Journal of Logic, Language and Information

, Volume 9, Issue 4, pp 447–466 | Cite as

Beyond the Turing Test

  • Jose Hernandez-Orallo


The main factor of intelligence is defined as the ability tocomprehend, formalising this ability with the help of new constructsbased on descriptional complexity. The result is a comprehension test,or C-test, which is exclusively defined in computational terms. Due toits absolute and non-anthropomorphic character, it is equally applicableto both humans and non-humans. Moreover, it correlates with classicalpsychometric tests, thus establishing the first firm connection betweeninformation theoretical notions and traditional IQ tests. The TuringTest is compared with the C-test and the combination of the two isquestioned. In consequence, the idea of using the Turing Test as apractical test of intelligence should be surpassed, and substituted bycomputational and factorial tests of different cognitive abilities, amuch more useful approach for artificial intelligence progress and formany other intriguing questions that present themselves beyond theTuring Test.

AI's anthropomorphism comprehension descriptional complexity inductive Inference measurement of intelligence psychometrics Turing test 


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  1. Angluin, D., 1988, “Queries and concept learning,” Machine Learning 2, 319–342.Google Scholar
  2. Barron, A., Rissanen, J., and Yu, B., 1998, “The minimum description length principle in coding and modeling,” IEEE Transactions on Information Theory 44, 2743–2760.Google Scholar
  3. Bien, Z., Kim Y.T., and Yang, S.H., 1998, “How to measure the machine intelligence quotient (MIQ): Two methods and applications,” pp. 03.15–03.22 in World Automation Congress (WAC), Albuquerque, NM: TSI Press.Google Scholar
  4. Blum, L. and Blum, M., 1975, “Towards a mathematical theory of inductive inference,” Information and Control 28, 125–155.Google Scholar
  5. Bochenski, J.M., 1965, The Methods of Contemporary Thought, Dordrecht: D. Reidel.Google Scholar
  6. Bradford P.G. and Wollowski, M., 1995, “A formalization of the Turing test (The Turing test as an interactive proof system),” SIGART Bulletin 6, 10.Google Scholar
  7. Chaitin, G.J., 1982, “Gödel”s theorem and information,” International Journal of Theoretical Physics 21, 941–954.Google Scholar
  8. Chandrasekaran, B., 1990, “What kind of information processing is intelligence?,” pp. 14–46 in Foundations of AI: A Source Book, D. Partridge and Y. Wilks, eds., Cambridge: Cambridge University Press.Google Scholar
  9. Evans, T.G., 1963, “A heuristic program to solve geometric analogy problems,” Ph.D. Thesis, MIT, Cambridge, MA. Also in Minsky, M., ed., 1968, Semantic Information Processing, Cambridge, MA: MIT Press.Google Scholar
  10. Eysenck, H.J., 1979, The Structure and Measurement of Intelligence, Berlin: Springer-Verlag.Google Scholar
  11. Fostel, G., 1993, “The Turing test is for the birds,” SIGART Bulletin 4, 7–8.Google Scholar
  12. Gammerman, A. and Vovk, V., eds., 1999, Special Issue on Kolmogorov Complexity, The Computer Journal 42.Google Scholar
  13. Gold, E.M., 1967, “Language identification in the limit,” Information & Control 10, 447–474.Google Scholar
  14. Harman, G., 1965, “The inference to the best explanation,” Philosophical Review 74, 88–95.Google Scholar
  15. Harnad, S., 1992, “The Turing test is not a trick: Turing indistinguishability is a scientific criterion,” SIGART Bulletin 3, 9–10.Google Scholar
  16. Herken, R., 1994, The Universal Turing Machine: A Half-Century Survey, 2nd edn., Oxford: Oxford University Press.Google Scholar
  17. Hernández-Orallo, J., 2000, “Constructive reinforcement learning,” International Journal of Intelligent Systems 15, 241–264.Google Scholar
  18. Hernández-Orallo, J. and García-Varea, I., 1998, “Explanatory and creative alternatives to the MDL principle,” pp. 17–19 in Proceedings of the International Conference on Model Based Reasoning (MBR”98), Pavia, 1998, S. Rini and G. Poletti, eds., University of Pavia, Italy. Also to appear in Foundations of Science.Google Scholar
  19. Hernández-Orallo, J. and Minaya-Collado, N., 1998, “A formal definition of intelligence based on an intensional variant of Kolmogorov complexity,” pp. 146–163 in Proceedings of the International Symposium of Engineers of Intelligent Systems (EIS”98), Tenerife, Spain. ICSC Academic Press.Google Scholar
  20. Hofstadter, D.R., 1979, Gödel, Escher, Bach. An Eternal Golden Braid, New York: Basic Books.Google Scholar
  21. Johnson, W.L., 1992, “Needed: A new test of intelligence,” SIGART Bulletin 3, 7–9.Google Scholar
  22. Kolmogorov, A.N., 1965, “Three approaches to the quantitative definition of information,” Problems Information Transmission 1, 1–7.Google Scholar
  23. Koppel, M., 1987, “Complexity, depth, and sophistication,” Complex Systems 1, 1087–1091.Google Scholar
