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Annals of Computer Science and Information Systems, Volume 5

Proceedings of the 2015 Federated Conference on Computer Science and Information Systems

The Cognitive Cycle

John F. Sowa

DOI: http://dx.doi.org/10.15439/2015F003

Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 11–16 (2015)

Full text

Abstract. n the twenty years from first grade to a PhD, students never learn any subject by the methods for which machine-learning algorithms have been designed. Those algorithms are useful for analyzing large volumes of data. But they don't enable a computer system to learn a language as quickly and accurately as a three-year-old child. They're not even as effective as a mother raccoon teaching her babies how to find the best garbage cans. For all animals, learning is integrated with the cognitive cycle from perception to purposeful action. Many algorithms are needed to support that cycle. But an intelligent system must be more than a collection of algorithms. It must integrate them in a cognitive cycle of perception, learning, reasoning, and action. That cycle is key to designing intelligent systems.

References

  1. Albus, James S. (2010) A model of computation and representation in the brain, Information Sciences 180, 1519–1554. http://www.james-albus.org/docs/ModelofComputation.pdf
  2. Breiman, Leo (2001) Statistical Modeling: The Two Cultures, Statistical Science 16:3, 199-231.
  3. Carbonell, Jaime G., Ryszard S. Michalski, & Tom M. Mitchell (1983) An overview of machine learning, in Michalski, Carbonell, & Mitchell, Machine Learning, Palo Alto: Tioga, pp. 3-23.
  4. Genesereth, Michael, Nathaniel Love, & Barney Pell (2005) General Game Playing: Overview of the AAAI Competition, AI Magazine 26:2, 62-72.
  5. Herbart, Johann Friedrich (1816) A Textbook in Psychology, translated by M. K. Smith, New York: Appleton, 1891.
  6. Hinton, Geoffrey E. (2009) Deep belief networks, Scholarpedia, 4(5):5947, http://www.scholarpedia.org/article/Deep_belief_networks
  7. Jaitly, Navdeep (2014) Exploring deep learning methods for discovering features in speech recognition, PhD Dissertation, University of Toronto. http://www.cs.toronto.edu/~ndjaitly/Jaitly_Navdeep_201411_PhD_thesis.pdf
  8. Lamb, Sydney M. (1999) Pathways of the Brain: The Neurocognitive Basis of Language, Amsterdam: John Benjamins.
  9. MacCartney, Bill, & Christopher D. Manning (2014) Natural logic and natural language inference, in Harry Bunt et al., Computing Meaning 4, Berlin: Springer, pp. 129–147.
  10. Majumdar, Arun K., & John F. Sowa (2009) Two paradigms are better than one and multiple paradigms are even better, in S. Rudolph, F. Dau, and S.O. Kuznetsov, eds., Proceedings of ICCS'09, LNAI 5662, Springer, pp. 32-47. http://www.jfsowa.com/pubs/paradigm.pdf
  11. Marcus, Gary F. (2012) Is “deep learning” a revolution in artificial intelligence? New Yorker, 25 November 2012. http://www.Newyorker.com/news/news-desk/is-deep-learning-a-revolution-in-artificial-intelligence
  12. Mason, Robert A., & Marcel Adam Just (2015) Physics instruction induces changes in neural knowledge representation during successive stages of learning, NeuroImage 111, 36-48.
  13. McCulloch, Warren S., & Walter Pitts (1943) A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics 5, 115-133.
  14. Minsky, Marvin (1986) The Society of Mind, New York: Simon & Schuster, Section 30.8.
  15. Mnih, V., K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmille (2013) Playing Atari with deep reinforcement learning, NIPS Deep Learning Workshop, https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
  16. Ohlsson, Stellan (2011) Deep Learning: How the Mind Overrides Experience, Cambridge: University Press.
  17. Peirce, Charles Sanders (1903) Pragmatism as a Principle and Method of Right Thinking, The 1903 Lectures on Pragmatism, ed. by P. A. Turrisi, SUNY Press, Albany, 1997. Also in [18], pp. 131-241.
  18. Peirce, Charles Sanders (EP) The Essential Peirce, ed. by N. Houser, C. Kloesel, and members of the Peirce Edition Project, 2 vols., Indiana University Press, Bloomington, 1991-1998. Peirce, Charles Sanders (CP) Collected Papers of C. S. Peirce, ed. by C. Hartshorne, P. Weiss, & A. Burks, 8 vols., Cambridge, MA: Harvard University Press, 1931-1958.
  19. Piaget, Jean (1968) On the Development of Memory and Identity, Barre, MA: Clark University Press.
  20. Quillian, M. Ross (1968) Semantic memory, in M. Minsky, Semantic Information Processing, Cambridge, MA: MIT Press, pp. 227-270.
  21. Regalado, Antonio (2014) Is Google cornering the market on deep learning? Technology Review, 29 January 2014. http://www.technologyreview.com/news/524026/is-google-cornering-the-market-on-deep-learning/
  22. Rosenblatt, Frank (1958) The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review 65:6, 386-408.
  23. Rumelhart, David E., & Donald A. Norman (1978) Accretion, tuning, and restructuring: three modes of learning, in J. W. Cotton & R. L. Klatzky, eds., Semantic Factors in Cognition, Hillsdale, NJ: Lawrence Erlbaum, pp. 37-54.
  24. Samuel, Arthur L. (1959) Some studies in machine learning using the game of checkers, IBM Journal of Research and Development 3, 211- 229.
  25. Socher, Richard, Cliff Chiung-Yu Lin, Andrew Y. Ng, & Christopher D. Manning (2011) Parsing natural scenes and natural language with recursive neural networks, ICML 2011. http://www.socher.org/uploads/Main/SocherLinNgManning_ICML2011.pdf
  26. Sowa, John F. (1992) Semantic networks, Encyclopedia of Artificial Intelligence, Second Edition, edited by S. C. Shapiro, Wiley, New York. Updated version at http://www.jfsowa.com/pubs/semnet.pdf
  27. Sowa, John F. (2006) The challenge of knowledge soup, in J. Ramadas & S. Chunawala, Research Trends in Science, Technology, and Mathematics Education, Mumbai: Homi Bhabha Centre, pp. 55-90. http://www.jfsowa.com/pubs/challenge.pdf
  28. Sowa, John F. (2015) Peirce, Polya, and Euclid: integrating logic, heuristics, and geometry, lecture presented at the American Philosophical Association conference, Vancouver, April 2015. Slides at http://www.jfsowa.com/talks/ppe.pdf
  29. Thorndike, Edward Lee (1932) The Fundamentals of Learning, New York: Teachers College Press.
  30. Watkins, Christopher J.C.H., & Peter Dayan (1992) Q-learning, Machine Learning 8:3-4, 279–292.
  31. Watson, Robert I. (1963) The Great Psychologists from Aristotle to Freud, New York: Lippencot, pp. 209-210.
  32. Whitehead, Alfred North (1933) Adventures of Ideas, New York: Macmillan.
  33. Wilk, Martin, as quoted by John W. Tukey (1962) The future of data analysis, Annals of Mathematical Statistics 33:1, 1-67.
  34. Zadeh, Lotfi A. (1965) Fuzzy sets, Information and Control 8, 338-353.