Philosophy of Cognitive Science > Philosophy of Artificial Intelligence

Philosophy of Artificial Intelligence

Edited by Eric Dietrich (State University of New York at Binghamton)
About this topic
Summary

The philosophy of artificial intelligence is a collection of issues primarily concerned with whether or not AI is possible -- with whether or not it is possible to build an intelligent thinking machine.  Also of concern is whether humans and other animals are best thought of as machines (computational robots, say) themselves. The most important of the "whether-possible" problems lie at the intersection of theories of the semantic contents of thought and the nature of computation. A second suite of problems surrounds the nature of rationality. A third suite revolves around the seeming “transcendent” reasoning powers of the human mind. These problems derive from Kurt Gödel's famous Incompleteness Theorem.  A fourth collection of problems concerns the architecture of an intelligent machine.  Should a thinking computer use discrete or continuous modes of computing and representing, is having a body necessary, and is being conscious necessary.  This takes us to the final set of questions. Can a computer be conscious?  Can a computer have a moral sense? Would we have duties to thinking computers, to robots?  For example, is it moral for humans to even attempt to build an intelligent machine?  If we did build such a machine, would turning it off be the equivalent of murder?  If we had a race of such machines, would it be immoral to force them to work for us?
Key works Probably the most important attack on whether AI is possible is John Searle's famous Chinese Room Argument: Searle 1980.  This attack focuses on the semantic aspects (mental semantics) of thoughts, thinking, and computing.   For some replies to this argument, see the same 1980 journal issue as Searle's original paper.  For the problem of the nature of rationality, see Pylyshyn 1987.  An especially strong attack on AI from this angle is Jerry Fodor's work on the frame problem: Fodor 1987.  On the frame problem in general, see McCarthy & Hayes 1969.  For some replies to Fodor and advances on the frame problem, see Ford & Pylyshyn 1996.  For the transcendent reasoning issue, a central and important paper is Hilary Putnam's Putnam 1960.  This paper is arguably the source for the computational turn in 1960s-70s philosophy of mind.  For architecture-of-mind issues, see, for starters: M. Spivey's The Contintuity of Mind, Oxford, which argues against the notion of discrete representations. See also, Gelder & Port 1995.  For an argument for discrete representations, see, Dietrich & Markman 2003.  For an argument that the mind's boundaries do not end at the body's boundaries, see, Clark & Chalmers 1998.  For a statement of and argument for computationalism -- the thesis that the mind is a kind of computer -- see Shimon Edelman's excellent book Edelman 2008. See also Chapter 9 of Chalmers's book Chalmers 1996.
Introductions Chinese Room Argument: Searle 1980. Frame problem: Fodor 1987, Computationalism and Godelian style refutation: Putnam 1960. Architecture: M. Spivey's The Contintuity of Mind, Oxford and Shimon Edelman's Edelman 2008. Ethical issues: Anderson & Anderson 2011.  Conscious computers: Chalmers 2011.
