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  1. Rogers Albritton (1964). Comments on Hilary Putnam's Robots: Machines or Artificially Created Life. Journal of Philosophy 61 (November):691-694.
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  2. Igor Aleksander (2011). Workspace Theories Are Alive and Well. International Journal of Machine Consciousness 3 (02):309-312.
  3. Igor Aleksander, David Gamez & Helen Morton (2009). Information or Logic in Modeling Conscious Systems? International Journal of Machine Consciousness 1 (02):185-192.
  4. Igor Aleksander & Helen B. Morton (2011). Informational Minds: From Aristotle to Laptops (Book Extract). International Journal of Machine Consciousness 3 (02):383-397.
  5. Patrick Allo (2012). Kees van Deemter: Not Exactly: In Praise of Vagueness. Minds and Machines 22 (1):41-45.
  6. Reza Amini, Catherine Sabourin & Joseph de Koninck (forthcoming). Word Associations Contribute to Machine Learning in Automatic Scoring of Degree of Emotional Tones in Dream Reports. Consciousness and Cognition.
  7. Susan Anderson & Michael Anderson (eds.) (2011). Machine Ethics. Cambridge University Press.
    The essays in this volume represent the first steps by philosophers and artificial intelligence researchers toward explaining why it is necessary to add an ...
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  8. Hans G. Andersson (1989). Man, Machine and Creativity. AI and Society 3 (2):155-158.
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  9. Daniel Andler (2006). Phenomenology in Cognitive Science and Artificial Intelligence. In H. Dreyfus & M. Wrathall (eds.), A Companion to Phenomenology and Existentialism.
    Fifty years before the present volume appeared, artificial intelligence (AI) and cognitive science (Cogsci) emerged from a couple of small-scale academic encounters on the East Coast of the United States. Wedded together like Siamese twins, these nascent research programs appeared to rest on some general assumptions regarding the human mind, and closely connected methodological principles, which set them at such a distance from phenomenology that no contact between the two approaches seemed conceivable. Soon however contact was made, in the form (...)
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  10. W. R. Ashby (1947). The Nervous System as Physical Machine: With Special Reference to the Origin of Adaptive Behaviour. Mind 56 (January):44-59.
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  11. Bernard J. Baars & Stan Franklin (2009). Consciousness is Computational: The Lida Model of Global Workspace Theory. International Journal of Machine Consciousness 1 (01):23-32.
  12. Denis L. Baggi (2000). The Intelligence Left in AI. AI and Society 14 (3-4):348-378.
    In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...)
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  13. Lynne Rudder Baker (1981). Why Computers Can't Act. American Philosophical Quarterly 18 (April):157-163.
  14. Alexander Barzel (1998). The Perplexing Conclusion: The Essential Difference Between Natural and Artificial Intelligence is Human Beings' Ability to Deceive. Journal of Applied Philosophy 15 (2):165–178.
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  15. Luc Patrick Beaudoin (2011). The Designer Stance Towards Shanahan's Dynamic Network Theory of the "Conscious Condition". International Journal of Machine Consciousness 3 (02):313-319.
  16. David Beisecker (2006). Dennett's Overlooked Originality. Minds and Machines 16 (1):43-55.
    No philosopher has worked harder than Dan Dennett to set the possibility of machine mentality on firm philosophical footing. Dennett’s defense of this possibility has both a positive and a negative thrust. On the positive side, he has developed an account of mental activity that is tailor-made for the attribution of intentional states to purely mechanical contrivances, while on the negative side, he pillories as mystery mongering and skyhook grasping any attempts to erect barriers to the conception of machine mentality (...)
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  17. John Beloff (2002). Minds or Machines. Truth Journal.
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  18. Jacob Berger & Kyle Ferguson (2009). You Gotta Listen to How People Talk': Machines and Natural Language. In Richard Brown & Kevin S. Decker (eds.), Terminator and Philosophy: I'll be Back, Therefore I Am.
  19. Mark H. Bickhard (1998). Levels of Representationality. Journal of Experimental and Theoretical Artificial Intelligence 10 (2):179-215.
