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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. S. M. Ali & R. M. Zimmer (1994). Discourse on Artificiality: A Unifying Framework For the Artificial Sciences. Idealistic Studies 24 (3):201-226.
    This paper presents a unifying framework for the study of artificial life, intelIigence and reality. By providing this framework we can give a clear and concise introduction to the fundamental arguments of all three artificial sciences and facilitate the translation of arguments from any one domain to the other two. The framework is based on a variant of functionalism that does not exclude the role of the observer.
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  2. John Anderson (1930). Cattell Group Intelligence Tests. [REVIEW] Australasian Journal of Philosophy 8:235.
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  3. John E. Anderson (1920). Intelligence Tests of Yale Freshmen. Journal of Philosophy, Psychology and Scientific Methods 17 (17):469-469.
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  4. Fiorella Battaglia (2016). Computers for Everyone. Metascience 25 (2):279-280.
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  5. J. L. Bell (1995). Review of B. Rotman, Ad Infinitum - The Ghost In Turing's Machine: Taking God Out of Mathematics and Putting the Body Back In: An Essay in Corporeal Semiotics. [REVIEW] Philosophia Mathematica 3 (2):218-221.
  6. F. M. Berger & Harry P. Kroitor (1967). Computers and Medical Discoveries. Perspectives in Biology and Medicine 11 (1):63-70.
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  7. W. E. Black (1928). Intelligence Tests of Blind Subjects with the Modified Bridges Point Scale. Australasian Journal of Psychology and Philosophy 6 (1):64-66.
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  8. E. K. Blum (1965). Enumeration of Recursive Sets By Turing Machine. Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 11 (3):197-201.
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  9. Jay David Bolter (1987). Machines and Intelligence: A Critique of Arguments Against the Possibility of Artificial IntelligenceStuart Goldkind. Isis 78 (4):597-597.
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  10. Nick Bostrom, When Machines Outsmart Humans.
    Artificial intelligence is a possibility that should not be ignored in any serious thinking about the future, and it raises many profound issues for ethics and public policy that philosophers ought to start thinking about. This article outlines the case for thinking that human-level machine intelligence might well appear within the next half century. It then explains four immediate consequences of such a development, and argues that machine intelligence would have a revolutionary impact on a wide range of the social, (...)
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  11. C. C. Brigham (1930). Intelligence Tests of Immigrant Groups. Psychological Review 37 (2):158-165.
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  12. Vern L. Bullough, Serge Lusignan & Thomas H. Ohlgren (1974). Report: Computers and the Medievalist. Speculum 49 (2):392-402.
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  13. Ian E. Bush (1979). Trouble with Medical Computers. Perspectives in Biology and Medicine 22 (4):600-620.
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  14. Juremir Machado da Silva (2009). Lula, la Machine À Communiquer. Hermes 53:193.
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  15. Farzad Didehvar & Mohammad Saleh Zareepour, Epistemological Observations About Mind-Machine Equivalence.
    One of the highly contraversial discussions in philosophy of mind is equivalence of human being mind and machines. Here we show that no one could prove that, in certain he is a machine.
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  16. Vincent J. Digricoli (1986). Mind and Computer. Thought: A Journal of Philosophy 61 (4):442-451.
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  17. Bj Dotzler (1989). Kant and Turing-on the Archaeology of Thought on the Machine. Philosophisches Jahrbuch 96 (1):115-131.
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  18. Fred Dretske (1998). Minds, Machines, and Money: What Really Explains Behavior. In J. A. M. Bransen & S. E. Cuypers (eds.), Human Action, Deliberation and Causation. Dordrecht: Kluwer Academic Publishers. pp. 157--173.
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  19. Hubert L. Dreyfus (2016). 20. What Computers Can’T Do: A Critique of Artificial Reason. In Bernard Williams (ed.), Essays and Reviews: 1959-2002. Princeton University Press. pp. 90-100.
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  20. K. Dunlap & A. Snyder (1920). Practice Effects in Intelligence Tests. Journal of Experimental Psychology 3 (5):396.