Related categories
Subcategories:
Can Machines Think? (1,989 | 383)
The Turing Test (444)
Godelian Arguments Against AI (262)
The Chinese Room (254)
Machine Consciousness (321)
Machine Mentality, Misc (325)
Computation and Representation (701 | 105)
Symbols and Symbol Systems (280)
Computational Semantics (83)
Implicit/Explicit Rules and Representations (67)
AI without Representation? (40)
Representation in Connectionism* (105)
Subsymbolic Computation* (33)
Computation and Representation, Misc (126)
Connectionism and Neural Networks (884 | 481)
Connectionism and Compositionality (68)
Representation in Connectionism* (105)
Connectionism and Eliminativism (23)
The Connectionist/Classical Debate (47)
Subsymbolic Computation* (33)
Philosophy of Connectionism, Misc (97)
Philosophy of Connectionism, Foundational Empirical Issues (30)
Areas of Artificial Intelligence (1,074 | 257)
Artificial Life* (225)
Computer Vision (3)
Cybernetics (166)
Dynamical Systems (213)
The Frame Problem (72)
Machine Learning (27)
Natural Language Processing (31)
Areas of Artificial Intelligence, Misc (26)
Robotics (281)
Computationalism (538 | 538)
Computationalism in Cognitive Science* (574)
Computation and Physical Systems* (499 | 3)
Ethics of Artificial Intelligence (1,147 | 2)
Artificial Intelligence Safety* (110)
Moral Status of Artificial Systems (315)
Robot Ethics* (308)
Machine Ethics (324)
Ethics of Artificial Intelligence, Misc (506)
Impact of Artificial Intelligence (626 | 43)
Artificial Intelligence Safety* (110)
Cyborgs (10)
Mind Uploading (35)
The Singularity (67)
Transhumanism (348)
Impact of Artificial Intelligence, Misc (15)
Philosophy of Artificial Intelligence, Miscellaneous (728 | 367)
Artificial Intelligence Methodology (100)
The Nature of Artificial Intelligence (100)
Philosophy of AI, General Works (50)
Philosophy of AI, Misc (111)
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  1. The Role of Experts in the Public Perception of Risk of Artificial Intelligence.Hugo Neri & Fabio Cozman - forthcoming - AI and Society.
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  2. Dance of the Artificial Alignment and Ethics.Karamjit S. Gill - forthcoming - AI and Society.
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  3. Artificial Intelligence Assistants and Risk: Framing a Connectivity Risk Narrative.Martin Cunneen, Martin Mullins & Finbarr Murphy - forthcoming - AI and Society.
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  4. Privacy, Autonomy, and Personalised Targeting: Rethinking How Personal Data is Used.Karina Vold & Jessica Whittlestone - forthcoming - In Carissa Véliz (ed.), Report on Data, Privacy, and the Individual in the Digital Age.
    Technological advances are bringing new light to privacy issues and changing the reasons for why privacy is important. These advances have changed not only the kind of personal data that is available to be collected, but also how that personal data can be used by those who have access to it. We are particularly concerned with how information about personal attributes inferred from collected data (such as online behaviour), can be used to tailor messages and services to specific individuals or (...)
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  5. The Limits of Machine Intelligence.Henry Shevlin, Karina Vold, Matthew Crosby & Marta Halina - 2019 - EMBO Reports 49177 (20).
    Despite there being little consensus on what intelligence is or how to measure it, the media and the public have become increasingly preoccupied with the concept owing to recent accomplishments in machine learning and research on artificial intelligence (AI). Governments and corporations are investing billions of dollars to fund researchers who are keen to produce an ever‐expanding range of artificial intelligent systems. More than 30 countries have announced such research initiatives over the past 3 years 1. For example, the EU (...)
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  6. Francesca Ferrando : Philosophical Posthumanism. Theory in the New Humanities, Series Editor: Rosi Braidotti, Preface by Rosi Braidotti). Bloomsbury Academic , Hardcover, 296 Pages, ISBN-10: 1350059501, ISBN-13: 978-1350059504. [REVIEW]Yvonne Förster - forthcoming - AI and Society.
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  7. What Does It Mean to Understand? Neural Networks Case.Albert Ierusalem & Aleksandr Senin - manuscript
    We can say that we understand neural networks then and only then if you will come to me and say that the best model ever for some task has a 100 layers, and I will answer "No! 101 layers model is the best!".
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  8. Is Cybersecurity a Public Good?Mariarosaria Taddeo - 2019 - Minds and Machines 29 (3):349-354.
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  9. Correction To: Interperforming in AI: Question of ‘Natural’ in Machine Learning and Recurrent Neural Networks.Tolga Yalur - forthcoming - AI and Society.
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  10. Cognitive Architecture, Holistic Inference and Bayesian Networks.Timothy J. Fuller - 2019 - Minds and Machines 29 (3):373-395.
    Two long-standing arguments in cognitive science invoke the assumption that holistic inference is computationally infeasible. The first is Fodor’s skeptical argument toward computational modeling of ordinary inductive reasoning. The second advocates modular computational mechanisms of the kind posited by Cosmides, Tooby and Sperber. Based on advances in machine learning related to Bayes nets, as well as investigations into the structure of scientific and ordinary information, I maintain neither argument establishes its architectural conclusion. Similar considerations also undermine Fodor’s decades-long diagnosis of (...)
