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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. John Anderson (1930). Cattell Group Intelligence Tests. [REVIEW] Australasian Journal of Philosophy 8:235.
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  2. John E. Anderson (1920). Intelligence Tests of Yale Freshmen. Journal of Philosophy, Psychology and Scientific Methods 17 (17):469-469.
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  3. Namit Arora (2011). The Minds of Machines. Philosophy Now 87:14-16.
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  4. Martin Atkinson & Margaret A. Boden (1979). Artificial Intelligence and Natural Man. Philosophical Quarterly 29 (116):278.
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  5. Bernard J. Baars, Uma Ramamurthy & Stan Franklin (2007). How Deliberate, Spontaneous, and Unwanted Memories Emerge in a Computational Model of Consciousness. In John H. Mace (ed.), Involuntary Memory. New Perspectives in Cognitive Psychology. Blackwell Publishing. 177-207.
  6. J. Barrett (1997). Individualism and the Cross-Contexts Test. Pacific Philosophical Quarterly 78 (3):242-60.
    Jerry Fodor has defended the claim that psychological theories should appeal to narrow rather than wide intentional properties. One of his arguments relies upon the cross contexts test, a test that purports to determine whether two events have the same causally relevant properties. Critics have charged that this test is too weak, since it counts certain genuinely explanatory relational properties in science as being causally irrelevant. Further, it has been claimed, the test is insensitive to the (...)
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  7. Jean-Roch Beausoleil (1989). The Metamathematics-Popperian Epistemology Connection and its Relation to the Logic of Turing's Programme. British Journal for the Philosophy of Science 40 (3):307-322.
    Turing's programme, the idea that intelligence can be modelled computationally, is set in the context of a parallel between certain elements from metamathematics and Popper's schema for the evolution of knowledge. The parallel is developed at both the formal level, where it hinges on the recursive structuring of Popper's schema, and at the contentual level, where a few key issues common to both epistemology and metamathematics are briefly discussed. In light of this connection Popper's principle of transference, akin to Turing's (...)
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  8. Anthony F. Beavers, Alan Turing: Mathematical Mechanist.
    I live just off of Bell Road outside of Newburgh, Indiana, a small town of 3,000 people. A mile down the street Bell Road intersects with Telephone Road not as a modern reminder of a technology belonging to bygone days, but as testimony that this technology, now more than a century and a quarter old, is still with us. In an age that prides itself on its digital devices and in which the computer now equals the telephone as a medium (...)
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  9. F. C. Becker (1913). E La Mettrie's Man a Machine. [REVIEW] Journal of Philosophy 10 (21):582.
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  10. John Beloff (1994). Minds and Machines: A Radical Dualist Perspective. Journal of Consciousness Studies 1 (1):32-37.
    The article begins with a discussion about what might constitute consciousness in entities other than oneself and the implications of the mind-brain debate for the possibility of a conscious machine. While referring to several other facets of the philosophy of mind, the author focuses on epiphenomenalism and interactionism and presents a critique of the former in terms of biological evolution. The interactionist argument supports the relevance of parapsychology to the problem of consciousness and the statistical technique of meta-analysis is cited (...)
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  11. Benjamin Bennett (2011). The Thinking Machine. Revue Internationale de Philosophie 1:7-26.
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  12. Dong Biao (2008). The Yan'an New Philosophy Association: An Ambitious Intellectual Machine of the CCP [J]. Modern Philosophy 3:011.
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  13. John Biro (2007). Intelligence, behavior and internal processing/Inteligência, comportamento e processamento interno. Manuscrito 30 (2):291-315.
    Ned Block has recently adduced some new arguments to show that “psychologism is true and thus a natural behaviorist analysis of intelligence that is incompatible with psychologism is false”. He introduces a thought experiment in which a machine is programmed to exhibit intelligent-seeming behavior and appeals to our intuition that such a machine is nevertheless not really inteligent; he traces that intuition to the fact that the machine is being thought of as operating with internal processes that, first, lack a (...)
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  14. Russell Blackford (2011). Editorial: Of Minds and Machines. Journal of Evolution and Technology 22 (1):i-ii.
    This special issue of JET deals with questions relating to our radically enhanced future selves or our possible “mind children” – conscious beings that we might bring about through the development of advanced computers and robots. Our mind children might exceed human levels of cognition, and avoid many human limitations and vulnerabilities. In a call for papers earlier this year, the editors asked how far we ought to go with processes that might ultimately convert humans to some sort of post-biological (...)
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  15. K. H. Bläsius & Hans-jürgen Bürckert (1989). Deduction Systems in Artificial Intelligence.
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  16. E. K. Blum (1965). Enumeration of Recursive Sets By Turing Machine. Mathematical Logic Quarterly 11 (3):197-201.
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  17. Margaret A. Boden (1982). Artificial Intelligence and Biological Reductionism. University of Sussex.
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  18. David L. Boyer (1983). J. R. Lucas, Kurt Godel, and Fred Astaire. Philosophical Quarterly 33 (131):147-159.
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  19. Alan Bundy (1981). Mental Machines Rule Ok. Dept. Of Artificial Intelligence, University of Edinburgh.
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  20. N. F. Bunnin (1980). Artificial Intelligence and Natural Man. Philosophical Books 21 (1):46-48.
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  21. Ludovic Burel-Regular (2002). A Girl And A Machine. Multitudes 8.