    The dominant assumptions -- throughout contemporary philosophy, psychology, cognitive science, and artificial intelligence -- about the ontology underlying intentionality, and its core of representationality, is that of encodings -- some sort of informational or correspondence or covariation relationship between the represented and its representation that constitutes that representational relationship. There are many disagreements concerning details and implementations, and even some suggestions about claimed alternative ontologies, such as connectionism (though none that escape what I argue is the fundamental flaw in these (...)
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  20. Paul Richard Blum (2010). MICHAEL POLANYI: CAN THE MIND BE REPRESENTED BY A MACHINE? Polanyiana 19 (1-2):35-60.
    In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s original statement and his (...)
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  21. Margaret A. Boden (1995). Android Epistemology. Cambridge: MIT Press.
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  22. Margaret A. Boden (1995). Could a Robot Be Creative--And Would We Know? In Android Epistemology. Cambridge: MIT Press.
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  23. Margaret A. Boden (1969). Machine Perception. Philosophical Quarterly 19 (January):33-45.
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  24. Nick Bostrom (2012). The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. Minds and Machines 22 (2):71-85.
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  25. Nick Bostrom (2003). Taking Intelligent Machines Seriously: Reply to Critics. Futures 35 (8):901-906.
    In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators.
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  26. Philip Brey (2001). American Philosophy of Technology: The Empirical Turn. Bloomington: Indiana University Press.
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  27. Philip Brey (2001). Hubert Dreyfus: Humans Versus Computers. In American Philosophy of Technology: The Empirical Turn. Bloomington: Indiana University Press.
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  28. Selmer Bringsjord (2010). Meeting Floridi's Challenge to Artificial Intelligence From the Knowledge-Game Test for Self-Consciousness. Metaphilosophy 41 (3):292-312.
    Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is to (...)
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  29. Selmer Bringsjord (2001). In Computation, Parallel is Nothing, Physical Everything. Minds and Machines 11 (1):95-99.
    Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...)
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  30. Selmer Bringsjord (1998). Cognition is Not Computation: The Argument From Irreversibility. Synthese 113 (2):285-320.
    The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.
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  31. Selmer Bringsjord (1994). Precis of What Robots Can and Can't Be. Psycholoquy 5 (59).
    This book argues that (1) AI will continue to produce machines with the capacity to pass stronger and stronger versions of the Turing Test but that (2) the "Person Building Project" (the attempt by AI and Cognitive Science to build a machine which is a person) will inevitably fail. The defense of (2) rests in large part on a refutation of the proposition that persons are automata -- a refutation involving an array of issues, from free will to Godel to (...)
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  32. Rodney Brooks (1991). Intelligence Without Representation. Artificial Intelligence 47:139-159.
    Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...)
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  33. Mario Bunge (1956). Do Computers Think? (I). British Journal for the Philosophy of Science 7 (26):139-148.
  34. Mario Bunge (1956). Do Computers Think? (II). British Journal for the Philosophy of Science 7 (27):212-219.
  35. Arthur W. Burks (1973). Logic, Computers, and Men. Proceedings and Addresses of the American Philosophical Association 46:39-57.
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  36. Richmond M. Campbell & Alexander Rosenberg (1973). Action, Purpose, and Consciousness Among the Computers. Philosophy of Science 40 (December):547-557.
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  37. David Casacuberta Sevilla (forthcoming). The Quest for Artificial Wisdom. AI and Society.
    The term “Contemplative sciences” refers to an interdisciplinary approach to mind that aims at a better understanding of alternative states of consciousness, like those obtained trough deep concentration and meditation, mindfulness and other “superior” or “spiritual” mental states. There is, however, a key discipline missing: artificial intelligence. AI has forgotten its original aims to create intelligent machines that could help us to understand better what intelligence is and is more worried about pragmatical stuff, so almost nobody in the field seems (...)
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  38. Gerard Casey (1992). Minds and Machines. American Catholic Philosophical Quarterly 66 (1):57-80.
    The emergence of electronic computers in the last thirty years has given rise to many interesting questions. Many of these questions are technical, relating to a machine’s ability to perform complex operations in a variety of circumstances. While some of these questions are not without philosophical interest, the one question which above all others has stimulated philosophical interest is explicitly non-technical and it can be expressed crudely as follows: Can a machine be said to think and, if so, in what (...)
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  39. Hector-Neri Castaneda (ed.) (1967). Intentionality, Minds and Perception. Wayne State University Press.
