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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. E. J. A. (1966). Art and Human Intelligence. [REVIEW] Review of Metaphysics 19 (3):602-602.
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  2. Sam S. Adams & Steve Burbeck (2012). Beyond the Octopus: From General Intelligence Toward a Human-Like Mind. In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. 49--65.
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  3. Bernardo Aguilera (2015). Behavioural Explanation in the Realm of Non-Mental Computing Agents. Minds and Machines 25 (1):37-56.
    Recently, many philosophers have been inclined to ascribe mentality to animals on the main grounds that they possess certain complex computational abilities. In this paper I contend that this view is misleading, since it wrongly assumes that those computational abilities demand a psychological explanation. On the contrary, they can be just characterised from a computational level of explanation, which picks up a domain of computation and information processing that is common to many computing systems but is autonomous from the domain (...)
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  4. 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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  5. Kenneth Aizawa (2003). Cognitive Architecture: The Structure of Cognitive Representations. In Stephen P. Stich & Ted A. Warfield (eds.), The Blackwell Guide to Philosophy of Mind. Blackwell. 172--189.
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  6. Igor Aleksander (2008). Modeling Consciousness in Virtual Computational Machines. Functionalism and Phenomenology. Synthesis Philosophica 22 (2):447-454.
    This paper describes the efforts of those who work with informational machines and with informational analyses to provide a basis for understanding consciousness and for speculating on what it would take to make a conscious machine. Some of the origins of these considerations are covered and the contributions of several researchers are reviewed. A distinction is drawn between functional and phenomenological approaches showing how the former lead to algorithmic methods based on conventional programming, while the latter lead to neural network (...)
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  7. Atocha Aliseda & Donald Gillies (2007). Logical, Historical and Computational Approaches. In Theo A. F. Kuipers (ed.), General Philosophy of Science. North Holland. 431--513.
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  8. Milton L. Andersen (1994). The Many and Varied Social Constructions of Intelligence. In Theodore R. Sarbin & John I. Kitsuse (eds.), Constructing the Social. Sage. 119--38.
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  9. Alan Ross Anderson & Kenneth M. Sayre (1966). Recognition: A Study in the Philosophy of Artificial Intelligence. Philosophical Quarterly 16 (65):387.
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  10. Michael Anderson (2004). Sex Differences in General Intelligence. In R. L. Gregory (ed.), The Oxford Companion to the Mind. Oxford University Press. 828--829.
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  11. Nicholas S. Anderson (2011). Unhomely at Home: Dwelling with Domestic Robots. Mediatropes 2 (1):37-59.
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  12. Rebecca Anderson (1987). Computers to Go. BioScience 37 (10):700-700.
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  13. D. Andler (1990). What is the Place of Artificial-Intelligence in Cognition Studies. Revue Internationale de Philosophie 44 (172):62-86.
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  14. John H. Andreae (1987). Design of a Conscious Robot. Metascience 5:41-54.
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  15. Jensine Andresen & Robert Kc Forman (2000). Cognitive Models and Spiritual Maps. Journal of Consciousness Studies. Controversies in Science and the Humanities, Special Edition 7 (11-12):4-287.
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  16. Zilhão António (2005). Are Our Brains Subcutaneous Machines of Truth-Optimization? Abstracta 1 (2):125-144.
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  17. Michael A. Arbib (2004). Beware the Passionate Robot. In J. Fellous (ed.), Who Needs Emotions. Oxford University Press.
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  18. Itamar Arel (2012). Deep Reinforcement Learning as Foundation for Artificial General Intelligence. In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. 89--102.
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  19. Solvi Arnold, Reiji Suzuki & Takaya Arita (2015). Selection for Representation in Higher-Order Adaptation. Minds and Machines 25 (1):73-95.
    A theory of the evolution of mind cannot be complete without an explanation of how cognition became representational. Artificial approximations of cognitive evolution do not, in general, produce representational cognition. We take this as an indication that there is a gap in our understanding of what drives evolution towards representational solutions, and propose a theory to fill this gap. We suggest selection for learning and selection for second order learning as the causal factors driving the emergence of innate and acquired (...)
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  20. Peter Asaro (2008). How Just Could a Robot War Be? In P. Brey, A. Briggle & K. Waelbers (eds.), Current Issues in Computing and Philosophy. Ios Press. 50--64.
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  21. 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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  22. 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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  23. Thomas B.�ck (1997). Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Complexity 2 (4):28-30.
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  24. R. M. Baer (1969). Definability by Turing Machines. Mathematical Logic Quarterly 15 (20‐22):325-332.
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  25. R. M. Baer (1967). Certain Directed Post Systems and Automata. Mathematical Logic Quarterly 13 (7‐12):151-174.
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  26. Dominic J. Balestra (1978). "Cybernetics and the Philosophy of Mind," by Kenneth Sayre. Modern Schoolman 55 (3):300-305.
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  27. 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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  28. John Barber (1999). Cybernetic Engines. Kairos 4 (1).
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  29. David Barker-Plummer, Turing Machines. Stanford Encyclopedia of Philosophy.
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  30. 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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  31. 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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  32. Gianfranco Basti (2013). Intelligence and Reference. In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. 139--159.
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  33. Michael Baumann (2000). Robot Worlds. Complexity 5 (6):48-50.
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  34. R. J. Baxter (1972). On Unlimited Register Machines. Mathematical Logic Quarterly 18 (7):97-102.
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  35. 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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  36. Verónica Becher (2012). Turing's Normal Numbers: Towards Randomness. In S. Barry Cooper (ed.), How the World Computes. 35--45.
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  37. 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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  38. 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.
  39. S. Bernardini (2006). Machine Readable Corpora. In Keith Brown (ed.), Encyclopedia of Language and Linguistics. Elsevier. 358--375.
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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. Antonia Birnbaum & Olivier Nottellet (2004). La machine à dessiner. Multitudes 1 (1):91-99.
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  45. 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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  46. D. S. Blacklock (1956). A Register of Intelligence. The Eugenics Review 47 (4):267.
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  47. 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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  48. 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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  49. Margaret Boden (1990). 1 Artificial Intelligence and Images of Man L'intelligence Artificielle Et les Images de L'Homme. In Tadeusz Buksiński (ed.), Interpretation in the Humanities. Uniwersytet Im. Adama Mickiewicza W Poznaniu. 71--10.
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  50. John Bolender (1998). Real Algorithms: A Defense of Cognitivism. Philosophical Inquiry 20 (3-4):41-58.
    John Searle dismisses the attempt to understand thought as a form of computation, on the grounds that it is not scientific. Science is concerned with intrinsic properties, i.e. those features which are not observer relative, e.g. science is concerned with mass but not with beauty. Computation, according to Searle, presupposes the property of following an algorithm, but algorithmicity is normative, by reason of appealing to function, and hence not intrinsic. I argue that Searle's critique presupposes the folk notion of function, (...)
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