About this topic
Summary This category is about whether or not computers, robots, and software agents can literally be said to think.  Humans think, chimps think, dogs think, cats and birds think. But do computers?  Is your computer thinking now?  Perhaps only specially programmed computers think?  Or perhaps only computers with special hardware can think -- hardware that resembles the neurons of the brain, for example. If computers can be made to think, then does that mean that humans are a kind of robot and their brains a kind of computer -- a neurocomputer, say?  One of the deeper issues here is that the term "thinking" is ambiguous in at least two ways: It can include being conscious of one's environment (surroundings), one's personal feelings and thoughts, etc., or it can mean cogitate, learn, plan, and solve problems, where these latter terms pick out mental events that may or may not be conscious.   
Key works The idea that machines could think occurred to the very first computer builders and programmers.  See, e.g., Alan Turing's great paper Turing 1950.  The term "artificial intelligence" (AI) goes back to a summer conference in held 1956 at Dartmouth College in New Hampshire.  Many AI pioneers took it for granted that within a decade or two computers would be as intelligent as humans.  A central paper from this time is McCarthy & Hayes 1969.  Another crucial paper is Putnam's Putnam 1960.  But the optimism proved to be unjustified.  The decades came and went without machines achieving human-level intelligence.  Soon several philosophers and other researchers argued that computers would never think and that human brains and minds were completely different from computers.  The most important paper in this push-back was John Searle's famous paper: Searle 1980, where he argues that machines cannot think at all because they lack the proper semantical connection to the world.  Summaries and replies to Searle's paper accompany it in the same journal issue (Searle 1980).  Also, a summary of Searle's anti-AI argument and many replies to it can be found in Dietrich 1994.  Another form of attack on AI came from Lucas 1961, who argued that Godel's famous Incompleteness Theorems showed that machines could not think.  This theme was reinvigorated by Roger Penrose in his well-known book Penrose 1989.  Yet another form of attack on AI came from Fodor 1987.  All of these attacks on AI spawned a large literature trying to refute them, agreeing with them, or amending them. To this day, it is not known whether or not machines (computers) can think, nor if humans are machines.  Nevertheless, the effort to build intelligent machines continues, and this is probably the best way to answer the question.
Introductions See Searle 1980 and the associated replies for a good introduction to the issues surrounding Searle's attack on AI. For some good history of AI, which raises many important issues, see Pamela McCorduck's McCorduck 2004 and Daniel Crevier's AI: The Tumultuous Search for Artificial Intelligence (1993).  
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  1. The Shortcut - Why Intelligent Machines Do Not Think Like Us.Nello Cristianini - forthcoming - Boca Raton, Florida: CRC Press.
    Book. From the Publisher. An influential scientist in the field of artificial intelligence (AI) explains its fundamental concepts and how it is changing culture and society. -/- A particular form of AI is now embedded in our tech, our infrastructure, and our lives. How did it get there? Where and why should we be concerned? And what should we do now? The Shortcut: Why Intelligent Machines Do Not Think Like Us provides an accessible yet probing exposure of AI in its (...)
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  2. Machine Learning, Functions and Goals.Patrick Butlin - 2022 - Croatian Journal of Philosophy 22 (66):351-370.
    Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs (...)
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  3. John Haugeland, ed., Mind Design II: Philosophy, Psychology, and Artificial Intelligence[REVIEW]Varol Akman - 1998 - ACM SIGART Bulletin 9 (3-4):33-36.
    This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland and published by The MIT Press in 1997.
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  4. THE ROBOTS ARE COMING: What’s Happening in Philosophy (WHiP)-The Philosophers, August 2022.Jeff Hawley - unknown
    Should we fear a future in which the already tricky world of academic publishing is increasingly crowded out by super-intelligent artificial general intelligence (AGI) systems writing papers on phenomenology and ethics? What are the chances that AGI advances to a stage where a human philosophy instructor is similarly removed from the equation? If Jobst Landgrebe and Barry Smith are correct, we have nothing to fear.
