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Summary

The philosophy of artificial intelligence is a collection of issues primarily concerned with whether or not AI is possible -- with whether or not it is possible to build an intelligent thinking machine.  Also of concern is whether humans and other animals are best thought of as machines (computational robots, say) themselves. The most important of the "whether-possible" problems lie at the intersection of theories of the semantic contents of thought and the nature of computation. A second suite of problems surrounds the nature of rationality. A third suite revolves around the seeming “transcendent” reasoning powers of the human mind. These problems derive from Kurt Gödel's famous Incompleteness Theorem.  A fourth collection of problems concerns the architecture of an intelligent machine.  Should a thinking computer use discrete or continuous modes of computing and representing, is having a body necessary, and is being conscious necessary.  This takes us to the final set of questions. Can a computer be conscious?  Can a computer have a moral sense? Would we have duties to thinking computers, to robots?  For example, is it moral for humans to even attempt to build an intelligent machine?  If we did build such a machine, would turning it off be the equivalent of murder?  If we had a race of such machines, would it be immoral to force them to work for us?

Key works Probably the most important attack on whether AI is possible is John Searle's famous Chinese Room Argument: Searle 1980.  This attack focuses on the semantic aspects (mental semantics) of thoughts, thinking, and computing.   For some replies to this argument, see the same 1980 journal issue as Searle's original paper.  For the problem of the nature of rationality, see Pylyshyn 1987.  An especially strong attack on AI from this angle is Jerry Fodor's work on the frame problem: Fodor 1987.  On the frame problem in general, see McCarthy & Hayes 1969.  For some replies to Fodor and advances on the frame problem, see Ford & Pylyshyn 1996.  For the transcendent reasoning issue, a central and important paper is Hilary Putnam's Putnam 1960.  This paper is arguably the source for the computational turn in 1960s-70s philosophy of mind.  For architecture-of-mind issues, see, for starters: M. Spivey's The Contintuity of Mind, Oxford, which argues against the notion of discrete representations. See also, Gelder & Port 1995.  For an argument for discrete representations, see, Dietrich & Markman 2003.  For an argument that the mind's boundaries do not end at the body's boundaries, see, Clark & Chalmers 1998.  For a statement of and argument for computationalism -- the thesis that the mind is a kind of computer -- see Shimon Edelman's excellent book Edelman 2008. See also Chapter 9 of Chalmers's book Chalmers 1996.
Introductions Chinese Room Argument: Searle 1980. Frame problem: Fodor 1987, Computationalism and Godelian style refutation: Putnam 1960. Architecture: M. Spivey's The Contintuity of Mind, Oxford and Shimon Edelman's Edelman 2008. Ethical issues: Anderson & Anderson 2011.  Conscious computers: Chalmers 2011.
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  1. D. Ahn, G. Ben-Avi, D. Ben Shalom, Ph Besnard, K. Borthen, C. Caleiro, W. A. Carnielli, M. E. Coniglio, R. Cooper & N. Dimitri (2003). Index of Authors of Volume 12. Journal of Logic, Language and Information 12 (531):531.
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  2. Janette Aschenwald, Stefan Fink & Gottfried Tappeiner (2001). Brave New Modeling: Cellular Automata and Artificial Neural Networks for Mastering Complexity in Economics. Complexity 7 (1):39-47.
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  3. W. Ross Ashby (1952). Can a Mechanical Chess-Player Outplay its Designer? British Journal for the Philosophy of Science 3 (9):44-57.
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  4. Kevin D. Ashley & Will Bridewell (2010). Emerging AI & Law Approaches to Automating Analysis and Retrieval of Electronically Stored Information in Discovery Proceedings. Artificial Intelligence and Law 18 (4):311-320.
    This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...)
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  5. Din Aslamazishvili (2008). Structure of Symbol Within Cultural Transitions. Proceedings of the Xxii World Congress of Philosophy 12:3-7.
    Among such social-philosophic notions as society, culture, civilization, system, human, sense, sign, truth and others, concept “symbol” takes a special place. Most of the researchers meet the view, that symbol possesses an important place in the development of culture as a social phenomenon. The role of symbol in cultures birth and development is characterized by antipathy and polysemy. However revelation of the symbol role in spiritual processes of cultural transitions is beyond question one of the urgent philosophic issues. Symbol is (...)