  24. Larsson, J.E., 1993, “The Turing test misunderstood,” SIGART Bulletin 4, 10.Google Scholar
  25. Levin, L.A., 1973, “Universal search problems,” Problems Information Transmission 9, 265–266.Google Scholar
  26. Li, M. and Vitányi, P., 1997, An Introduction to Kolmogorov Complexity and Its Applications, 2nd edn., Berlin: Springer-Verlag.Google Scholar
  27. Marcus, G.F., Vijayan, S., Bandi Rao, S., and Vishton, P.M., 1998, “Rule learning by seven-monthold infants,” Science 283, 77–80.Google Scholar
  28. Millican, P.J.R. and Clark, A., eds., 1996, Machines and Thought. The Legacy of Alan Turing, Vol. I, Oxford: Clarendon Press.Google Scholar
  29. Neisser, U., Boodoo, G., Bouchard, T.J., Boykin, A.W., Brody, N., Ceci, S.J., Halpem, D.F., Lochlin, J.C., Perloff, R., Sternberg, R.J., and Urbina, S., 1996, “Intelligence: Knowns and unknowns,” American Psychologist 51, 77–101.Google Scholar
  30. Popper, K.R., 1962, Conjectures and Refutations: The Growth of Scientific Knowledge, New York: Basic Books.Google Scholar
  31. Preston, B., 1991, “AI, anthropocentrism, and the evolution of “intelligence”,” Minds and Machines 1, 259–277.Google Scholar
  32. Rissanen, J., 1996, “Fisher information and stochastic complexity,” IEEE Transactions on Information Theory IT-42, 40–47.Google Scholar
  33. Schnorr, C.P., 1973, “Process complexity and effective random tests,” Journal of Computer and Systems Sciences 7, 376–388.Google Scholar
  34. Shapiro, S.C., 1992, “The Turing test and The Economist,” SIGART Bulletin 3, 10–11.Google Scholar
  35. Shieber, S.M., 1994, “Lessons from a restricted Turing test,” Communications of the ACM 37, 70–78.Google Scholar
  36. Simon H. and Kotovsky, K., 1963, “Human acquisition of concepts for sequential patterns,” Psychological Review 70, 534–46.Google Scholar
  37. Solomonoff, R.J., 1957, “An inductive inference machine,” pp. 56–62 in IRE Convention Record, Section on Information Theory, Part 2, New York: Institute of Radio Engineers.Google Scholar
  38. Solomonoff, R.J., 1964, “A formal theory of inductive inference,” Information & Control 7, 1–22, March, 224–254, June.Google Scholar
  39. Solomonoff, R.J., 1978, “Complexity-based induction sytems: Comparisons and convergence theorems,” IEEE Transactions on Information Theory IT-24, 422–438.Google Scholar
  40. Solomonoff, R.J., 1997, “The discovery of algorithmic probability,” Journal of Computer and System Sciences 55, 73–88.Google Scholar
  41. Solomonoff, R.J., 1999, “Two kinds of probabilistic induction,” The Computer Journal 42, 256–259 (Special Issue on “Kolmogorov Complexity”).Google Scholar
  42. Spearman, C., 1904, ““General Intelligence” objectively determined and measured,” American Journal of Psychology 15, 201–293.Google Scholar
  43. Sternberg, R.J., 1977, Intelligence, Information Processing, and Analogical Reasoning, NewYork: John Wiley & Sons.Google Scholar
  44. Sternberg, R.J. and Detterman, D.K., 1986, What is Intelligence? Contemporary Viewpoints on Its Nature and Definition, Norwood, NJ: Ablex.Google Scholar
  45. Stonier, T., 1992, Beyond Information. The Natural History of Intelligence, Berlin: Springer-Verlag.Google Scholar
  46. Suttner, C.B. and Sutcliffe, G., 1998, “The TPTP problem library: CNF release v1.2.1,” Journal of Automated Reasoning 21, 177–203.Google Scholar
  47. Thagard, P., 1989, “Explanatory coherence,” Behavioural and Brain Sciences 12, 435–502.Google Scholar
  48. The Economist (Editorial), 1992, “Artificial stupidity,” The Economist, 324, no. 7770, August 1, p. 14.Google Scholar
  49. Turing, A.M., 1936, “On computable numbers with an application to the Entscheidungsproblem,” Proceedings London Mathematical Society, Series 2 42, 230–265. Correction, 1937, Ibid. 43, 544–546.Google Scholar
  50. Turing, A.M., 1950, “Computing machinery and intelligence,” Mind 59, 433–460.Google Scholar
  51. Valiant, L., 1984, “A theory of the learnable,” Communications of the ACM 27, 1134–1142.Google Scholar
  52. Vitányi, P. and Li, M., 1997, “On prediction by data compression,” pp. 14–30 in Proceedings 9th European Conference on Machine Learning, M. van Someren and G. Widmer, eds., LNAI, Vol. 1224, Berlin: Springer-Verlag.Google Scholar
  53. Watanabe, S., 1972, “Pattern recognition as information compression,” pp. 31–60 in Frontiers of Pattern Recognition, S. Watanabe, ed., New York: Academic Press.Google Scholar
  54. Zvonkin, A.K. and Levin, L.A., 1970, “The complexity of finite objects and the development of the concepts of information and randomness by means of the Theory of Algorithms,” Russian Mathematical Surveys 25, 83–124.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Jose Hernandez-Orallo
    • 1
  1. 1.Departament de Sistemes Informàtics i ComputacióUniversitat Politècnica de ValènciaValènciaSpain

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