://www.depauw.edu/humanimalia/issue12/ attebery.html. 3. Donna J. Haraway, Staying With the Trouble (Durham: Duke University Press, 2016). 4. Lucienne Strivay et al., “Les Enfants du Compost”, in Gestes Spéculatifs, ed. Didier Debaise and Isabelle Stengers (Dijon: Les presses du réel, 2015), 151. 5. Tsung-Yi Lin et al., “Microsoft COCO: Com- mon Objects in Context”, working paper, accessed August 2, 2018, https://arxiv.org/abs/ 1405.0312. 6. Nigel Thrift, “The 'sentient' city and what it may portend,” Big Data & Society April-June, (2014): 1. 7. Anna Tsing et al., Arts of Living on a Damaged Planet (Minneapolis/London: University of Minnesota Press, 2017). edge production in its widest sense, in the field of art and society at large. His particular inter- ests revolve around issues of human and other- thanhuman relations, im/materiality, affect and cognition, identity politics, the question of nature and technology, and a/biotic multispecies entanglements. In his approach, he draws on a range of social scientific and philosophical sources such as science and technology studies, design studies, cultural studies, cognitive sciences, HCI, pragmatism, and the like. Currently, he is conducting research on the development of smart tools for civic engagement with a participatory design approach. Biographies Guillaume Slizewicz is a French designer working at Urban Species (Intermedia Lab, LUCA School of Arts), an interdisciplinary research group focusing on citizen participation in the city of Brussels. His work is at the crossroad of political sciences and interaction design. Having completed Politics, Philosophy and Economics at the University of Kent in Canterbury and Sciences-po Lille, he specialized in Product development and design at KEA Copenhagen School of Design and Technology and followed a course in Machine Learning at CIID taught by Gene Kogan and Andreas Refsgaard. He is interested in the interstices offered by electronic objects in the urban spaces, the unexpected behavior that glitches provoke and the surprise created by misused hardware systems and hijacked algorithms. With his team, he is thinking on how to repopulate the city via new breeds of urban electronic wildlife. Greg Nijs is a sociologist working as a researcher at Urban Species (Dept. of Architecture, Université Libre de Bruxelles), an interdisciplinary research group focusing on citizen participation in the city of Brussels. He is also curator and co-director at c-o-m-p-o-s-i-t-e, a Brussels-based non-profit art space. By staging exhibitions Greg tackles questions of knowl- Proceedings of Art Machines: International Symposium on Computational Media Art 2019 175 Anonymous Conjecture Fangqing He New York University Shanghai https://quinnhe.github.io/anonymousConjecture.html Abstract While most celebrities try hard to make their faces recognizable, some decide to hide their identities. Satoshi Nakamoto (the creator of bitcoin), The Residents (a surrealist band), Banksy (world’s top street artist), The Stig (an expert-level test driver) are all mysterious anonymities about whom hundreds of people make conjectures. However, none of them are assumed to be female. The project aims to force people to make new conjectures: the anonymous can be women. Invisible Discrimination Against Women As many feminist campaigns sweep the world, more and more people are becoming aware of issues like gender equality, and sexual harassment etc. However, not all discrimination is physical and visible. It’s easy to ignore mental discrimination against females: something invisible that even some women fail to recognize. Creating an interactive experience, the art project intends to make the audience realize the invisible. The project highlights the anonymous. People love to make guesses about the mysterious celebrities who intentionally hide their identities. If people were questioned about their identities, they would mostly assume that they were male. The creator of bitcoin, Satoshi Nakamoto, is considered to be a 37-year-old man living in Japan. [1] Banksy, one of the world’s top street artists, is regarded as a 28year-old white man. [2] Similarly, people assume The Residents (a famous band) and The Stig (the test driver for Top Gear) are all males. However, can’t women be coding and business geniuses? Can’t women do street graffiti and express to the world their anger against wars and 176 desire for peace? Can’t women play music or be a F1 driver? The answers are, no doubt, yes. What the project does is to present an image of the celebrity that people normally have and break that image by revealing a female body figure gradually. The audience move their hands slowly and sweep the sand on the familiar male faces which gradually reveal the female body figures. Conjecture, Not Conjecture The project does not intend to offer a clear answer for the identities of these anonymous people. However, it wants to suggest that their identities are women. Women can do things that we assume they cannot. Fig 1. Anonymous Conjecture, 2018, Fangqing He Though the project is still in progress, the female figure already tells many stories: a pregnant woman, a woman who smokes on the street, a woman who picks up kids from school. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Anonymous Conjecture. Fangqing He They can be a great role model in any industry, they can be Banksy; they can be Satoshi [fig. 1]. References 1. Steadman, Ian. “The Mysterious Satoshi Nakamoto.” New Statesman, 145, no. 5313 (May 2016), p. 17. 