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  40. David J. Chalmers (2010). The Singularity: A Philosophical Analysis. Journal of Consciousness Studies.
    What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far (...)
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  41. David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology. Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.
    High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...)
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  42. Antonio Chella (2009). Book Review: "World in My Mind, My Mind in the World" by Igor Aleksander. [REVIEW] International Journal of Machine Consciousness 1 (01):177-179.
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  43. Antonio Chella & Riccardo Manzotti (2007). Artificial Consciousness. Imprint Academic.
  44. Christopher Cherry (1991). Machines as Persons? - I. In Human Beings. New York: Cambridge University Press.
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  45. Christopher Cherry (1991). Human Beings. New York: Cambridge University Press.
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  46. Ron Chrisley (2009). Synthetic Phenomenology. International Journal of Machine Consciousness 1 (01):53-70.
    The term \synthetic phenomenology" refers to: 1) any attempt to characterize the phenomenal states possessed, or modeled by, an artefact (such as a robot); or 2) any attempt to use an artefact to help specify phenomenal states (independently of whether such states are possessed by a naturally conscious being or an artefact). The notion of synthetic phenomenology is clari¯ed, and distinguished from some related notions. It is argued that much work in machine consciousness would bene¯t from being more cognizant of (...)
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  47. Ron Chrisley, I. Aleksander, S. Bringsjord, R. Clowes, J. Parthemore, S. Stuart, S. Torrance & T. Ziemke (2008). Assessing Artificial Consciousness: A Collective Review Article. Journal of Consciousness Studies 15 (7):95-110.
    While the recent special issue of JCS on machine consciousness (Volume 14, Issue 7) was in preparation, a collection of papers on the same topic, entitled Artificial Consciousness and edited by Antonio Chella and Riccardo Manzotti, was published. The editors of the JCS special issue, Ron Chrisley, Robert Clowes and Steve Torrance, thought it would be a timely and productive move to have authors of papers in their collection review the papers in the Chella and Manzotti book, and include these (...)
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  48. William Clancey (1995). How Situated Cognition is Different From Situated Robotics. In Luc Steels & Rodney Brooks (eds.), The "Artificial Life" Route to "Artificial Intelligence": Building Situated Embodied Agents. Hillsdale, Nj: Lawrence Erlbaum Associates.
  49. Andy Clark (2001). Mindware: An Introduction to the Philosophy of Cognitive Science. New York: Oxford University Press.
    Mindware: An Introduction to the Philosophy of Cognitive Science invites readers to join in up-to-the-minute conceptual discussions of the fundamental issues, problems, and opportunities in cognitive science. Written by one of the most renowned scholars in the field, this vivid and engaging introductory text relates the story of the search for a cognitive scientific understanding of mind. This search is presented as a no-holds-barred journey from early work in artificial intelligence, through connectionist (artificial neural network) counter-visions, and on to neuroscience, (...)
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  50. Andy Clark (1991). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge: MIT Press.
  51. Jon Cogburn & Jason Megil (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  52. L. Jonathan Cohen (1955). Can There Be Artificial Minds? Analysis 16 (December):36-41.
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  53. Allan Collins & Edward E. Smith (eds.) (1988). Readings in Cognitive Science, a Perspective From Psychology and Artificial Intelligence. Morgan Kaufmann Publishers.
  54. Harry M. Collins (2008). Response to Selinger on Dreyfus. Phenomenology and the Cognitive Sciences 7 (2).
    My claim is clear and unambiguous: no machine will pass a well-designed Turing Test unless we find some means of embedding it in lived social life. We have no idea how to do this but my argument, and all our evidence, suggests that it will not be a necessary condition that the machine have more than a minimal body. Exactly how minimal is still being worked out.
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  55. B. Jack Copeland (2000). Narrow Versus Wide Mechanism: Including a Re-Examination of Turing's Views on the Mind-Machine Issue. Journal of Philosophy 97 (1):5-33.
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  56. B. Jack Copeland & Diane Proudfoot (2000). What Turing Did After He Invented the Universal Turing Machine. Journal of Logic, Language and Information 9 (4):491-509.
    Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis (...)