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  11. Key Ethical Challenges in the European Medical Information Framework.Luciano Floridi, Christoph Luetge, Ugo Pagallo, Burkhard Schafer, Peggy Valcke, Effy Vayena, Janet Addison, Nigel Hughes, Nathan Lea, Caroline Sage, Bart Vannieuwenhuyse & Dipak Kalra - 2019 - Minds and Machines 29 (3):355-371.
    The European Medical Information Framework project, funded through the IMI programme, has designed and implemented a federated platform to connect health data from a variety of sources across Europe, to facilitate large scale clinical and life sciences research. It enables approved users to analyse securely multiple, diverse, data via a single portal, thereby mediating research opportunities across a large quantity of research data. EMIF developed a code of practice to ensure the privacy protection of data subjects, protect the interests of (...)
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  12. Qualitative Models in Computational Simulative Sciences: Representation, Confirmation, Experimentation.Nicola Angius - 2019 - Minds and Machines 29 (3):397-416.
    The Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how (...)
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  13. The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David Watson - 2019 - Minds and Machines 29 (3):417-440.
    Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...)
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  14. A Common Frame for Formal Imagination.Joan Casas-Roma, M. Elena Rodríguez & Antonia Huertas - forthcoming - Minds and Machines:1-32.
    In this paper, we review three influential theories of imagination in order to understand how the dynamics of imagination acts could be modeled using formal languages. While reviewing them, we notice that they are not detailed enough to account for all the mechanisms involved in creating and developing imaginary worlds. We claim those theories could be further refined into what we call the Common Frame for Imagination Acts, which defines a framework that can be used to study the dynamics of (...)
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  15. Helen A'Loy and Other Tales of Female Automata: A Gendered Reading of the Narratives of Hopes and Fears of Intelligent Machines and Artificial Intelligence.Rachel Adams - forthcoming - AI and Society.
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  16. Community Protocols for Researchers: Using Sketches to Communicate Interaction Guidelines.Naska Goagoses, Heike Winschiers-Theophilus & Tariq Zaman - forthcoming - AI and Society.
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  17. Decentered Ethics in the Machine Era and Guidance for AI Regulation.Christian Hugo Hoffmann & Benjamin Hahn - forthcoming - AI and Society.
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  18. Limiting the Discourse of Computer and Robot Anthropomorphism in a Research Group.Matthew J. Cousineau - 2019 - AI and Society 34 (4):877-888.
    Social science research on the anthropomorphisms of computers and robots has been devoted to studying intellectual anthropomorphism, emotional anthropomorphism, bodily anthropomorphism, and the limits of computer and robot anthropomorphism. Although these represent important patterns for studying the anthropomorphisms of computers and robots, there are other important patterns. The limitation of anthropomorphism is one of these patterns. The limitation of anthropomorphism is a discursive practice which places limits on anthropomorphism. Discursive practices are interactional and practical activities for making sense of who (...)
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  19. “Intelligent” Finance and Treasury Management: What We Can Expect.Petr Polak, Christof Nelischer, Haochen Guo & David C. Robertson - forthcoming - AI and Society.
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  20. Information and Friend Segregation for Online Social Networks: A User Study.Javed Ahmed, Serena Villata & Guido Governatori - 2019 - AI and Society 34 (4):753-766.
    Online social networks captured the attention of the masses by offering attractive means of sharing personal information and developing social relationships. People expose personal information about their lives on OSNs. This may result in undesirable consequences of users’ personal information leakage to an unwanted audience and raises privacy concerns. The issue of privacy has received a significant attention in both the research literature and the mainstream media. In this paper, we present results of an empirical study that measure users’ attitude (...)
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  21. Using Game Description Language for Mediated Dispute Resolution.Dave de Jonge, Tomas Trescak, Carles Sierra, Simeon Simoff & Ramon López de Mántaras - 2019 - AI and Society 34 (4):767-784.