2. “Banksy Identified? Geographic Profiling Pinpoints Identity of Elusive Artist.” Philadelphia Examiner (PA), 5 Mar. 2016. Biography Fangqing (Quinn) is an Interactive Media Arts and Computer Science student from New York University. To explore possibilities of daily life and evoke people’s day-dreamish romance, her works focus more on creating an illusion between unreality and reality with interactive installations. Her creative directions include human-computer interaction, creative coding, programming art, and virtual reality. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 177 Adversarial Ornament Attack Michal Jurgielewicz Rare Resolutions rareresolutions@gmail.com Abstract This project investigates the surface modulation in architectural and landscape design seen through machine vision. Taking an architectural ornament and logic behind the adversarial attack on deep neural networks as the core elements of project, Ornament Attack explores the perturbations in image capture and recognition systems and their effects in built environment. With the influence of socialmedia services on tourism and consumerism trends, the current perception of both cities and remote locations are driven by their photogenic attributes, computational power of photo-editing software and recommendation/marketing algorithms networks. Monopoly of such is a disadvantage to the beauty of diversity in representation. Therefore the creation of a constantly evolving physical and digital ornament disrupting machine vision, parallel to the advancement of machine learning and deep neural networks, can not only shift our perception of space, but also add new categories and behaviors to it, along with a new mythology in which machines believe. Machine eyes “For art to face the machines, it needs to leave the church of humans and become fully processual and transmittable.” [1] Nowadays, we live in a global, highly connected and automated world. Every day we take an active part in an exponential flow of media, products, ideologies, money and technologies. That movement, equipped with tools and platforms aided by neural networks and machine learning, entered our everyday life and reshaped not only our built environment, but the way we experience it. Every time we look at 178 our smartphone or browser, our world gets automatic auto-correction. We wander streets, visiting places that somehow appear, on top of our search results in Google Maps or TripAdvisor. We communicate with hashtags on Instagram and through our satellite eye we travel to places, events, and other people’s life moments while going to work every morning. We purchase products shipped to us through a network of ports and logistic centers located in remote locations operated by the same algorithms that stream our media. If you liked that, then you will love this - tells us our feed constantly. All this is pinned to the precise location of our own behavioral map. “Today’s culture as global culture is very much the processes of de- and reterritorialization. It should be remarked, that “territory” in the ethnological sense, is understood as the environment of a group that cannot itself be objectively located, but is constituted by the patterns of interaction through which the group secures a certain stability and location.” [2] However, in this world of constant optimization and technological advancement we do not take the central place. Companies like “Google, Facebook or Amazon don’t have users or customers [as we would like to think about ourselves]. Instead they have participants under the machine surveillance.” [3] The same companies that provide us with platforms and tools for our everyday life, now design cities and “countryside” for machines that operate next to us. Always-watching autonomous cars and drones delivering our mail are only a prologue to true smart-cities with homes controlled by always listening Amazon Echo type -like assistants and, before we ask ourselves about its architecture, we should understand different relationships that exist in our environment. “Human to human, human to machine and machine to machine - what is the real nature of Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Adversarial Ornament Attack. Michal Jurgielewicz these?” [4] How do machines see and how do they locate themselves in this complex network of assumptions about them? Finally, if architecture is not only a building, but also an infrastructure, what are the spatial implications of that? “What happens when the information necessary to comprehend and operate the environment is not immanent to that environment, but has become decoupled from it? When signs, directions, notifications, alerts and all the other instructions necessary to the fullest use of the city appear only in augmentive overlay and, as will inevitably be the case, that overlay is made available to some but not to others?” [5] On the other side, what are machines capable of seeing? In planetary-scale operations, the Earth is being constantly rendered, unfolding new terrains, structures and behaviors unknown to our sensorium, yet intertwined with the landscape we occupy. In these conditions, Adversarial Ornament Attack becomes the semi-geological force shaping the environment with traces of technological progress new to human culture and machines. This project is a speculative fiction approach to explore the relationship between privacy, space and data. It is a story about the enclaves where you cannot take a photo because the façade patterns and areas are invisible to autonomous-car traffic by their architectural design. However, it is also the story about landscapes emerging in these conditions, perceived only by the machines, created by the collisions in the image classifiers. Adversarial Ornament Attack is filled with nostalgia for the unknown, cities, places and landscapes that exists in our imagination until visited. It mixes craftsmanship with fabrication and neural networks to construct new environments for humans and for the machines to explore, from machine eye perspective. Adversarial Attack Adversarial Attack on Deep Neural Network (DNN) is a subtle modification of an image, invisible to the human eye, which results with misclassification of the image by DNN interpreter. Recent findings show that these networks are very vulnerable to adversarial attacks, even when modified and printed images are captured by regular smartphone camera and tested. 