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Artificial Intelligence, Artificial Life, and the Symbol-Matter Problem

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Part of the book series: Boston Studies in the Philosophy of Science ((BSPS,volume 178))

Abstract

What is the relation between matter and form? This question is of course as old as philosophy itself. But it also arises at the foundations of two recent scientific endeavours — the computational approach to the mind-brain in cognitive science and artificial intelligence (AI), and the synthetic approach to living systems in theoretical biology and artificial life (AL). In these fields the question arises primarily in connection with the status of symbols, that is, items that are physically realized, formally identified, and semantically interpretable.

Earlier versions of this essay were presented to a symposium on Connectionism at the 1992 meeting of the Canadian Philosophical Association and to the Department of Philosophy at the University of Toronto. The thoughts expressed here have also benefited greatly from conversations with Kenneth Cheung, Ronald de Sousa, Paul Thompson, Sonia Sedivy, and Francisco Varela

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Notes

  1. See Stephen Harnad, ‘The Symbol Grounding Problem’, in Stephanie Forrest, ed., Emergent Computation (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1991), pp. 335–346.

    Google Scholar 

  2. See H. H. Pattee, ‘Simulations, Realizations, and Theories of Life’, in Christopher G. Langton, ed., Artificial Life. Santa Fe Studies in the Sciences of Complexity Volume VI (Redwood City, CA: Addison-Wesley, 1989), pp. 63–75.

    Google Scholar 

  3. The term ‘Strong AI’ was coined by John Searle who opposes the view. See his ‘Minds, Brains, and Programs’, Behavioral and Brain Sciences 3 (1980): 417–458.

    Article  Google Scholar 

  4. See Langton ‘Artificial Life’ op cit. Harold C. Morris, ‘On the Feasibility of Computational Artificial Life: A Reply to Critics’, in Jean Arcady Meyer and Stewart Wilson, eds., From Animals to Animals (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1991), pp. 40–49. See also Elliot Sober, ‘Learning from Functionalism - Prospects for Strong Artificial Life’, in Christopher G. Langton, Charles Taylor, J. Doyne Farmer, and Steen Rasumussen, eds., Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity, Proceedings Volume X (Redwood City, CA: Addison-Wesley, 1992), pp. 749–765.

    Google Scholar 

  5. John Searle, ‘Is the Brain a Digital Computer?’, Proceedings and Addresses of the American Philosophical Association 64 (1990): 21–37, and The Rediscovery of the Mind (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1992), Chapter 9.

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  6. Searle uses the term “digital computer” but I think “physical symbol system” is a more precise designation for the kind of computational system he has in mind. See Alan Newell, ‘Physical Symbol Systems’, Cognitive Science 4 (1980): 135–183.

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  7. See John Searle, Intentionality. An Essay in the Philosophy of Mind (Cambridge: Cambridge University Press, 1983), p. 20.

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  8. See John Haugeland’s discussion of this point in his Artificial Intelligence: The Very Idea (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1985), pp. 96, 119–123.

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  9. Jerry Fodor and Zenon Pylyshyn, ‘Connectionism and Cognitive Architecture: A Critical Review’ Cognition 28(1988): 3–71. Page references are to the article as reprinted in Steven Pinker and Jacques Mehler, eds., Connections and Symbols (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1989)

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  10. Zenon Pylyshyn, Computation and Cognition. Toward a Foundation for Cognitive Science (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1984), pp. 54–59.

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  11. See ibid., pp. 57–58, and Jerry Fodor, Representations. Philosophical Essays on the Foundations of Cognitive Science (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1989). pp. 22–23.

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  12. Ray Jackendoff, Consciousness and the Computational Mind (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1987), pp. 29–36.

    Google Scholar 

  13. W. S. McCulloch and W. Pitts, ‘A Logical Calculus of the Ideas Immanent in Nervous Activity’, reprinted in W. S. McCulloch, Embodiments of Mind (Cambridge, Massachusetts: The MIT Press, 1965).