    Mediation is a process in which two parties agree to resolve their dispute by negotiating over alternative solutions presented by a mediator. In order to construct such solutions, the mediator brings more information and knowledge, and, if possible, resources to the negotiation table. In order to do so, the mediator faces the challenge of determining which information is relevant to the current problem, given a vast database of knowledge. The contribution of this paper is the automated mediation machinery to resolve (...)
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  22. The Change of Signaling Conventions in Social Networks.Roland Mühlenbernd - 2019 - AI and Society 34 (4):721-734.
    To depict the mechanisms that have enabled the emergence of semantic conventions, philosophers and researchers particularly access a game-theoretic model: the signaling game. In this article I argue that this model is also quite appropriate to analyze not only the emergence of a semantic convention, but also its change. I delineate how the application of signaling games helps to reproduce and depict mechanisms of semantic change. For that purpose I present a model that combines a signaling game with innovative reinforcement (...)
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  23. Ethics of AI and Cybersecurity When Sovereignty is at Stake.Paul Timmers - forthcoming - Minds and Machines:1-11.
    Sovereignty and strategic autonomy are felt to be at risk today, being threatened by the forces of rising international tensions, disruptive digital transformations and explosive growth of cybersecurity incidents. The combination of AI and cybersecurity is at the sharp edge of this development and raises many ethical questions and dilemmas. In this commentary, I analyse how we can understand the ethics of AI and cybersecurity in relation to sovereignty and strategic autonomy. The analysis is followed by policy recommendations, some of (...)
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  24. Evil and Roboethics in Management Studies.Enrico Beltramini - 2019 - AI and Society 34 (4):921-929.
    In this article, I address the issue of evil and roboethics in the context of management studies and suggest that management scholars should locate evil in the realm of the human rather than of the artificial. After discussing the possibility of addressing the reality of evil machines in ontological terms, I explore users’ reaction to robots in a social context. I conclude that the issue of evil machines in management is more precisely a case of technology anthropomorphization.
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  25. Screen Reading and the Creation of New Cognitive Ecologies.Robert W. Clowes - 2019 - AI and Society 34 (4):705-720.
    It has been widely argued that digital technologies are transforming the nature of reading, and with it, our brains and a wide range of our cognitive capabilities. In this article, we begin by discussing the new analytical category of deep-reading and whether it is really on the decline. We analyse deep reading and its grounding in brain reorganization, based upon Michael Anderson’s Massive Redeployment hypothesis and Dehaene’s Neuronal Recycling which both help us to theorize how the capacities of brains are (...)
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  26. The Brain as Artificial Intelligence: Prospecting the Frontiers of Neuroscience.Steve Fuller - 2019 - AI and Society 34 (4):825-833.
    This article explores the proposition that the brain, normally seen as an organ of the human body, should be understood as a biologically based form of artificial intelligence, in the course of which the case is made for a new kind of ‘brain exceptionalism’. After noting that such a view was generally assumed by the founders of AI in the 1950s, the argument proceeds by drawing on the distinction between science—in this case neuroscience—adopting a ‘telescopic’ or a ‘microscopic’ orientation to (...)
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  27. Augmented Societies with Mirror Worlds.Alessandro Ricci, Luca Tummolini & Cristiano Castelfranchi - 2019 - AI and Society 34 (4):745-752.
    Computing systems can function as augmentation of individual humans as well as of human societies. In this contribution, we take mirror worlds as a conceptual blueprint to envision future smart environments in which the physical and the virtual layers are blended into each other. We suggest that pervasive computing technologies can be used to create a coupling between these layers, so that actions or, more generally, events in the physical layer would have an effect in the virtual layer and viceversa. (...)
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  28. The Computational Therapeutic: Exploring Weizenbaum’s ELIZA as a History of the Present.Caroline Bassett - 2019 - AI and Society 34 (4):803-812.