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  21. A. R. E. (1966). Computers and the Human Mind: An Introduction to Artificial Intelligence. [REVIEW] Review of Metaphysics 20 (1):150-150.
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  22. Suitbert Ertel (2005). Are ESP Test Results Stochastic Artifacts? Brugger & Taylor's Claims Under Scrutiny. Journal of Consciousness Studies 12 (3):61-80.
    Peter Brugger & Kirsten Taylor regard positive extrasensory perception test results as methodical artifacts. In their view, sequences of guessing, e.g. of symbol cards, being non-random, overlap with finite sequences of non-random targets, and surpluses of hits from chance are deemed to be due to correlated non- randomness. The present author's ESP test data obtained from his 'ball drawing test ' applied with N = 231 psychology majors were used for testing five hypotheses derived from B&T's claims. B&T would expect (...)
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  23. Daniel Estrada, Rethinking Machines: Artificial Intelligence Beyond the Philosophy of Mind.
    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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  24. Thomas Eudaly, Response [to Whitmer's "Intentionality, Artificial Intelligence and the Causal Powers of the Brain"].
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  25. Luis Farinas del Cerro, Andreas Herzig & Jerome Mengin (eds.) (2012). Logics in Artificial Intelligence. Springer.
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  26. Solomon Feferman (2009). Gödel, Nagel, Minds, and Machines. Journal of Philosophy 106 (4):201-219.
    Ernest Nagel Lecture, Columbia University, Sept. 27, 2007.
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  27. Henry H. Ferguson (1937). Incentives and an Intelligence Tests. Australasian Journal of Philosophy 15 (1):39 – 53.
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  28. Henry H. Ferguson (1937). Incentives and an Intelligence Tests. Australasian Journal of Psychology and Philosophy 15 (1):39-53.
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  29. Peter A. Flach (1991). The Dialectics of Artificial Intelligence. In P. A. Flach (ed.), Future Directions in Artificial Intelligence. New York: Elsevier Science.
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  30. Bent Flyvbjerg (1992). Dreyfus & Dreyfus: Opretholdelse Af Ikke-Rationaliserede Praksisser. Philosophia 21 (1-2).
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  31. Thomas George Foran (1927). Intelligence Tests. Thought: A Journal of Philosophy 2 (2):277-298.
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  32. Meyer Fortes & H. A. Reyburn (1932). Perceptual Tests of “General Intelligence” for Inter-Racial Use. Transactions of the Royal Society of South Africa 20 (3):281-299.
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  33. F. S. Freeman (1931). The Factors of Speed and Power in Tests of Intelligence. Journal of Experimental Psychology 14 (1):83.
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  34. Iván Futó & T. Gergely (1990). Artificial Intelligence in Simulation.
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  35. Robert George Gell, Machines Cannot Think.
    This paper is a critical essay on the question "Can machines think?", with particular attention paid to the articles appearing in an anthology Minds and Machines, A. R. Anderson editor. The general conclusion of this paper is that those arguments which have been advanced to show that machines can think are inconclusive. I begin by examining rather closely a paper by Hilary Putnam called "Minds and Machines" in which he argues that the traditional mind-body problem can arise with a complex (...)
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  36. F. H. George (1957). Thinking and Machines. Philosophy 32 (121):168 - 169.
    Professor A. D. Ritchie's remarks cannot go without some reply, since otherwise they would only have the effect of increasing the already considerable confusion on the subject of Cybernetics.
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  37. Clark Glymour (1992). Android Epistemology: Computation, Artificial Intelligence. In Merrilee H. Salmon (ed.), Introduction to the Philosophy of Science. Hackett. pp. 364.
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  38. Antoni Gomila & Vincent C. Müller (2012). Challenges for Artificial Cognitive Systems. Journal of Cognitive Science 13 (4):452-469.
    The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out our neck (...)