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  5. What’s Happening in Philosophy (WHiP)-The Philosophers, July 2022.Jeff Hawley - unknown
    Originally published in PhilosophyNews, July 19, 2022. -/- This new series, What’s Happening in Philosophy (WHiP)-The Philosophers aims to provide a monthly snapshot of various trends and discussions happening across the discipline. -/- In this inaugural post, we begin with a harrowing tale from David Edmonds involving the murder of the German philosopher Moritz Schlick. Schlick was a Vienna Circle guiding spirit and logical positivist thinker. Next up is Steven Nadler’s take on several biographies of the ‘father of modern philosophy’ (...)
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  6. Embedding the assessment of emotion in the learning process with AI-driven technologies.Rossitza Kaltenborn - 2019 - In Petrov Vesselin & Katie Andersen (eds.), Traditional Learning Theories, Process Philosophy and AI. Brüssel, Belgien:
    This paper examines the possibility of an objective evaluation of emotions occurring within the learning process and methods for embedding such an evaluation in advanced learning systems. The main conceptual understandings of emotion in learning and teaching are systematized, with an emphasis on the process philosophy approach. Different models of emotion are considered and the possible generalization of Whitehead’s approach to the role of emotion in education is examined. Special attention is given to significant developments in artificial intelligence in identifying (...)
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  7. Portraits, Facial Perception, and Aspect-Seeing.Andreas Vrahimis - 2022 - British Journal of Aesthetics 62 (1):85–100.
    Is there a substantial difference between a portrait depicting the sitter’s face made by an artist and an image captured by a machine able to simulate the neuro-physiology of facial perception? Drawing on the later Wittgenstein, this paper answers this question by reference to the relation between seeing a visual pattern as (i) a series of shapes and colours, and (ii) a face with expressions. In the case of the artist, and not of the machine, the portrait’s creative process involves (...)
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  8. The kantian notion of freedom and autonomy of artificial agency.Manas Sahu - 2021 - Prometeica - Revista De Filosofía Y Ciencias 23:136-149.
    The objective of this paper is to provide critical analysis of the Kantian notion of freedom ; its significance in the contemporary debate on free-will and determinism, and the possibility of autonomy of artificial agency in the Kantian paradigm of autonomy. Kant's resolution of the third antinomy by positing the ground in the noumenal self resolves the problem of antinomies; however, it invites an explanatory gap between phenomenality and the noumenal self; even if he has successfully established the compatibility of (...)
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  9. From symbols to knowledge systems: A. Newell and H. A. Simon's contribution to symbolic AI.Luis M. Augusto - 2021 - Journal of Knowledge Structures and Systems 2 (1):29 - 62.
    A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...)
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  10. Saint Thomas d'Aquin contre les robots. Pistes pour une approche philosophique de l'Intelligence Artificielle.Matthieu Raffray - 2019 - Angelicum 4 (96):553-572.
    In light of the pervasive developments of new technologies, such as NBIC (Nanotechnology, biotechnology, information technology, and cognitive science), it is imperative to produce a coherent and deep reflexion on the human nature, on human intelligence and on the limit of both of them, in order to successfully respond to some technical argumentations that strive to depict humanity as a purely mechanical system. For this purpose, it is interesting to refer to the epistemology and metaphysics of Thomas Aquinas as a (...)
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  11. Can Artificial Intelligence Make Art?Elzė Sigutė Mikalonytė & Markus Kneer - 2022 - ACM Transactions on Human-Robot Interactions.
    In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...)
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  12. Playing the Blame Game with Robots.Markus Kneer & Michael T. Stuart - 2021 - In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI’21 Companion). New York, NY, USA:
    Recent research shows – somewhat astonishingly – that people are willing to ascribe moral blame to AI-driven systems when they cause harm [1]–[4]. In this paper, we explore the moral- psychological underpinnings of these findings. Our hypothesis was that the reason why people ascribe moral blame to AI systems is that they consider them capable of entertaining inculpating mental states (what is called mens rea in the law). To explore this hypothesis, we created a scenario in which an AI system (...)
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  13. Updating the Frame Problem for Artificial Intelligence Research.Lisa Miracchi - 2020 - Journal of Artificial Intelligence and Consciousness 7 (2):217-230.