    Long restricted to ’reproductive’ activity- and thus considered economically ’inactive’- women were judged lacking in the capacity to create economic value. She appears today at the very heart of the production of knowledge, wealth, language and affect. Today, whilst capital aims its valorisation directly at the interior of circuits of creation , it remains incapable of subsuming it.
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  22. Arthur W. Burks, From ENIAC to the Stored Program Computer : Two Revolutions in Computers.
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  23. Cyril Burt (1961). Intelligence and Attainment Tests. The Eugenics Review 53 (1):41.
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  24. Cyril Burt (1939). The Latest Revision of the Binet Intelligence Tests. The Eugenics Review 30 (4):255.
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  25. Cyril Burt (1923). Intelligence Tests and Their Use. The Eugenics Review 15 (1):350.
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  26. Robert G. Burton (1993). Natural and Artificial Minds. Monograph Collection (Matt - Pseudo).
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  27. Raúl Carnota & Ricardo Rodríguez (2011). AGM Theory and Artificial Intelligence. In Erik J. Olson Sebastian Enqvist (ed.), Belief Revision Meets Philosophy of Science. Springer. 1--42.
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  28. Harold D. Carrier (1999). Artificial Intelligence and Metaphor Making: Some Philosophic Considerations. Knowledge, Technology and Policy 12 (1):45-59.
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  29. John L. Casti (1995). If d'Arcy Had Only Had a Computer:How Computers Have Changed the Face of Science. Complexity 1 (3):5-8.
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  30. P. Ceruzzi (1999). Talking Back to the Machine: Computers and Human Aspiration. Knowledge Technology and Policy 12 (3):115-116.
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  31. K. Chalvet-Monfray, P. Auger, L. P. Belzunces, C. Fléché & P. Sabatier (1996). Modelling Based Method for Pharmacokinetic Hypotheses Test. Acta Biotheoretica 44 (3-4):335-348.
    The aim of this work is to propose methods to test mechanism of synergy of toxic agents in bees. A synergy between prochloraz, an imidazole fungicide, and deltamethrin, a pyrethroid insecticide, was demonstrated experimentally. The hypothesis is that prochloraz modifies the penetration or the metabolism of deltamethrin. This hypothesis is tested using a pharmacokinetic box model. A previous experimental work showed that bee instantaneous mortalities were higher, from the time t 1 to the time t 2 after spraying, in (...)
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  32. A. Clark (1988). J. Haugeland, "Artificial Intelligence". Philosophical Quarterly 38 (151):249.
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  33. J. J. Clarke (1972). Turing Machines and the Mind-Body Problem. British Journal for the Philosophy of Science 23 (February):1-12.
  34. Andrew Clifton, The Introspection Game - or, Does the Tin Man Have a Heart?
    Eliminative functionalism is the view that mental attributes, of humans and other machines, consist ultimately in behavioural abilities or dispositions. Hence, ‘Strong AI’: if a machine consistently acts as if it were fully conscious, then conscious it is. From these assumptions, optimistic futurists have derived a variety of remarkable visions of our ‘post-human’ future; from widely-recognised ‘robot rights’ to ‘mind uploading’, immortality, ‘apotheosis’ and beyond. It is argued here, however, that eliminative functionalism is false; for at least on our present (...)
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  35. Jonathan Cohen (1955). Can There Be Artificial Minds? Analysis 16 (2):36 - 41.
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  36. L. Jonathan Cohen & A. D. Booth (1970). Machine Translation. Philosophical Quarterly 20 (79):187.
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  37. C. L. Constance (1932). Correlation by Calculating Machine. Journal of Experimental Psychology 15 (4):458.
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  38. Jack Copeland (1996). On Alan Turing's Anticipation of Connectionism. Synthese 108 (3):361-377.
    It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...)
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  39. Jack Copeland (1994). Turing, Wittgenstein, and the Science of the Mind. Australasian Journal of Philosophy 72 (4):497-519.
  40. J. W. Cornman, G. Cottrell, R. Cummins, A. Cussins, L. Darden, C. Darwin, W. Demopoulos, M. Derthick, H. Gardner & M. S. Gazzaniga (1993). Dreyfus, HL, 3% Dreyfus, SE, 396. In Scott M. Christensen & Dale R. Turner (eds.), Folk Psychology and the Philosophy of Mind. L. Erlbaum.
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  41. A. T. C. Cree (1902). The Axe Test. The Classical Review 16 (04):194-195.
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  42. Juremir Machado da Silva (2009). Lula, la Machine À Communiquer. Hermes 53:193.
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  43. Armand de Callataÿ (1991). Paradigm Shifts in Artificial Intelligence. In P. A. Flach (ed.), Future Directions in Artificial Intelligence. New York: Elsevier Science.
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  44. Molisa Derk (2011). An on-Line Graduate Degree in Computers and Society. Acm Sigcas Computers and Society 41 (1):19-22.
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  45. 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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  46. Vincent J. Digricoli (1986). Mind and Computer. Thought 61 (4):442-451.
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  47. Bj Dotzler (1989). Kant and Turing-on the Archaeology of Thought on the Machine. Philosophisches Jahrbuch 96 (1):115-131.
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  48. 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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  49. K. Dunlap & A. Snyder (1920). Practice Effects in Intelligence Tests. Journal of Experimental Psychology 3 (5):396.
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  50. 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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