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  6. Thomas B.�ck (1997). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Complexity 2 (4):28-30.
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  7. Phil Badger (2014). The Morality Machine. Philosophy Now 104:24-27.
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  8. R. M. Baer (1969). Definability by Turing Machines. Mathematical Logic Quarterly 15 (20‐22):325-332.
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  9. R. M. Baer (1967). Certain Directed Post Systems and Automata. Mathematical Logic Quarterly 13 (7‐12):151-174.
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  10. John Baker (unknown). Philosophy and Artificial Intelligence in Canada. Eidos: The Canadian Graduate Journal of Philosophy 12.
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  11. Dominic J. Balestra (1978). "Cybernetics and the Philosophy of Mind," by Kenneth Sayre. Modern Schoolman 55 (3):300-305.
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  12. S. Ball (1901). Richet, C. -L'Homme Et l'Intelligence. Mind 10:145.
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  13. Tirthnath Bandyopadhyay (1985). Man and Machine. Indian Philosophical Quarterly 12 (1):37.
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  14. Ranan B. Banerji (1991). The Need for a Formal Education in Artificial Intelligence. In P. A. Flach (ed.), Future Directions in Artificial Intelligence. New York: Elsevier Science.
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  15. John Barber (1999). Cybernetic Engines. Kairos 4 (1).
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  16. David Barker-Plummer, Turing Machines. Stanford Encyclopedia of Philosophy.
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  17. John A. Barnden (1995). Simulative Reasoning, Common-Sense Psychology and Artificial Intelligence. In Martin Davies & Tony Stone (eds.), Mental Simulation: Evaluations and Applications. Blackwell. 247--273.
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  18. Robin Barrow (2015). Language, Intelligence, and Thought. Routledge.
    In this text, first published in 1993, Barrow decisively rejects the traditional assumption that intelligence has no educational significance and contends instead that intelligence is developed by the enlargement of understanding. Arguing that much educational research is driven by a concept of intelligence that has no obvious educational relevance, Dr Barrow suggests that this is partly due to a widespread lack of understanding about the nature and point of philosophical analysis, and partly due to a failure to face up to (...)
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  19. John A. Barry (1991). Technobabble. Monograph Collection (Matt - Pseudo).
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  20. Thomas C. Bartee (1991). Computer Architecture and Logic Design.
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  21. Archana Baruah (2006). Philosophy of Artificial Intelligence: A Heideggerian Approach. Philosophy Pathways 122.
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  22. Jon Barwise & John Etchemendy (1993). Turing's World 3.0 for the Macintosh an Introduction to Computability Theory.
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  23. John Basl (2014). Machines as Moral Patients We Shouldn't Care About (Yet): The Interests and Welfare of Current Machines. Philosophy and Technology 27 (1):79-96.
    In order to determine whether current (or future) machines have a welfare that we as agents ought to take into account in our moral deliberations, we must determine which capacities give rise to interests and whether current machines have those capacities. After developing an account of moral patiency, I argue that current machines should be treated as mere machines. That is, current machines should be treated as if they lack those capacities that would give rise to psychological interests. Therefore, they (...)
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  24. Caroline Bassett (1999). Intelligence Inside? Radical Philosophy 93.
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  25. Gianfranco Basti (2013). Intelligence and Reference. In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. 139--159.
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  26. Michael Baumann (2000). Robot Worlds. Complexity 5 (6):48-50.
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  27. R. J. Baxter (1972). On Unlimited Register Machines. Mathematical Logic Quarterly 18 (7):97-102.
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  28. Umut Baysan (2015). Realization Relations in Metaphysics. Minds and Machines:1-14.
    “Realization” is a technical term that is used by metaphysicians, philosophers of mind, and philosophers of science to denote some dependence relation that is thought to obtain between higher-level properties and lower-level properties. It is said that mental properties are realized by physical properties; functional and computational properties are realized by first-order properties that occupy certain causal/functional roles; dispositional properties are realized by categorical properties; so on and so forth. Given this wide usage of the term “realization”, it would be (...)
