Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as (...) AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by _locked_ abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead _unlocked_. (shrink)

This article conducts a mixed-method content analysis of Chinese and American news media coverage of Google DeepMind’s Go playing computer program, AlphaGo. Drawing on humanistic approaches to artificial intelligence, combined with an empirically rigorous content analysis, it examines the differences and overlap in coverage by the Chinese and American press in their accounts of AlphaGo, and its historic match with Korea’s Lee Sedol in March, 2016. The event was not only followed intensely in China, but also made the (...) front page of The New York Times. This article finds that the Chinese press was more likely than the American press to frame AlphaGo as non-threatening, which the authors attribute to cultural differences and the two countries' different understandings of Go. In addition to quantitatively identifying similarities and differences in the framing of AlphaGo, this paper also investigates the underlying and evolving contestations over what constitutes the “human” and the “machine.” It concludes by discussing the implications of the study’s findings as well as outlining avenues for further research. (shrink)

Under the rubric of understanding the problem of explainability of AI in terms of abductive cognition, I propose to review the lessons from AlphaGo and her more powerful successors. As AI players in Baduk have arrived at superhuman level, there seems to be no hope for understanding the secret of their breathtakingly brilliant moves. Without making AI players explainable in some ways, both human and AI players would be less-than omniscient, if not ignorant, epistemic agents. Are we bound to (...) have less explainable AI Baduk players as they make further progress? I shall show that the resolution of this apparent paradox depends on how we understand the crucial distinction between abduction and inference to the best explanation. Some further philosophical issues arising from explainable AI will also be discussed in connection with this distinction. (shrink)

It is time to update Deleuze and Guattari's contrast between Chess and Go in the ‘Nomadology’ Plateau with a discussion of AlphaGo, the artificial intelligence that revolutionised Go in 2016. I focus less on the political issues in Go nomadology, central as they are, and more on smooth space and time. I explain and speculate on some details in Go strategy, as well as some processes of machine learning. The relations between human Go, computer Go, and smooth-time nomadology remain (...) unsettled, in my view, but many resources for nomadological analysis open up for readers who learn to play, and program, Go. (shrink)

The victories of the Go-playing artificial intelligence “AlphaGo” against professional player Lee Sedol in 2016 had a profound impact on public and academic perceptions of AI. This event shocked observers, as the ability of a machine to defeat a world champion human in a highly complex game seemed to indicate that a machine had achieved human-like—or more than human—intelligence. But why was AlphaGo so readily anthropomorphized by academic and non-academic audiences alike? Drawing from existing analyses of reactions to (...) and arguments concerning AlphaGo and AI generally, this paper argues that “generative” cognitive science—a school of thought exemplified by the linguistic work of Noam Chomsky—offers two novel contributions to this subject. First, generativism sheds light on an irrational double standard in the study of the human mind in contrast to the study of non-cognitive systems—“methodological dualism”—which, I argue, has been transferred to evaluations of AlphaGo and other AI. Second, by exposing this irrational double standard in perceptions of AI, I employ generativism’s more well-known arguments concerning the nature of human intelligence and its scientific study to the evaluation of AI, exposing deficient interpretations widely used in the case of AlphaGo and AI generally. (shrink)

_Locked_ and _unlocked_ strategies are illustrated in this article as concepts that deal with important cognitive aspects of deep learning systems. They indicate different inference routines that refer to poor (locked) to rich (unlocked) cases of creative production of creative cognition. I maintain that these differences lead to important consequences when we analyze computational deep learning programs, such as AlphaGo/AlphaZero, which are able to realize various types of abductive hypothetical reasoning. These programs embed what I call locked abductive strategies, (...) so, even if they present spectacular performances for example in games, they are characterized by poor types of hypothetical creative cognition insofar as they are constrained in what I call _eco-cognitive openness_. This openness instead characterizes unlocked human cognition that pertains to higher kinds of abductive reasoning, in both the creative and diagnostic cases, in which cognitive strategies are instead unlocked. This special kind of “openness” is physically rooted in the fundamental character of the human brain as an open system constantly coupled with the environment (that is, an “open” or “dissipative” system): its activity is the uninterrupted attempt to achieve the equilibrium with the environment in which it is embedded, and this interplay can never be switched off without producing severe damage to the brain. The brain cannot be conceived as deprived of its physical quintessence that is its openness. In the brain, contrary to the computational case, ordering is not derived from the outside thanks to what I have called in a recent book “computational domestication of ignorant entities”, but it is the direct product of an “internal” open dynamical process of the system. (shrink)