3D objects with a slight change of the texture are misinterpreted as well. A turtle is a rifle, a baseball becomes an espresso cup. Fig 1. Top: Example Attack, Explaining and Harnessing Adversarial Examples, 2015, Ian J Goodfellow, Jonathon Shlens, Christian Szegedy, image, Cornell University Library, Bottom: Fooling Image Recognition with Adversarial Examples, MITCSAIL Youtube Channel, 2017, Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok, video, MIT References 1. Mohammad Salemy, Art after the Machines, Supercommunity: Diabolical Togetherness Beyond Contemporary Art (London: Verso, 2017), 345. 2. Ryszard Wolny, “Gilles Deleuze and Felix Guttari’s Concepts of Deterritorialisation and Retteriorialisation as Globalisation of Culture,” 37. 3. Bruce Sterling, An Epic Struggle of the Internet of Things (Moscow, Strelka Press, 2014) 8. 4. Theodore Spyropoulus, Future Culture (London, AA Lecture Series, 2018). 5. Adam Greenfield, Radical Technologies: The Design of Everyday Life (London: Verso, 2018), 176. Biography Michal Jurgielewicz is an architect, founder of Rare Resolutions, an investigative architecture agency currently based in Bangkok, exploring Proceedings of Art Machines: International Symposium on Computational Media Art 2019 179 Part III. Artistic project abstracts possible presents through constantly changing cultural, technological and geographical landscapes. He took part in international festivals, exhibitions, workshops and seminars in Poland, Italy, The Netherlands. 180 Proceedings of Art Machines: International Symposium on Computational Media Art 2019 The Time Machine: a Multiscreen Generative Video Artwork Daniel Buzzo University of the West of England, Bristol, UK daniel.buzzo@uwe.ac.uk Abstract ‘The Time Machine’ is a multi-screen, highperformance, generative video art installation based around multiple low-cost computer platforms. Using algorithmic selection of palindromic loops of timelapse video the work contrasts the external, machine perception of time with our internal, phenomenological experience of it. The video feeds, recorded from around the world, tick and tock backward and forward creating a polyrhythmic, 12 screen time-piece. The images loop back and forth on each screen of the installation, creating a large polyrhythmic clock of high definition, fullcolor motion. Each screen detailing a passage of time from around the world, captured, frozen, forward and reverse. The time-lapse loops slowly switch, selected from over a thousand separate pieces by generative algorithms on each host computer. Creating a Time Machine reflecting the world, gently rocking back and forth with a myriad of subcadences, confronting the viewer with the unanswerable challenge of comprehending time. Introduction The work uses looping time-lapse video shot in locations around the world to engage the viewer with a discussion on the experience, rhythm, repetition and flow of time. Running across multiple monitor screens the installation senses the audience and in response creates palindromic video loops from high resolution time-lapse video. The video feeds, recorded from around the world, tick and tock backward and forward creating a polyrhythmic, multiscreen time-piece, a video-clock locked in receptive, slowly evolving loops. A Time Machine reflecting the world. The backward and forth looping of the video feeds engage the viewer with both the reassurance and the discomfort of seeing the world as “clock-time.” The mechanistic vision that time is something created and measured, governed and ruled externally to ourselves and external to our experience. Fig 1. THE TIME MACHINE 2017, Daniel Buzzo multi-screen generative video installation, Copyright the author. The piece is a companion to the 2016 dual screen installation “What Do We Know Of Time When All We Can Know For Real Is Now?” [1] [2] Exhibited at events such as “Digital Futures,” Victoria & Albert Museum, Computer Art Congress 5, Paris ACM MM at OBA in Amsterdam. The work “The Time Machine” contrasts the external, machine perception of time with our internal, phenomenological experience of it. The notion of ‘clock time’ is a powerful and extremely widely adopted metaphor for what can be argued as the most fundamental element of experience. [3] Time links all things we see and perceive, from our earliest awareness of our own physical growth and mortality to more Proceedings of Art Machines: International Symposium on Computational Media Art 2019 181 Part III. Artistic project abstracts subtle realizations of the narrative procession of events and even the concept of causality. [4] The complexity of dissembling what this experience means has been wrestled with for millennia, as Augustine of Hippo asked in 400AD "What then is time? If no one asks me, I