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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. Farzaneh Abdollahi (2009). Computational Intelligence Part II Lecture 1: Identification Using Neural Networks. In L. Magnani (ed.), Computational Intelligence.
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  2. Fred Adams & Rebecca Garrison (2014). The Mark of the Cognitive: Reply to Elpidorou. Minds and Machines 24 (2):213-216.
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  3. 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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  4. Morana Alač (forthcoming). Social Robots: Things or Agents? AI and Society.
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  5. Patrick Allo (2015). Donald W. Loveland, Richard E. Hodel, and S. G. Sterrett: Three Views of Logic: Mathematics, Philosophy and Computer Science. [REVIEW] Minds and Machines 25 (3):291-296.
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  6. Peter Bøgh Andersen & Berit Holmqvist (1989). Interactive Fiction: Artificial Intelligence as a Mode of Sign Production. AI and Society 4 (4):291-313.
    Interactive media need their own idioms that exploit the characteristics of the computer based sign. The fact that the reader can physically influence the course of events in the system changes the author's role, since he no longer creates a linear text but anarrative space that the reader can use to generate stories. Although stories are not simulations of the real world, they must still contain recognizable parts where everyday constraints of time and space hold. AI-techniques can be used to (...)
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  7. 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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  8. Michael L. Anderson, Home Projects People Publications Links.
    However, there has also been growing interest in trying to create, and investigate the potential benefits of, intelligent systems which are themselves metacognitive. It is thought that systems that monitor themselves, and proactively respond to problems, can perform better, for longer, with less need for (expensive) human intervention. Thus has IBM widely publicized their "autonomic computing" initiative, aimed at developing computers which are (in their words) self-aware, selfconfiguring, self-optimizing, self-healing, self-protecting, and self-adapting. More ambitiously, it is hypothesized that metacognitive awareness (...)
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  9. Louise M. Antony (2002). How to Play the Flute: A Commentary on Dreyfus's “Intelligence Without Representation”. [REVIEW] Phenomenology and the Cognitive Sciences 1 (4):395-401.
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  11. Santiago Arango-Muñoz (2015). Joëlle Proust: The Philosophy of Metacognition: Mental Agency and Self-Awareness. Minds and Machines 25 (3):297-300.
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  12. Stuart Armstrong, Nick Bostrom & Carl Shulman (forthcoming). Racing to the Precipice: A Model of Artificial Intelligence Development. AI and Society.
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  13. Nasim Ashrafi & Mohammad Naghizadeh (forthcoming). Clarifying the Interaction Between Ideas and Architectural Works in the Achaemenid Era. AI and Society.
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  14. Babette Babich (forthcoming). Heidegger on Technology and Gelassenheit: Wabi-Sabi and the Art of Verfallenheit. AI and Society.
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  15. R. M. Baer (1967). Certain Directed Post Systems and Automata. Mathematical Logic Quarterly 13 (7‐12):151-174.
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  16. John Baker (unknown). Philosophy and Artificial Intelligence in Canada. Eidos: The Canadian Graduate Journal of Philosophy 12.
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  17. Dominic J. Balestra (1978). "Cybernetics and the Philosophy of Mind," by Kenneth Sayre. Modern Schoolman 55 (3):300-305.
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  18. S. Ball (1901). Richet, C. -L'Homme Et l'Intelligence. Mind 10:145.
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  19. Tirthnath Bandyopadhyay (1985). Man and Machine. Indian Philosophical Quarterly 12 (1):37.
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  20. 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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  21. John Barber (1999). Cybernetic Engines. Kairos 4 (1).
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  22. David Barker-Plummer, Turing Machines. Stanford Encyclopedia of Philosophy.
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  23. 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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  24. 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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  25. John A. Barry (1991). Technobabble. Monograph Collection (Matt - Pseudo).
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  26. Thomas C. Bartee (1991). Computer Architecture and Logic Design.
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  27. Archana Baruah (2006). Philosophy of Artificial Intelligence: A Heideggerian Approach. Philosophy Pathways 122.
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  28. Jon Barwise & John Etchemendy (1993). Turing's World 3.0 for the Macintosh an Introduction to Computability Theory.
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  29. Caroline Bassett (1999). Intelligence Inside? Radical Philosophy 93.
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  30. Gianfranco Basti (2013). Intelligence and Reference. In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. 139--159.
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  31. Bogdan Batrinca & Philip C. Treleaven (2015). Social Media Analytics: A Survey of Techniques, Tools and Platforms. AI and Society 30 (1):89-116.
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  32. Michael Baumann (2000). Robot Worlds. Complexity 5 (6):48-50.
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  33. R. J. Baxter (1972). On Unlimited Register Machines. Mathematical Logic Quarterly 18 (7):97-102.
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  34. Umut Baysan (2015). Realization Relations in Metaphysics. Minds and Machines (3):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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  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. Susanne Beck (forthcoming). The Problem of Ascribing Legal Responsibility in the Case of Robotics. AI and Society.
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  38. 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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  39. 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.
  40. 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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  41. 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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  42. Rebecca Bendick & Albert Borgmann (forthcoming). Explanation in Philosophy and the Limits of Precision. AI and Society.
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  43. Enoch Arnold Bennett (1908). The Human Machine.
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  44. Arnold Berleant (1968). V. Tejera's "Art and Human Intelligence". [REVIEW] Philosophy and Phenomenological Research 29 (2):307.
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  45. S. Bernardini (2006). Machine Readable Corpora. In Keith Brown (ed.), Encyclopedia of Language and Linguistics. Elsevier 358--375.
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  46. Jeremy Bernstein (1982). Science Observed Essays Out of My Mind /Jeremy Bernstein. --. --. Basic Books, C1982.
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  47. Jeremy Bernstein (1982). Science Observed Essays Out of My Mind.
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  48. 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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  49. 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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  50. Harsh Bhasin & Sushant Mehta (2016). On the Applicability of Diploid Genetic Algorithms. AI and Society:01-10.
    The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It is also (...)
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