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Summary The content of this category can be found in the categories "Can machines think?" and "Machine consciousness." 
Key works The content of this category can be found in the categories "Can machines think?" and "Machine consciousness."  See those two for readings and references, also.
Introductions See the categories "Can machines think?" and "Machine consciousness"
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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. [REVIEW] Minds and Machines 22 (1):41-45.
  6. Reza Amini, Catherine Sabourin & Joseph de Koninck (2011). Word Associations Contribute to Machine Learning in Automatic Scoring of Degree of Emotional Tones in Dream Reports. Consciousness and Cognition 20 (4):1570-1576.
  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.
    To be an agent, one must be able to formulate intentions. To be able to formulate intentions, one must have a first-person perspective. Computers lack a first-person perspective. So, computers are not agents.
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  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. 239-252.
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  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. Kimberly Bonia, Fern Brunger, Laura Fullerton, Chad Griffiths & Chris Kaposy (2012). DAKO on Trial. Techné: Research in Philosophy and Technology 16 (3):275-295.
    This paper tells the story of a recent laboratory medicine controversy in the Canadian province of Newfoundland and Labrador. During the controversy, a DAKOAutostainer machine was blamed for inaccurate breast cancer test results that led to the suboptimal treatment of many patients. In truth, the machine was not at fault. Using concepts developed by Bruno Latour and Pierre Bourdieu, we document the changing nature of the DAKO machine’s agency before, during, and after the controversy, and we make the ethical argument (...)
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  25. Nick Bostrom (2012). The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. [REVIEW] Minds and Machines 22 (2):71-85.
    This paper discusses the relation between intelligence and motivation in artificial agents, developing and briefly arguing for two theses. The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having (...)
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  26. 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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  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. Philip Brey (2001). American Philosophy of Technology: The Empirical Turn. Bloomington: Indiana University Press.
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  29. 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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  30. 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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  31. 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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  32. 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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  33. 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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  34. Mario Bunge (1956). Do Computers Think? (I). British Journal for the Philosophy of Science 7 (26):139-148.
  35. Mario Bunge (1956). Do Computers Think? (II). British Journal for the Philosophy of Science 7 (27):212-219.
  36. Arthur W. Burks (1973). Logic, Computers, and Men. Proceedings and Addresses of the American Philosophical Association 46:39-57.
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  37. Richmond M. Campbell & Alexander Rosenberg (1973). Action, Purpose, and Consciousness Among the Computers. Philosophy of Science 40 (December):547-557.
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  38. David Casacuberta Sevilla (2013). The Quest for Artificial Wisdom. AI and Society 28 (2):199-207.
    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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  39. 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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  40. Hector-Neri Castaneda (ed.) (1967). Intentionality, Minds and Perception. Wayne State University Press.
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  41. David J. Chalmers (2010). The Singularity: A Philosophical Analysis. Journal of Consciousness Studies 17 (9-10):9 - 10.
    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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  42. 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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  43. 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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  44. Antonio Chella & Riccardo Manzotti (2007). Artificial Consciousness. Imprint Academic.
  45. Christopher Cherry (1991). Machines as Persons? - I. In Human Beings. New York: Cambridge University Press. 11-24.
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  46. Christopher Cherry (1991). Human Beings. New York: Cambridge University Press.
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  47. 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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  48. 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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  49. 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. 227-236.
  50. 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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