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  57. Roberto Cordeschi (2007). AI Turns Fifty: Revisiting its Origins. Applied Artificial Intelligence 21:259-279.
    Applied Artificial Intelligence, 21, 2007, pp. 259-279.
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  58. David Corfield (forthcoming). Varieties of Justification in Machine Learning. Minds and Machines.
    Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.
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  59. Charles B. Cross (2004). A Correction to “Nonmonotonic Inconsistency” [Artificial Intelligence 149 (2003) 161–178]. Artificial Intelligence 160 (1-2):191-192.
    This note corrects an error in the statement and proof of Propositions 9 and 10 of [C. Cross, Nonmonotonic inconsistency, Artificial Intelligence 149 (2) (2003) 161–178]. Both results turn out to depend on the postulate of Consistency Preservation.
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  60. Jamie Cullen (2009). Imitation Versus Communication: Testing for Human-Like Intelligence. Minds and Machines 19 (2):237-254.
    Turing’s Imitation Game is often viewed as a test for theorised machines that could ‘think’ and/or demonstrate ‘intelligence’. However, contrary to Turing’s apparent intent, it can be shown that Turing’s Test is essentially a test for humans only. Such a test does not provide for theorised artificial intellects with human-like, but not human-exact, intellectual capabilities. As an attempt to bypass this limitation, I explore the notion of shifting the goal posts of the Turing Test, and related tests such as the (...)
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  61. Lisa Damm (2011). Moral Machines: Teaching Robots Right From Wrong. Philosophical Psychology 25 (1):149 - 153.
    Philosophical Psychology, Volume 25, Issue 1, Page 149-153, February 2012.
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  62. Lindley Darden (1982). Artificial Intelligence and Philosophy of Science: Reasoning by Analogy in Theory Construction. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:147 - 165.
    This paper examines the hypothesis that analogies may play a role in the generation of new ideas that are built into new explanatory theories. Methods of theory construction by analogy, by failed analogy, and by modular components from several analogies are discussed. Two different analyses of analogy are contrasted: direct mapping (Mary Hesse) and shared abstraction (Michael Genesereth). The structure of Charles Darwin's theory of natural selection shows various analogical relations. Finally, an "abstraction for selection theories" is shown to be (...)
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  63. Terry Dartnall (2000). Reverse Psychologism, Cognition and Content. Minds and Machines 10 (1):31-52.
    The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and (...)
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  64. Kenneth M. Dayre (1968). Intelligence, Bodies, and Digital Computers. Review of Metaphysics 21 (June):714-723.
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  65. Pierre De Loor, Kristen Manac’H. & Jacques Tisseau (2009). Enaction-Based Artificial Intelligence: Toward Co-Evolution with Humans in the Loop. Minds and Machines 19 (3):319-343.
    This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate (...)
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  66. Don Dedrick (1998). [Book Chapter].
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  67. Craig DeLancey (2001). Passionate Engines: What Emotions Reveal About the Mind and Artificial Intelligence. Oxford University Press.
    The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help (...)
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  68. William A. Dembski (1999). Are We Spiritual Machines? First Things 96:25-31.
    For two hundred years materialist philosophers have argued that man is some sort of machine. The claim began with French materialists of the Enlightenment such as Pierre Cabanis, Julien La Mettrie, and Baron d’Holbach (La Mettrie even wrote a book titled Man the Machine). Likewise contemporary materialists like Marvin Minsky, Daniel Dennett, and Patricia Churchland claim that the motions and modifications of matter are sufficient to account for all human experiences, even our interior and cognitive ones. Whereas the Enlightenment philosophes (...)
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  69. Daniel C. Dennett (1997). Did Hal Committ Murder? In D. Stork (ed.), Hal's Legacy: 2001's Computer As Dream and Reality. MIT Press.
    The first robot homicide was committed in 1981, according to my files. I have a yellowed clipping dated 12/9/81 from the Philadelphia Inquirer--not the National Enquirer--with the headline: Robot killed repairman, Japan reports The story was an anti-climax: at the Kawasaki Heavy Industries plant in Akashi, a malfunctioning robotic arm pushed a repairman against a gearwheel-milling machine, crushing him to death. The repairman had failed to follow proper instructions for shutting down the arm before entering the workspace. Why, indeed, had (...)