    The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...)
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  14. Artificial Intelligence: Philosophical and Epistemological Perspectives.Pierre Livet & Franck Varenne - 2020 - In H. Prade, Papini O. & Marquis P. (eds.), A Guided Tour of Artificial Intelligence Research. pp. 437-455.
    Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...)
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  15. Blended Cognition.Jordi Vallverdú & Vincent C. Müller (eds.) - 2019 - Cham: Springer.
    The central concept of this edited volume is "blended cognition", the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding (...)
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  16. In 30 Schritten zum Mond? Zukünftiger Fortschritt in der KI.Vincent C. Müller - 2018 - Medienkorrespondenz 20 (05.10.2018):5-15.
    Die Entwicklungen in der Künstlichen Intelligenz (KI) sind spannend. Aber wohin geht die Reise? Ich stelle eine Analyse vor, der zufolge exponentielles Wachstum von Rechengeschwindigkeit und Daten die entscheidenden Faktoren im bisherigen Fortschritt waren. Im Folgenden erläutere ich, unter welchen Annahmen dieses Wachstum auch weiterhin Fortschritt ermöglichen wird: 1) Intelligenz ist eindimensional und messbar, 2) Kognitionswissenschaft wird für KI nicht benötigt, 3) Berechnung (computation) ist hinreichend für Kognition, 4) Gegenwärtige Techniken und Architektur sind ausreichend skalierbar, 5) Technological Readiness Levels (TRL) (...)
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  17. Sociality and Normativity for Robots. Studies in the Philosophy of Sociality.Raul Hakli & Johanna Seibt (eds.) - 2017 - Cham: Springer.
    This volume offers eleven philosophical investigations into our future relations with social robots--robots that are specially designed to engage and connect with human beings. The contributors present cutting edge research that examines whether, and on which terms, robots can become members of human societies. Can our relations to robots be said to be "social"? Can robots enter into normative relationships with human beings? How will human social relations change when we interact with robots at work and at home? The authors (...)
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  18. Philosophy and theory of artificial intelligence 2017.Vincent C. Müller (ed.) - 2017 - Berlin: Springer.
    This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...)
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  19. Artificiell Intelligens: Tankar utan innehåll?Sten Lindström & Ingar Brinck - 1993 - In Åke E. Andersson & Nils-Eric Sahlin (eds.), Huvudinnehåll: Tolv Filosofiska Uppsatser. 641 91 Nora, Sverige: pp. 121-146.
    Artificiell intelligens (AI) är ett ungt forskningsområde där många av de grundläggande problemen förefaller att vara av filosofisk art.1 Ämnet har sina filosofiska rötter dels i traditionen från Leibniz, Frege, Russell och Hilbert, som strävar efter att formalisera principerna för exakt tänkande, dels i den klassiska mekanismen: idén att människan är en maskin och att det mänskliga tänkandet är en mekanisk process. Som en första approximation kan vi säga att AI är det vetenskapliga studiet av hur man konstruerar och bygger (...)
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  20. Should machines be tools or tool-users? Clarifying motivations and assumptions in the quest for superintelligence.Dan J. Bruiger - manuscript
    Much of the basic non-technical vocabulary of artificial intelligence is surprisingly ambiguous. Some key terms with unclear meanings include intelligence, embodiment, simulation, mind, consciousness, perception, value, goal, agent, knowledge, belief, optimality, friendliness, containment, machine and thinking. Much of this vocabulary is naively borrowed from the realm of conscious human experience to apply to a theoretical notion of “mind-in-general” based on computation. However, if there is indeed a threshold between mechanical tool and autonomous agent (and a tipping point for singularity), projecting (...)
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  21. Oxford Handbook of the Philosophy of Artificial Intelligence (forthcoming).Vincent C. Müller - manuscript
    Handbook in the making. Expected completion for 2021.
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  22. Animals, Humans, Machines and Thinking Matter, 1690-1707.Ann Thomson - 2010 - Early Science and Medicine 15 (1-2):3-37.