The purpose of this paper is to study the philosophical basis of the architecture of two Artificial Intelligence systems (AlphaGo and Hide and Seek). The problem considers the elucidation of the epistemological distinction between “knowledge” and “intuition” and questions whether the fulfillment of a programmed metric can be considered as a product of knowledge or, as an example of intuitive thinking. Through the analysis of both examples, it is shown that achieving the desired objective (winning a game of Go (...) and developing new hiding strategies) is not derived from the system’s architecture, but from the AI’s creativity to achieve the desired result; in that sense, it can be confirmed that certain intelligent systems are capable of creative and intuitive thinking. El objetivo del presente artículo es estu- diar los fundamentos filosóficos de la arquitectura de programación en dos sistemas de Inteligencia Artificial (AlphaGo y Hide and Seek). El problema dilucida la distinción epistemológica de los concep- tos “conocimiento”, “intuición” y “abducción”, para definir si el cumplimiento exitoso de una métrica programada, por parte de una computadora, es con- dición suficiente para atribuirle un comportamiento inteligente. A través del análisis de ambos ejemplos se muestran dos cuestiones: i) el cumplimiento exitoso de un objetivo programado ofrece nuevos recursos de conocimiento, ii) dichos conocimientos dependen de la ejecución de un programa cuyo pro- cesamiento es desarrollado por una IA y, por tanto, las operaciones superan las capacidades intelectivas humanas. Las conclusiones apuntan a que las com- putadoras son recursos de conocimiento especiales de comprobación de hipótesis. (shrink)

인공지능 칩을 뇌에 장착함으로써 인간의 인지능력을 향상시킬 수 있을까? 이런 향상이 실현될 수 있으려면 어떤 조건이 충족되어야 하는가? 이 논문은 인공지능이 인간 인지체계의 일부로 작동할 수 있을 조건을 따짐으로써 인공지능을 활용하여 인지능력을 향상하는 일을 전망하는 데 일조하려 한다. 이 일을 위하여 이 논문은 중앙신경체계에 연결되어 생각대로 움직이는 의수의 경우, 뇌에 알파고 칩을 장착하고 바둑을 두는 사람의 경우, 그리고 외국어 통 · 번역 인공지능을 작동하는 칩을 두개골 안에 이식한 사람의 경우를 차례로 검토한다. 생체공학 의수는 인공지능으로 사람이 더 똑똑해지는 경우는 아니지만 인공지능이 (...) 인지체계의 작동을 보완하는 실질적인 활용의 가능성을 함축하고 있다. 뇌 안에 이식된 알파고 칩에 관한 검토는 모종의 확률이 개입하는 인지능력 향상이 가능하다는 결론에 도달하지만, 그 과정에서 결정과 제어의 주체에 관한 물음을 촉발하고 이로 인해 ‘인간이 인공지능을 활용한다’는 구조를 재고하게 한다. 통 · 번역 인공지능의 경우에 대한 검토는 이 기술을 활용할 수 있게 할 특정 방식을 전망하게 하지만, 그 방식은 그 사람의 인지능력 자체를 향상하는 것이 아니다. 인간의 뇌가 언어의 의미를 어떻게 처리하는지 해명되기 전까지는 통 · 번역 인공지능을 인간 인지체계의 일부로 활용하는 일이 불가능할 것이다. (shrink)

In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the world‐champion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as “creative” by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the (...) form of Monte Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI. (shrink)

In domains as disparate as playing Go and predicting the structure of proteins, artificial intelligence (AI) technologies have begun to perform at levels beyond which any humans can achieve. Does this fact represent something lamentable? Does superhuman AI performance somehow undermine the value of human achievements in these areas? Go grandmaster Lee Sedol suggested as much when he announced his retirement from professional Go, blaming the advances of Go-playing programs like AlphaGo for sapping his will to play the game (...) at a high level. In this paper, I attempt to make sense of Sedol’s lament. I consider a number of ways that the existence of superhuman-performing AI technologies could undermine the value of human achievements. I argue there is very little in the nature of the technology itself that warrants such despair. (Compare: does the existence of a fighter jet undermine the value of being the fastest human sprinter?) But I also argue there are several more localized domains where these technologies threaten to displace human beings from being able to achieve valuable things at all. This is a particular worry for those in unequal societies, I argue, given the difficulty of many achievements and the corresponding amount of resources needed to achieve great things. (shrink)