    This paper explores the history of ELIZA, a computer programme approximating a Rogerian therapist, developed by Jospeh Weizenbaum at MIT in the 1970s, as an early AI experiment. ELIZA’s reception provoked Weizenbaum to re-appraise the relationship between ‘computer power and human reason’ and to attack the ‘powerful delusional thinking’ about computers and their intelligence that he understood to be widespread in the general public and also amongst experts. The root issue for Weizenbaum was whether human thought could be ‘entirely computable’. (...)
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  29. Non-Artificial Non-Intelligence: Amazon’s Alexa and the Frictions of AI.Tero Karppi & Yvette Granata - 2019 - AI and Society 34 (4):867-876.
    This paper examines a case where Amazon’s cloud-based AI assistant Alexa accidentally ordered a dollhouse for a 6-year-old girl. In the press, the case was defined as a technical recognition problem. Building on this idea, we argue that the dollhouse case helps us to analyze the limits of current AI applications. By drawing on the writings of Gilles Deleuze and François Laruelle, we argue that these limits are not merely technical but more deeply embedded in the structures where the thinking (...)
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  30. Social Intelligence: How to Integrate Research? A Mechanistic Perspective.Marcin Miłkowski - 2019 - AI and Society 34 (4):735-744.
    Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different explanations, however, comes (...)
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  31. The Reappearing Tool: Transparency, Smart Technology, and the Extended Mind.Michael Wheeler - 2019 - AI and Society 34 (4):857-866.
    Some thinkers have claimed that expert performance with technology is characterized by a kind of disappearance of that technology from conscious experience, that is, by the transparency of the tools and equipment through which we sense and manipulate the world. This is a claim that may be traced to phenomenological philosophers such as Heidegger and Merleau-Ponty, but it has been influential in user interface design where the transparency of technology has often been adopted as a mark of good design. Moreover, (...)
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  32. Machine Learning: A Structuralist Discipline?Christophe Bruchansky - 2019 - AI and Society 34 (4):931-938.
    Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce some of (...)
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  33. Two-Dimensional Opinion Dynamics in Social Networks with Conflicting Beliefs.Shuwei Chen, David H. Glass & Mark McCartney - 2019 - AI and Society 34 (4):695-704.
    Two models are developed for updating opinions in social networks under situations where certain beliefs might be considered to be competing. These two models represent different attitudes of people towards the perceived conflict between beliefs. In both models agents have a degree of tolerance, which represents the extent to which the agent takes into account the differing beliefs of other agents, and a degree of conflict, which represents the extent to which two beliefs are considered to be competing. Computer simulations (...)
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  34. A Cross-Cultural Assessment of the Semantic Dimensions of Intellectual Humility.Markus Christen, Mark Alfano & Brian Robinson - 2019 - AI and Society 34 (4):785-801.
    Intellectual humility can be broadly construed as being conscious of the limits of one’s existing knowledge and capable of acquiring more knowledge, which makes it a key virtue of the information age. However, the claim “I am humble” seems paradoxical in that someone who has the disposition in question would not typically volunteer it. Therefore, measuring intellectual humility via self-report may be methodologically unsound. As a consequence, we suggest analyzing intellectual humility semantically, using a psycholexical approach that focuses on both (...)
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  35. Should I Kill or Rather Not?Luis Moniz Pereira - 2019 - AI and Society 34 (4):939-943.
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  36. Simon Penny : Making Sense: Cognition, Computing, Art and Embodiment.Karamjit S. Gill - 2019 - AI and Society 34 (4):947-949.
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  37. Social Intelligence.Andreas Herzig, Emiliano Lorini & David Pearce - 2019 - AI and Society 34 (4):689-689.
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  38. Tony D. Sampson: The Assemblage Brain. Sense Making in Neuroculture.Tero Karppi - 2019 - AI and Society 34 (4):945-946.
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  39. Guest Preface: Streams of Consciousness: Cognition and Intelligent Devices.Nathaniel Tkacz - 2019 - AI and Society 34 (4):691-693.
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  40. Can a Machine Think ? Automation Beyond Simulation.M. Beatrice Fazi - 2019 - AI and Society 34 (4):813-824.
    This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...)