    This article looks at the debate on the soul in England at the turn of the eighteenth century and at the role played within it by the question of animal soul, which had both theological and scientific ramifications. It discusses the difficulty of accounting for animal behaviour without either adopting the animal-machine hypothesis or according animals an immaterial and hence immortal soul. While those who denied the existence of an immaterial human soul and refused any fundamental distinction between humans and (...)
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  23. Computability and human symbolic output.Jason Megill & Tim Melvin - 2014 - Logic and Logical Philosophy 23 (4):391-401.
    This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. (...)
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  24. HOOKWAY, CHRISTOPHER (ed.) [1984]: Minds, Machines and Evolution. Cambridge University Press. Pp. xi+ 177. [REVIEW]Peter Gibbins - 1986 - British Journal for the Philosophy of Science 37 (3):369-371.
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  25. What are computers (if they're not thinking things)?John Preston - unknown
  26. Mind, Man, and Machine: A Dialogue.Paul T. Sagal - 1994 - Hackett Publishing Company.
    Explores the ideas of Turing, Lucas, Scriven, Putnam, and Searle, and renders the Gödel-Church-Lucas argument in terms intelligible to beginning students. Updated and expanded to take into account important arguments and developments in the ten years since its original publication, this provocative dialogue explores the ideas of Turing, Lucas, Scriven, Putnam, and Searle, and renders the complex Gödel-Church-Lucas argument in transparent terms. It includes a new argument, based loosely on Tarski's work on truth and the liar paradox, and a new (...)
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  27. Review article: “Computers and Cognition: Why Minds are not Machines” by James H. Fetzer.Valdemar W. Setzer - 2001 - Pragmatics and Cognition 9 (2):293-312.
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  28. Collecting pebbles: An insight into machine aesthetics: Mealing Collecting pebbles.Stuart Mealing - 2007 - Think 5 (14):73-78.
    Could a machine possess an aesthetic sense? Could it appreciate the beauty of a pebble?
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  29. Thorndike's Animal Intelligence: Experimental Studies.Margaret Floy Washburn - 1912 - Journal of Philosophy, Psychology and Scientific Methods 9 (7):193.
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  30. Man Machine and Other Writings. [REVIEW]Patricia Ann Easton - 1999 - Dialogue 38 (3):627-629.
    There is a great deal in Man Machine and Other Writings that will delight the reader. Thomson has managed to capture much of La Mettrie’s wit and poetic use of language, which is no easy task; as La Mettrie himself comments on his “figurative style,” it “is often necessary in order to express better what is felt and to add grace to truth itself”. The central thesis of Man Machine needs little introduction. Inspired by the suggestion in Part 5 of (...)
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  31. Super-intelligence and (super-)consciousness.Steve Torrance - 2012 - International Journal of Machine Consciousness 4 (2):483-501.
  32. Consciousness: A Philosophic Study of Minds and Machines.J. R. Lucas & Kenneth M. Sayre - 1972 - Philosophical Review 81 (2):241.
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  33. Computers and Classroom Culture.Janet Ward Schofield - 1995 - Cambridge University Press.
    As important as it is to realize the potential of computer technology to improve education, it is just as important to understand how the social organization of schools and classrooms influences the use of computers, and in turn is effected by that technology in unanticipated ways. In Computers and Classroom Culture, first published in 1996, Janet Schofield observes the fascinating dynamics of the computer-age classroom. Among her many discoveries, Schofield describes how the use of an artificially-intelligent tutor in a geometry (...)
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  34. Computers for everyone: Elizabeth R. Petrick: Making computers accessible: disability rights and digital technologies. Baltimore: Johns Hopkins University Press, 2015, 208pp, $49.95 HB. [REVIEW]Fiorella Battaglia - 2016 - Metascience 25 (2):279-280.
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  35. Computational Reflection, Machines and Minds.Gaetano Aurelio Lanzarone - 2009 - Dialogue and Universalism 19 (1-2):9-30.
    The purpose of this paper is to argue that, in order for the debate in Computing and philosophy to move forward with respect to its current state, the advances of Computer Science and Artificial Intelligence of the last decades must be taken into account. Computational reflection, one of these advances, is presented in detail and its philosophical implications are discussed, in contrast with old-fashioned views of computational systems such as those presented by Lucas’ papers on Minds and Machines.
