The Two (Computational) Faces of AI

  • David Davenport
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 5)


There is no doubt that AI research has made significant progress, both in helping us understand how the human mind works and in constructing ever more sophisticated machines. But, for all this, its conceptual foundations remain remarkably unclear and even unsound. In this paper, I take a fresh look, first at the context in which agents must function and so how they must act, and second, at how it is possible for agents to communicate, store and recognise (sensory) messages. This analysis allows a principled distinction to be drawn between the symbolic and connectionist paradigms, showing them to be genuine design alternatives. Further consideration of the connectionist approach seems to offer a number of interesting clues as to how the human brain—apparently of the connectionist ilk—might actually work its incredible magic.


Input Pattern Symbol System Connectionist Approach Symbolic Approach Chinese Room 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bickhard, M.H., Terveen, L.: Foundational Issues in Artificial Intelligence and Cognitive Science: Impasse and Solution. Elsevier Scientific (1995)Google Scholar
  2. Chalmers, D.: On implementing a computation. Minds and Machines 4, 391–402 (1995)CrossRefGoogle Scholar
  3. Chalmers, D.J.: Subsymbolic computation and the chinese room. In: Dinsmore, J. (ed.) The Symbolic and Connectionist Paradigms: Closing the Gap, ch. 2, pp. 25–48. Lawrence Erlbaum Associates (1992)Google Scholar
  4. Chemero, A.: Radical Embodied Cognitive Science. MIT Press (2009)Google Scholar
  5. Davenport, D.: Inscriptors: Knowledge representation for cognition. In: Gun, L., Onvural, R., Gelenbe, E. (eds.) Proceedings of the 8th International Symposium on Computer and Information Science, Istanbul (1993)Google Scholar
  6. Davenport, D.: Intelligent systems: the weakest link? In: Kaynak, O., Honderd, G., Grant, E. (eds.) NATO ARW on “Intelligent Systems: Safety, Reliability and Maintainability Issues”, Kusadasi, 1992. Springer, Berlin (1993)Google Scholar
  7. Davenport, D.: Computationalism: The very idea. In: New Trends in Cognitive Science, Vienna (1999),; also published on MIT’s COGNET and in Conceptus Studien 14 (Fall 2000)
  8. Davenport, D.: Revisited: A computational account of the notion of truth. In: Vallverdu, J. (ed.) ECAP 2009, Proceedings of the 7th European Conference on Philosophy and Computing, Universitat Autonoma de Barcelona (2009)Google Scholar
  9. Dreyfus, H.: What Computers Can’t Do. MIT Press (1972)Google Scholar
  10. Dreyfus, H.L.: What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press (1992)Google Scholar
  11. Floridi, L.: The Philosophy of Information. Oxford University Press (2011)Google Scholar
  12. Fodor, J.: The Language of Thought. Harvard University Press, Cambridge (1975)Google Scholar
  13. Fodor, J.A., Pylyshyn, Z.W.: Connectionism and cognitive architecture: a critical analysis. Cognition 28(1-2), 3–71 (1988)CrossRefGoogle Scholar
  14. van Gelder, T.: What might cognition be if not computation? Journal of Philosophy 91, 345–381 (1995)CrossRefGoogle Scholar
  15. Gibson, J.: The Ecological Approach to Visual Perception. Houghton-Mifflin, Boston (1979)Google Scholar
  16. Ginsberg, M.: SIGART Bulletin 6(2) (1995)Google Scholar
  17. Harnad, S.: Grounding symbols in the analog world with neural nets. Think 2, 1–16 (1993)Google Scholar
  18. Haugeland, J.: Artificial Intelligence: The Very Idea. MIT Press (1985)Google Scholar
  19. McTear, M.: Understanding Cognitive Science. Horwood Ltd. (1988)Google Scholar
  20. Newell, A., Simon, H.: Computer science as empirical inquiry: Symbols and search. Communications of the ACM 19(3), 113–126 (1976)MathSciNetCrossRefGoogle Scholar
  21. Nilsson, N.J.: The Physical Symbol System Hypothesis: Status and Prospects. In: Lungarella, M., Iida, F., Bongard, J.C., Pfeifer, R. (eds.) 50 Years of AI. LNCS (LNAI), vol. 4850, pp. 9–17. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. Peng, Y., Reggia, J.: Abductive Inference Models for Diagnostic Problem Solving. Springer, New York (1990)zbMATHCrossRefGoogle Scholar
  23. Robbins, P., Aydede, M. (eds.): The Cambridge Handbook of Situated Cognition. Cambridge University Press (2009)Google Scholar
  24. Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press (1986)Google Scholar
  25. Searle, J.R.: Minds, brains, and programs. Behavioral and Brain Sciences 3(03), 417–424 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Computer Engineering Dept.Bilkent UniversityAnkaraTurkey

Personalised recommendations