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  41. The Naturalness of Artificial Intelligence From the Evolutionary Perspective.Vladimír Havlík - 2019 - AI and Society 34 (4):889-898.
    Current discussions on artificial intelligence, in both the theoretical and practical realms, contain a fundamental lack of clarity regarding the nature of artificial intelligence, perhaps due to the fact that the distinction between natural and artificial appears, at first sight, both intuitive and evident. Is AI something unnatural, non-human and therefore dangerous to humanity, or is it only a continuation of man’s natural tendency towards creativity? It is not surprising that from the philosophical point of view, this distinction is the (...)
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  42. The Eyes of the State: How Central Banks Think.Nick Srnicek - 2019 - AI and Society 34 (4):847-856.
    In November 2015, after weeks of bitter political rhetoric and much discussion about the consequences of the proposed changes, the UK Tory government reversed their decision to eliminate tax credits for the poorest. While much was made of this reversal, particularly who deserved responsibility for the change, a more subtle politics was going on behind the scenes. The primary justification for the reversal, given by the Chancellor George Osborne, was that tax revenues in forthcoming years were now expected to be (...)
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  43. Transitions in Human–Computer Interaction: From Data Embodiment to Experience Capitalism.Tony D. Sampson - 2019 - AI and Society 34 (4):835-845.
    This article develops a critical theory of human–computer interaction intended to test some of the assumptions and omissions made in the field as it transitions from a cognitive theoretical frame to a phenomenological understanding of user experience described by Harrison et al. as a third research paradigm and similarly Bødker :24–31; Bødker, Interactions 22):24–31, 2015) as third-wave HCI. Although this particular focus on experience has provided some novel avenues of academic enquiry, this article draws attention to a distinct bridge between (...)
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  44. On Social Machines for Algorithmic Regulation.Nello Cristianini & Teresa Scantamburlo - forthcoming - AI and Society:1-18.
    Autonomous mechanisms have been proposed to regulate certain aspects of society and are already being used to regulate business organisations. We take seriously recent proposals for algorithmic regulation of society, and we identify the existing technologies that can be used to implement them, most of them originally introduced in business contexts. We build on the notion of ‘social machine’ and we connect it to various ongoing trends and ideas, including crowdsourced task-work, social compiler, mechanism design, reputation management systems, and social (...)
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  45. Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human–Machine Collaboration.Ron Eglash, Lionel Robert, Audrey Bennett, Kwame Porter Robinson, Michael Lachney & William Babbitt - forthcoming - AI and Society:1-15.
    Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche (...)
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  46. A Possibility of Inappropriate Use of Gender Studies in Human-Robot Interaction.Tatsuya Nomura - forthcoming - AI and Society:1-4.
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  47. The New AI Spring: A Deflationary View.Jocelyn Maclure - forthcoming - AI and Society:1-4.
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  48. Interperforming in AI: Question of ‘Natural’ in Machine Learning and Recurrent Neural Networks.Tolga Yalur - forthcoming - AI and Society:1-9.
    This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It shows the limits on the architecture of these network systems due to the misemployment of ‘natural’ performance, and it offers ‘context’ as a variable from a performative approach, instead of a constant. The article begins with a brief review of machine learning-based natural language processing systems and continues with a concentration on the relevant (...)
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  49. Indowordnet’s Help in Indian Language Machine Translation.S. Sreelekha & Pushpak Bhattacharyya - forthcoming - AI and Society:1-10.
    Languages with insufficient digitally available resources, such as, Indian–Indian and English–Indian language Machine Translation system developments, faces the difficulty to translate various lexical phenomena. In this paper, we present our work on a comparative study of 440 phrase-based statistical trained models for 110 language pairs across 11 Indian languages. We have developed 110 baseline statistical machine translation systems. Then, we have augmented the training corpus with Indowordnet synset word entries of lexical database and further trained 110 models on top of (...)
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  50. Anthropomorphizing AlphaGo: A Content Analysis of the Framing of Google DeepMind’s AlphaGo in the Chinese and American Press.Nathaniel Ming Curran, Jingyi Sun & Joo-Wha Hong - forthcoming - AI and Society.
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