know: if I wish to explain it to one that asketh, I know not." St Augustine’s Confessions, Book IX The model of time we have in daily life treats the ideal of “Now” as a special moment, though this may be particular to humans. It gives the notion of the “unfolding” of the universe and shows time as a continuum. [5] Human convention may dictate we travel along this because, as Augustine of Hippo postulated in 400AD, humans have fallible perception and cannot see the world as it truly is. Augustine argues, how can that which is not real (the Future) become real (the present) and then become unreal again (the past)? The evidence and the balance of the philosophical argument is for procession and flow. What Heraclitus, and subsequently Nietszche described as all is chaos and becoming. However, clock time, an external mechanical, industrial notion of time, has become dominant since the turn of the last century. [6] The patterns and rhythms seen are considered cyclic, oscillating and reciprocating like the cogs and gears in a clock. Even the movements of stars moon and planets around us are considered as an orrery, a child’s instructional toy to describe the universe. This work presents this mechanical clock fiction direct to the viewer. Folding half a dozen different types of time together in a multi-screen video form. Time lapse video from different time zones shifted and collated together, sunshine alongside moon light, dawn next to the falling of dusk. The video loops back and forth on each screen of the installation, creating a large polyrhythmic clock of high definition, full color motion. Each screen detailing a passage of time from around the world, captured, frozen, forward and reverse. The time-lapse loops slowly switch, selected from over a thousand separate pieces by generative algorithms on each host 182 computer. Creating a slowly evolving and changing time machine. Gently rocking back and forth with a myriad of sub cadences, confronting the viewer with the unanswerable challenge of comprehending time. References 1. Daniel Buzzo, “What Do We Know Of Time When All We Can Know For Real Is Now,” in Proceedings of the 5th Computer Art Congress (Paris: Europia Press, 2016). 2. http://buzzo.com/what-do-we-know-of-timewhen-all-we-can-know-for-real-is-now/ 3. N.D. Munn, “The Cultural Anthropology of Time: A Critical Essay,” Annual Review of Anthropology, 21, no. 1 (1992): 93–123. 4. T. Garcia, and K. Pender, “Another Order Of Time: Towards a Variable Intensity of the Now,” Parrhesia: A Journal Of Critical Philosophy, 19 (2014): 1–13. 5. E. Husserl, On the Phenomenology of the Consciousness of Internal Time (1893-1917), in Edmund Husserl Collected Works (1991). 6. J. Martineau, Time, Capitalism and Alienation (Brill, 2015). Biography Dr. Daniel Buzzo is a media artist, interaction designer, researcher and senior lecturer in Digital Media and Creative Technologies in UK, Netherlands and Hong Kong. He is a founder member of the Creative Technologies Lab at the University of the West of England and program leader of the Master program in Creative Technology. His experimental interactive media art work is intimately bound in time, temporality and lens-based visualization. He constructs and uses experimental cameras and data visualization systems for urban imaging, street photography and visualization. He publishes and presents widely and his work has been shown at international exhibitions, galleries and conferences including Digital Futures at Victoria and Albert Museum, London; Computer Art Congress, Paris; International Symposium of Electronic Art (ISEA) Colombia; DataAesthetics at ACM MultiMedia, Amsterdam; GENART XX, Italy; and Carbon Silicon at Oriel Sycharth Gallery. Proceedings of Art Machines: International Symposium on Computational Media Art 2019 Part IV Review Board 183 Part IV. Review Board Review Board of Art Machines: International Symposium on Computational Media Art Tanya Toft Ag, City University of Hong Kong, Urban Media Art Academy Gustavo Armagno, Universidad de la República Javier Baliosian, Universidad de la República Maurice Benayoun, City University of Hong Kong Alvaro Cassinelli, University of Tokyo Lin Chang, National Tsing-Hua University Damien Charrieras, City University of Hong Kong Budhaditya Chattopadhyay, American University of Beirut John Drever, Goldsmiths, University of London Hongbo Fu, City University of Hong Kong Daniel Howe, City University of Hong Kong Tobias Klein, School of Creative Media Dietmar Koering, Arphenotype Gene Kogan Harald Kraemer, City University of Hong Kong Kin Chung Kwan, City University of Hong Kong Linda Lai, City University of Hong Kong Tomas Laurenzo, City University of Hong Kong Guillermo Moncecchi, Universidad de la República Lisa So Young Park, City University of Hong Kong Jane Prophet, University of Michigan Anna Ridler Alejandro Rodriguez, dogrush Hector Rodriguez, City University of Hong Kong Pilar Rosado Rodrigo, Universitat de Barcelona Margaret Schedel, Stony Brook University Jeffrey Shaw, City University of Hong Kong Malina Siu, City University of Hong Kong Ayoung Suh, City University of Hong Kong Jeff Thompson, Stevens Institute of Technology Ken Ueno, City University of Hong Kong Guan Wang, City University of Hong Kong Pengfei Xu, Shenzhen University Dongming Yan, NLPR-CASIA Yang Yeung, Chinese University of Hong Kong Kaho Yu, City University of Hong Kong Bo Zheng, City University of Hong Kong 184 Proceedings of Art Machines: International Symposium on Computational Media Art 2019

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