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  70. Daniel C. Dennett (1989). Cognitive Ethology. In Goals, No-Goals and Own Goals. Unwin Hyman.
    The field of Artificial Intelligence has produced so many new concepts--or at least vivid and more structured versions of old concepts--that it would be surprising if none of them turned out to be of value to students of animal behavior. Which will be most valuable? I will resist the temptation to engage in either prophecy or salesmanship; instead of attempting to answer the question: "How might Artificial Intelligence inform the study of animal behavior?" I will concentrate on the obverse: "How (...)
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  71. Daniel C. Dennett (1984). Can Machines Think? In M. G. Shafto (ed.), How We Know. Harper & Row.
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  72. Eric Dietrich (2011). Homo Sapiens 2.0 Why We Should Build the Better Robots of Our Nature. In M. Anderson S. Anderson (ed.), Machine Ethics. Cambridge Univ. Press.
    It is possible to survey humankind and be proud, even to smile, for we accomplish great things. Art and science are two notable worthy human accomplishments. Consonant with art and science are some of the ways we treat each other. Sacrifice and heroism are two admirable human qualities <span class='Hi'>that</span> pervade human interaction. But, as everyone knows, all this goodness is more than balanced by human depravity. Moral corruption infests our being. Why?
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  73. Eric Dietrich (2001). Banbury Bound, or Can a Machine Be Conscious? J. Of Experimental and Theoretical AI 13 (2):177-180.
    In mid-May of 2001, I attended a fascinating workshop at Cold Spring Harbor Labs. The conference was held at the lab's Banbury Center, an elegant mansion and its beautiful surrounding estate, located on Banbury Lane, in the outskirts of Lloyd Harbor, overlooking the north shore of Long Island in New York. The estate was formerly owned by Charles Sammis Robertson. In 1976, Robertson donated his estate, and an endowment for its upkeep, to the Lab. The donation included the Robertson's mansion, (...)
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  74. Eric Dietrich (2000). Cognitive Science and the Mechanistic Forces of Darkness, or Why the Computational Science of Mind Suffers the Slings and Arrows of Outrageous Fortune. Techné 5 (2):73-82.
    A recent issue of Time magazine (March 29, 1999) was devoted to the twenty greatest "thinkers" of the twentieth century -- scientists, inventors, and engineers. There is one interesting omission: there are no cognitive psychologists or cognitive scientists. (Cognitive science is an amalgam of cognitive, neuro, and developmental psychology, artificial intelligence, philosophy, linguistics, biology, and anthropology.) Freud is there, to be sure. But, while he was very influential, it is not even clear that he was a scientist, let alone a (...)
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  75. Eric Dietrich (1996). AI, Situatedness, Creativity, and Intelligence; or the Evolution of the Little Hearing Bones. J. Of Experimental and Theoretical AI 8 (1):1-6.
    Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, (...)
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  76. Eric Dietrich (1995). AI and the Mechanistic Forces of Darkness. J. Of Experimental and Theoretical AI 7 (2):155-161.
    Under the Superstition Mountains in central Arizona toil those who would rob humankind o f its humanity. These gray, soulless monsters methodically tear away at our meaning, our subjectivity, our essence as transcendent beings. With each advance, they steal our freedom and dignity. Who are these denizens of darkness, these usurpers of all that is good and holy? None other than humanity’s arch-foe: The Cognitive Scientists -- AI researchers, fallen philosophers, psychologists, and other benighted lovers of computers. Unless they are (...)
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  77. Eric Dietrich (1994). AI and the Tyranny of Galen, or Why Evolutionary Psychology and Cognitive Ethology Are Important to Artificial Intelligence. Journal of Experimental And Theoretical Artificial Intelligence 6 (4):325-330.
    Concern over the nature of AI is, for the tastes many AI scientists, probably overdone. In this they are like all other scientists. Working scientists worry about experiments, data, and theories, not foundational issues such as what their work is really about or whether their discipline is methodologically healthy. However, most scientists aren’t in a field that is approximately fifty years old. Even relatively new fields such as nonlinear dynamics or branches of biochemistry are in fact advances in older established (...)
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  78. Fred Dretske (1993). Can Intelligence Be Artificial? Philosophical Studies 71 (2):201-16.