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  29. Anthony F. Beavers, Mechanists of the Revolution: The Case of Edison and Bell.
    The “information age” is often thought in terms of the digital revolution that begins with Turing’s 1937 paper, “On computable numbers, with an application to the Entscheidungsproblem.” However, this can only be partially correct. There are two aspects to Turing’s work: one dealing with questions of computation that leads to computer science and another concerned with building computing machines that leads to computer engineering. Here, we emphasize the latter because it shows us a Turing connected with mechanisms of information flow (...)
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  30. Verónica Becher (2012). Turing's Normal Numbers: Towards Randomness. In S. Barry Cooper (ed.), How the World Computes. 35--45.
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  31. Randall D. Beer & Paul L. Williams (2015). Information Processing and Dynamics in Minimally Cognitive Agents. Cognitive Science 39 (1):1-38.
    There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined into a (...)
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  32. J. L. Bell (1995). Review of B. Rotman, Ad Infinitum - The Ghost In Turing's Machine: Taking God Out of Mathematics and Putting the Body Back In: An Essay in Corporeal Semiotics. [REVIEW] Philosophia Mathematica 3 (2):218-221.
  33. Irena Bellert (1998). Human Reasoning and Artificial Intelligence. When Are Computers Dumb in Simulating Human Reasoning? Poznan Studies in the Philosophy of the Sciences and the Humanities 62:95-102.
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  34. Trevor J. M. Bench-Capon & Paul E. Dunne (2005). Argumentation in AI and Law: Editors' Introduction. [REVIEW] Artificial Intelligence and Law 13 (1):1-8.
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  35. Enoch Arnold Bennett (1908). The Human Machine.
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  36. Arnold Berleant (1968). V. Tejera's "Art and Human Intelligence". [REVIEW] Philosophy and Phenomenological Research 29 (2):307.
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  37. S. Bernardini (2006). Machine Readable Corpora. In Keith Brown (ed.), Encyclopedia of Language and Linguistics. Elsevier. 358--375.
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  38. Jeremy Bernstein (1982). Science Observed Essays Out of My Mind /Jeremy Bernstein. --. --. Basic Books, C1982.
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  39. Jeremy Bernstein (1982). Science Observed Essays Out of My Mind.
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  40. K. Bertels, L. Neuberg, S. Vassiliadis & G. Pechanek (2000). A Look Inside the Learning Process of Neural Networks. Complexity 5 (6):34-38.
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  41. Jamshed J. Bharucha (2002). Neural Nets, Temporal Composites, and Tonality. In Daniel Levitin (ed.), Foundations of Cognitive Psychology: Core Readings. Mit Press. 455.
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  42. Francesco Bianchini (2008). Concetti Analogici: L'Approccio Subcognitivo Allo Studio Della Mente. Quodlibet.
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  43. M. Bickhard (1999). Representation in Natural and Artificial Agents. In Edwina Taborsky (ed.), Semiosis. Evolution. Energy: Towards a Reconceptualization of the Sign. Shaker Verlag. 15--26.
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  44. Adam Biela (1974). The Decision Model of Information Processing in Situations with a Source of Reliable Information. Roczniki Filozoficzne 22 (4):115.
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  45. Aart Bijl (1995). Ourselves and Computers Difference in Minds and Machines. Monograph Collection (Matt - Pseudo).
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  46. Antonia Birnbaum & Olivier Nottellet (2004). La machine à dessiner. Multitudes 1 (1):91-99.
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  47. Mag Stefan Blachfellner, Rafael Capurro, Johannes Britz, Thomas Hausmanninger, Makoto Nakada & Marcus Apel (2009). Business Intelligence Meets Moral Intelligence. International Review of Information Ethics 10:02.
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  48. D. S. Blacklock (1956). A Register of Intelligence. The Eugenics Review 47 (4):267.
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  49. D. S. Blank, L. A. Meeden & J. B. Marshall (1992). Symbolic Manipulations Via Subsymbolic Computations. In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. 113--148.
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  50. M. A. Boden (1990). The Social Impact of Artificial Intelligence. In R. Kurzweil (ed.), The Age of Intelligent Machines. Mit Press. 450--453.
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