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  14. For different statements of this point, see Humberto R. Maturana and Francisco J. Varela, Autopoiesis and Cognition: The Realization of the Living. Boston Studies in the Philosophy of Science, Volume 43 (Dordrecht and Boston: D. Reidel, 1980), pp. 19, 124–134; Walter Freeman and Christine Skarda, ‘Spatial EEG Patterns, Nonlinear Dynamics, and Perception: The Neo- Sherringtonian View’, Brain Research Reviews 10 (1985): 145–175; Gerald Edelman, Neural Darwinism (New York: Basic Books, 1987); and Andreas K. Engel, Peter König, Andreas K. Kreiter, Thomas B. Schillen, and Wolf Singer, ‘Temporal Coding in the Visual Cortex: New Vistas on Integration in the Nervous System’, Trends in Neuroscience 15, No. 6 (1992): 218–226.

    Google Scholar 

  15. Paul Smolensky, ‘On the Proper Treatment of Connectionism’, Behavioral and Brain Sciences 11 (1988): 1–74.

    Article  Google Scholar 

  16. See Martin Gardner, ‘Mathematical Games: The Fantastic Combinations of John Conway’s New Solitaire Game of “Life”’, Scientific American 224, No. 4 (1970): 120–123.

    Article  Google Scholar 

  17. John Maynard Smith, The Problems of Biology (Oxford: Oxford University Press, 1986), p.19

    Google Scholar 

  18. H. H. Pattee, ‘Dynamic and Linguistic Modes of Complex Systems’, International Journal of General Systems Theory 3 (1977): 259–266.

    Article  Google Scholar 

  19. John Von Neumann, The Theory of Self Reproducing Automata, ed. A. Burks (Urbana, Illinois: University of Illinois Press, 1966).

    Google Scholar 

  20. For further discussion of this important point see Susan Oyama, The Ontogeny of Information: Developmental Systems and Evolution (Cambridge: Cambridge University Press, 1985).

    Google Scholar 

  21. F. J. Varela, H. Maturana, and R. Uribe, ‘Autopoiesis: The Organization of Living Systems, its Characterization and a Model’, Biosystems 5 (1974): 187–195, and Humberto R. Maturana and Francisco J. Varela, Autopoiesis and Cognition, op. cit.

    Article  Google Scholar 

  22. Francisco J. Varela, Principles of Biological Autonomy (New Jersey: Elsevier North Holland, (1979), p. 75.

    Google Scholar 

  23. See Ned Block, ‘Introduction: What is Functionalism?’, in Ned Block, ed., Readings in the Philosophy of Psychology. Volume One (Cambridge, Massachusetts: Harvard University Press, (1980), pp. 171–184.

    Google Scholar 

  24. Claus Emmeche, ‘Life as an Abstract Phenomenon. Is Artificial Life Possible?’, in Francisco J. Varela and Paul Bourgine, eds., Toward a Practice of Autonomous Systems. Proceedings of the First European Conference on Artificial Life (Cambridge, Massachusetts: The MIT Press/ A Bradford Book, 1992), pp. 466–474.

    Google Scholar 

  25. See David E. Rummelhart, ‘Brain Style Computation: Learning and Generalization’, in Steven F. Zornetzer, Joel L. Davis, and Clifford Lau, eds., An Introduction to Neural and Electronic Networks (San Diego: Academic Press, 1990), pp. 405–420.

    Google Scholar 

  26. See the article by Carpenter and Grossberg cited in the previous note. In addition see Stephen Grossberg, ‘How Does a Brain Build a Cognitive Code?’, Psychological Review 87 (1980): 1–55, and ‘Competitive Learning: From Interactive Activation to Adaptive Resonance’, Cognitive Science 11 (1987): 23–63.

    Article  Google Scholar 

  27. Alan Newell and Herbert A. Simon, ‘Computer Science as Empirical Inquiry: Symbols and Search’, reprinted in John Haugeland, ed., Mind Design (Cambridge, Massachusetts: The MIT Press/A Bradford Book, 1981).

    Google Scholar 

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© 1996 Kluwer Academic Publishers

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Thompson, E. (1996). Artificial Intelligence, Artificial Life, and the Symbol-Matter Problem. In: Marion, M., Cohen, R.S. (eds) Québec Studies in the Philosophy of Science. Boston Studies in the Philosophy of Science, vol 178. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0113-1_5

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  • DOI: https://doi.org/10.1007/978-94-009-0113-1_5

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