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  36. Can a Turing Machine Know That the Gödel Sentence is True?Storrs McCall - 1999 - Journal of Philosophy 96 (10):525-532.
  37. Harry M. Collins. Artificial Experts: Social Knowledge and Intelligent Machines. Cambridge, Mass, and London: MIT Press, 1990. Pp. xiii + 266. ISN 0-262-13168-X. £17.95. [REVIEW]Geoffrey Tweedale - 1991 - British Journal for the History of Science 24 (4):481-482.
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  38. Alex Roland with Philip Shiman, strategic computing: Darpa and the Quest for machine intelligence, 1983–1993. History of computing. Cambridge, ma and London: Mit press, 2002. Pp. XXVI+427. Isbn 0-262-18226-2. £33.50. [REVIEW]James Sumner - 2006 - British Journal for the History of Science 39 (4):622-624.
  39. Machines and Intelligence: A Critique of Arguments against the Possibility of Artificial Intelligence. Stuart Goldkind.Jay David Bolter - 1987 - Isis 78 (4):597-597.
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  40. Intelligence tests of immigrant groups.C. C. Brigham - 1930 - Psychological Review 37 (2):158-165.
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  41. The intelligence examination and evaluation: A study of the child's mind (second report) Part II.J. Victor Haberman - 1916 - Psychological Review 23 (6):484-500.
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  42. The intelligence examination and evaluation: A study of the child's mind (second report):Part I.J. Victor Haberman - 1916 - Psychological Review 23 (5):352-379.
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  43. Intelligence and Behavior.A. A. Roback - 1922 - Psychological Review 29 (1):54-62.
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  44. Is lack of intelligence the chief cause of delinquency?Curt Rosenow - 1920 - Psychological Review 27 (2):147-157.
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  45. A reply to " The nature of animal intelligence and the methods of investigating it".Edward Thorndike - 1899 - Psychological Review 6 (4):412-420.
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  46. The nature of animal intelligence and the methods of investigating it.Wesley Mills - 1899 - Psychological Review 6 (3):262-274.
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  47. Machines and Thought: The Legacy of Alan Turing, Volume 1.Peter J. R. Millican & Andy Clark (eds.) - 1996 - Oxford, England: Oxford University Press UK.
    This is the first of two volumes of essays in commemoration of Alan Turing, whose pioneering work in the theory of artificial intelligence and computer science continues to be widely discussed today. A group of prominent academics from a wide range of disciplines focus on three questions famously raised by Turing: What, if any, are the limits on machine `thinking'? Could a machine be genuinely intelligent? Might we ourselves be biological machines, whose thought consists essentially in nothing more than the (...)
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  48. Genes, Affect, and Reason: Why Autonomous Robot Intelligence Will Be Nothing Like Human Intelligence.Henry Moss - 2016 - Techné: Research in Philosophy and Technology 20 (1):1-15.
    Abstract: Many believe that, in addition to cognitive capacities, autonomous robots need something similar to affect. As in humans, affect, including specific emotions, would filter robot experience based on a set of goals, values, and interests. This narrows behavioral options and avoids combinatorial explosion or regress problems that challenge purely cognitive assessments in a continuously changing experiential field. Adding human-like affect to robots is not straightforward, however. Affect in organisms is an aspect of evolved biological systems, from the taxes of (...)
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  49. Rethinking machines: artificial intelligence beyond the philosophy of mind.Daniel Estrada - unknown
    Recent philosophy of mind has increasingly focused on the role of technology in shaping, influencing, and extending our mental faculties. Technology extends the mind in two basic ways: through the creative design of artifacts and the purposive use of instruments. If the meaningful activity of technological artifacts were exhaustively described in these mind-dependent terms, then a philosophy of technology would depend entirely on our theory of mind. In this dissertation, I argue that a mind-dependent approach to technology is mistaken. Instead, (...)
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  50. Enumeration of Recursive Sets By Turing Machine.E. K. Blum - 1965 - Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 11 (3):197-201.
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