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  79. Fred Dretske (1985). Machines and the Mental. Proceedings and Addresses of the American Philosophical Association 59 (1):23-33.
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  80. Eric Drexler (1986). Engines of Creation. Fourth Estate.
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  81. Eric Drexler (1986). Thinking Machines. In Engines of Creation. Fourth Estate.
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  82. Hubert L. Dreyfus (1972). What Computers Can't Do. Harper and Row.
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  83. Hubert L. Dreyfus (1967). Why Computers Must Have Bodies in Order to Be Intelligent. Review of Metaphysics 21 (September):13-32.
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  84. Adam Drozdek (1993). Computers and the Mind-Body Problem: On Ontological and Epistemological Dualism. Idealistic Studies 23 (1):39-48.
    There seems to exist an indirect link between computer science and theology via psychology, which is founded on dualism. First, these theories from psychology, computer science and theology are considered that acknowledge the existence of (at least) two different kinds of reality, or, possibly, two different realms of the same reality. In order to express a root of incompatibility of science and theology, a distinction is drawn between ontological and epistemological dualism. It seems that computer science combines ontological monism with (...)
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  85. Claus Emmeche, Does a Robot Have an Umwelt?
    It is argued that the notion of Umwelt is relevant for contemporary discussions within theoretical biology, biosemiotics, the study of Artificial Life, Autonomous Systems Research and philosophy of biology. Focus is put on the question of whether an artificial creature can have a phenomenal world in the sense of the Umwelt notion of Jakob von Uexküll, one of the founding figures of biosemiotics. Rather than vitalism, Uexküll's position can be interpreted as a version of qualitative organicism. A historical sketch of (...)
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  86. Ronald P. Endicott (1996). Searle, Syntax, and Observer-Relativity. Canadian Journal of Philosophy 26 (1):101-22.
    I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...)
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  87. Timothy Ferris (1992). The Mind's Sky: Human Intelligence in a Cosmic Context. Bantam Books.
  88. Mark Fisher (1983). A Note on Free Will and Artificial Intelligence. Philosophia 13 (September):75-80.
  89. John M. Ford (1991). Review of Artificial Intelligence and Human Reason: A Teleological Critique. [REVIEW] Theoretical and Philosophical Psychology 11 (2):126-130.
  90. P. J. Fozzy (1963). Professor MacKay on Machines. British Journal for the Philosophy of Science 14 (August):154-156.
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  91. Julian Friedland (2005). Wittgenstein and the Aesthetic Robot's Handicap. Philosophical Investigations 28 (2):177-192.
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  92. James S. Fulton (1957). Computing Machines and Minds. Personalist 38:62-72.
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  93. Salvatore Gaglio (2007). Intelligent Artificial Systems. In Antonio Chella & Riccardo Manzotti (eds.), Artificial Consciousness. Imprint Academic.
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  94. Matjaz Gams (ed.) (1997). Mind Versus Computer: Were Dreyfus and Winograd Right? Amsterdam: IOS Press.
    M. Gams et al. (Eds.) IOS Press, "Strong AI": an Adolescent Disorder Donald Michie Professor Emeritus, University of Edinburgh, UK Associate ...
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  95. Alan Gauld (1966). Could a Machine Perceive? British Journal for the Philosophy of Science 17 (May):44-58.
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  96. Daniel Gogol (1970). Determinism and the Predicting Machine. Philosophy and Phenomenological Research 30 (March):455-456.
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  97. Sanford C. Goldberg (1997). The Very Idea of Computer Self-Knowledge and Self-Deception. Minds and Machines 7 (4):515-529.
    Do computers have beliefs? I argue that anyone who answers in the affirmative holds a view that is incompatible with what I shall call the commonsense approach to the propositional attitudes. My claims shall be two. First,the commonsense view places important constraints on what can be acknowledged as a case of having a belief. Second, computers – at least those for which having a belief would be conceived as having a sentence in a belief box – fail to satisfy some (...)
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  98. Stuart Goldkind (1982). Machines and Mistakes. Ratio 24 (December):173-184.
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  99. Antoni Gomila (1995). Android Epistemology. Cambridge: MIT Press.
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  100. Antoni Gomila (1995). From Cognitive Systems to Persons. In Android Epistemology. Cambridge: MIT Press.
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