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  39. Ae Goss (1986). Mind and Behavior in Diagram-Intelligence. Bulletin of the Psychonomic Society 24 (5):339-339.
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  40. John Gray (1924). God-Made and Machine-Made. New Blackfriars 5 (56):451-457.
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  41. Stefan Gruner (2008). Comments on 'How Would You Know If You Synthesized a Thinking Thing'. Minds and Machines 18 (1):107-120.
    In their Minds and Machines essay How would you know if you synthesized a Thinking Thing? (Kary & Mahner, Minds and Machines, 12(1), 61–86, 2002), Kary and Mahner have chosen to occupy a high ground of materialism and empiricism from which to attack the philosophical and methodological positions of believers in artificial intelligence (AI) and artificial life (AL). In this review I discuss some of their main arguments as well as their philosophical foundations. Their central argument: ‘AI is Platonism’, which (...)
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  42. J. Victor Haberman (1916). The Intelligence Examination and Evaluation: A Study of the Child's Mind :Part I. Psychological Review 23 (5):352-379.
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  43. J. Victor Haberman (1916). The Intelligence Examination and Evaluation: A Study of the Child's Mind Part II. Psychological Review 23 (6):484-500.
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  44. P. Hajek (1986). Ethical Problems of Artificial-Intelligence. Filosoficky Casopis 34 (3):467-471.
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  45. John W. Hamblen (1981). Computer Literacy and Societal Impact of Computers. Acm Sigcas Computers and Society 11 (3):19.
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  46. Robin Hanson, Has Penrose Disproved A.I.?
    Being read is not the same as being believed. Most reviewers have praised the book as original, well-written, thought-provoking, etc., and then gone on to take issue with one or more of Penrose's main theses. Penrose seems unfamiliar with the existing literature in cognitive science, philosophy of mind, and AI. The handful of reviewers who agree with Penrose don't seem to have paid much attention to his specific arguments - they always thought AI was bogus. See, for example, the 37 (...)
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  47. S. Harnad (2000). Minds, Machines and Turing. Journal of Logic, Language and Information 9 (4):425-445.
    Turing's celebrated 1950 paper proposes a very generalmethodological criterion for modelling mental function: total functionalequivalence and indistinguishability. His criterion gives rise to ahierarchy of Turing Tests, from subtotal (toy) fragments of ourfunctions (t1), to total symbolic (pen-pal) function (T2 – the standardTuring Test), to total external sensorimotor (robotic) function (T3), tototal internal microfunction (T4), to total indistinguishability inevery empirically discernible respect (T5). This is areverse-engineering hierarchy of (decreasing) empiricalunderdetermination of the theory by the data. Level t1 is clearly toounderdetermined, T2 (...)
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  48. Stevan Harnad (2002). Darwin, Skinner, Turing and the Mind. Magyar Pszichologiai Szemle 57 (4):521-528.
    Darwin differs from Newton and Einstein in that his ideas do not require a complicated or deep mind to understand them, and perhaps did not even require such a mind in order to generate them in the first place. It can be explained to any school-child (as Newtonian mechanics and Einsteinian relativity cannot) that living creatures are just Darwinian survival/reproduction machines. They have whatever structure they have through a combination of chance and its consequences: Chance causes changes in the genetic (...)
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  49. Stevan Harnad (2002). Turing Indistinguishability and the Blind Watchmaker. In James H. Fetzer (ed.), Consciousness Evolving. John Benjamins. pp. 3-18.
    Many special problems crop up when evolutionary theory turns, quite naturally, to the question of the adaptive value and causal role of consciousness in human and nonhuman organisms. One problem is that -- unless we are to be dualists, treating it as an independent nonphysical force -- consciousness could not have had an independent adaptive function of its own, over and above whatever behavioral and physiological functions it "supervenes" on, because evolution is completely blind to the difference between a conscious (...)
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  50. Juris Hartmanis (2012). Turing Machine-Inspired Computer Science Results. In S. Barry Cooper (ed.), How the World Computes. pp. 276--282.
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