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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. 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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  5. Juremir Machado da Silva (2009). Lula, la Machine À Communiquer. Hermes 53:193.
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  6. 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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  7. Vincent J. Digricoli (1986). Mind and Computer. Thought: A Journal of Philosophy 61 (4):442-451.
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  8. Bj Dotzler (1989). Kant and Turing-on the Archaeology of Thought on the Machine. Philosophisches Jahrbuch 96 (1):115-131.
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  9. 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 157--173.
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  10. K. Dunlap & A. Snyder (1920). Practice Effects in Intelligence Tests. Journal of Experimental Psychology 3 (5):396.
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  11. 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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  12. 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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  13. Thomas Eudaly, Response [to Whitmer's "Intentionality, Artificial Intelligence and the Causal Powers of the Brain"].
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  14. Luis Farinas del Cerro, Andreas Herzig & Jerome Mengin (eds.) (2012). Logics in Artificial Intelligence. Springer.
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  15. 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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  16. 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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  17. Bent Flyvbjerg (1992). Dreyfus & Dreyfus: Opretholdelse Af Ikke-Rationaliserede Praksisser. Philosophia 21 (1-2).
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  18. Thomas George Foran (1927). Intelligence Tests. Thought: A Journal of Philosophy 2 (2):277-298.
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  19. 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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  20. Iván Futó & T. Gergely (1990). Artificial Intelligence in Simulation.
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  21. 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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  22. Clark Glymour (1992). Android Epistemology: Computation, Artificial Intelligence. In Merrilee H. Salmon (ed.), Introduction to the Philosophy of Science. Hackett Pub. 364.
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  23. 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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  24. Ae Goss (1986). Mind and Behavior in Diagram-Intelligence. Bulletin of the Psychonomic Society 24 (5):339-339.
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  25. 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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  26. S. O. H. (1968). Understanding Computers. Review of Metaphysics 22 (1):142-142.
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  27. P. Hajek (1986). Ethical Problems of Artificial-Intelligence. Filosoficky Casopis 34 (3):467-471.
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  28. John W. Hamblen (1981). Computer Literacy and Societal Impact of Computers. Acm Sigcas Computers and Society 11 (3):19.
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  29. 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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  30. 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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  31. 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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  32. Stevan Harnad (2002). Turing Indistinguishability and the Blind Watchmaker. In James H. Fetzer (ed.), Consciousness Evolving. John Benjamins 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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  33. Juris Hartmanis (2012). Turing Machine-Inspired Computer Science Results. In S. Barry Cooper (ed.), How the World Computes. 276--282.
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  34. C. Judson Herrick (1931). The Thinking Machine. Journal of Philosophy 28 (18):493-497.
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  35. Andrew Hodges, Alan Turing in the Stanford Encyclopedia of Philosophy.
    The origin of my article lies in the appearance of Copeland and Proudfoot's feature article in Scientific American, April 1999. This preposterous paper, as described on another page, suggested that Turing was the prophet of 'hypercomputation'. In their references, the authors listed Copeland's entry on 'The Church-Turing thesis' in the Stanford Encyclopedia. In the summer of 1999, I circulated an open letter criticising the Scientific American article. I included criticism of this Encyclopedia entry. This was forwarded to Prof. Ed Zalta, (...)
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  36. Andrew Hodges, The Alan Turing Bibliography.
    Almost everything Turing wrote is now accessible on-line in some form, much of it in the Turing Digital Archive, which makes available scanned versions of the physical papers held in the archive at King's College, Cambridge University. See..
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  37. Andrew Hodges, Unveiling the Official Blue Plaque on Alan Turing's Birthplace.
    The day was particularly appropriate. There was a great deal of publicity for the 50th anniversary of the world's first working modern computer, which ran at Manchester on 21 June 1948. And at 10.30pm the night before, 22 June 1998, the House of Commons had voted by a large majority to change the law so that homosexual and heterosexual acts would alike be governed by an 'age of consent' of 16. It was recognised by all sides that the issue at (...)
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  38. James P. Hogan (1998). Mind Matters Exloring the World of Artificial Intelligence. Monograph Collection (Matt - Pseudo).
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  39. Ted Honderich (2006). Screen Test. The Philosophers' Magazine 36 (36):80-81.
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  40. Ted Honderich (2006). Screen Test. The Philosophers' Magazine 36 (36):80-81.
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  41. Philip K. Hooper (1966). The Undecidability of the Turing Machine Immortality Problem. Journal of Symbolic Logic 31 (2):219-234.
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  42. Ben Hyink (2004). Mann Versus Machine. Free Inquiry 24.
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  43. Jordi Vallverdú I. Segura (2011). The Eastern Construction of the Artificial Mind. Enrahonar: Quaderns de Filosofía 47:171-185.
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  44. Satoshi Iriyama & Masanori Ohya (2009). On Generalized Quantum Turing Machine and Its Applications. In Institute of Physics Krzysztof Stefanski (ed.), Open Systems and Information Dynamics. World Scientific Publishing Company 16--02.
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  45. Stanley L. Jaki (1969). Brain, Mind And Computers. Herder & Herder.
  46. P. Johannesma (1986). Eduardo Caianiello: Thought Processes and Thinking Machines. In G. Palm & A. Aertsen (eds.), Brain Theory. Springer 241--244.
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  47. Deborah Johnson (1994). Who Should Teach Computer Ethics and Computers & Society? Acm Sigcas Computers and Society 24 (2):6-13.
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  48. Bill Jones (1969). A Note on Alan GAULD's 'Could a Machine Perceive?'. British Journal for the Philosophy of Science 20 (3):261-262.
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  49. Howard F. Kamler (1982). Could Persons Be Nonconscious Like Machines? Nature and System 4 (September):143-150.
  50. J. R. Kantor (1935). Man and Machine in Science. Journal of Philosophy 32 (25):673-684.
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