Artificial agents are progressively becoming more present in everyday-life situations and more sophisticated in their interaction affordances. In some specific cases, like Google Duplex, GPT-3 bots or Deep Mind’s AlphaGo Zero, their capabilities reach or exceed human levels. The use contexts of everyday life necessitate making such agents understandable by laypeople. At the same time, displaying human levels of social behavior has kindled the debate over the adoption of Dennett’s ‘intentional stance’. By means of a comparative analysis of the (...) literature on robots and virtual agents, we defend the thesis that approaching these artificial agents ‘as if’ they had intentions and forms of social, goal-oriented rationality is the only way to deal with their complexity on a daily base. Specifically, we claim that this is the only viable strategy for non-expert users to understand, predict and perhaps learn from artificial agents’ behavior in everyday social contexts. Furthermore, we argue that as long as agents are transparent about their design principles and functionality, attributing intentions to their actions is not only essential, but also ethical. Additionally, we propose design guidelines inspired by the debate over the adoption of the intentional stance. (shrink)

State-of-the-art machine learning systems now routinely exceed benchmarks once thought beyond the ken of artificial intelligence (AI). Often these systems accomplish tasks through novel, insightful processes that remain inscrutable to even their human designers. Taking AlphaGo’s 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford’s 2015 book Achievement. Achievements like these are directly attributable to AI systems themselves. They are artificial achievements. This (...) opens the door to a challenge that calls out for further inquiry. Since Bradford grounds the intrinsic value of achievements in the exercise of distinctively human perfectionist capacities, the existence of artificial achievements raises the possibility that some achievements might be valueless. (shrink)

There has been a push in recent years to provide better explanations for how AIs make their decisions. Most of this push has come from the ethical concerns that go hand in hand with AIs making decisions that affect humans. Outside of the strictly ethical concerns that have prompted the study of explainable AIs (XAIs), there has been research interest in the mere possibility of creating XAIs in various domains. In general, the more accurate we make our models the harder (...) they are to explain. Go playing AIs like AlphaGo and KataGo provide fantastic examples of this phenomenon. In this paper, I discuss a non-exhaustive list of the leading theories of explanation and what each of these theories would say about the explainability of AI-played moves of Go. Finally, I consider the possibility of ever explaining AI-played Go moves in a way that meets the four principles of XAI. I conclude, somewhat pessimistically, that Go is not as imminently explainable as other domains. As such, the probability of having an XAI for Go that meets the four principles is low. (shrink)

In my opinion, it is only in the framework of a research dealing with abductive cognition that we can analyze important cognitive aspects of human and machine capacities. From the point of view of human capacities the phenomenological concept of anticipation, which is related to the problem of the spontaneous generation of spatiality and its three-dimensionality, will be central. I will describe that anticipations can be seen as types of visual and manipulative abduction and also fruitful to illustrate, in the (...) case of human and machine capacities, the respective role of two kinds of strategic reasoning: locked and unlocked abductive strategies, which characterize the basic cognitive pro- cedure of “reading ahead”. The specificity of these contrasting inferential strategies is also related to their potentiality in producing different kinds of hypothetical outcomes, which in turn represent dissimilar levels of knowledge creativity. This diversity is also fundamental to depict the special character, the kind of creativity, and the limits of current computational deep learning AI systems, such as AlphaGo, which realize abductive cognitive processes. (shrink)

Beginning with a survey of the shortcoming of theories of organology/media-as-externalization of mind/body—a philosophical-anthropological tradition that stretches from Plato through Ernst Kapp and finds its contemporary proponent in Bernard Stiegler—I propose that the phenomenological treatment of media as an outpouching and extension of mind qua intentionality is not sufficient to counter the ̳black-box‘ mystification of today‘s deep learning‘s algorithms. Focusing on a close study of Simondon‘s On the Existence of Technical Objectsand Individuation, I argue that the process-philosophical work of Gilbert (...) Simondon, with its critique of Norbert Wiener‘s first-order cybernetics, offers a precursor to the conception of second-order cybernetics (as endorsed byFrancisco Varela, Humberto Maturana, and Ricardo B. Uribe) and, specifically, its autopoietic treatment of information. It has been argued by those such as Frank Pasquale that neuro-inferential deep learning systems premised on predictive patterning, suchas AlphaGo Zero, have a veiled logic and, thus, are ̳black boxes‘. In detailing a philosophical-historical approach to demystify predictive patterning/processing and the logic of such deep learning algorithms, this paper attempts to shine a light on such systems and their inner workingsàla Simondon. (shrink)