Beyond the Turing Test
 Jose HernandezOrallo
 … show all 1 hide
Purchase on Springer.com
$39.95 / €34.95 / £29.95*
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Abstract
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 Ctest, which is exclusively defined in computational terms. Due toits absolute and nonanthropomorphic character, it is equally applicableto both humans and nonhumans. 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 Ctest 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.
 Angluin, D., 1988, “Queries and concept learning,” Machine Learning 2, 319–342.
 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.
 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.
 Blum, L. and Blum, M., 1975, “Towards a mathematical theory of inductive inference,” Information and Control 28, 125–155.
 Bochenski, J.M., 1965, The Methods of Contemporary Thought, Dordrecht: D. Reidel.
 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.
 Chaitin, G.J., 1982, “Gödel”s theorem and information,” International Journal of Theoretical Physics 21, 941–954.
 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.
 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.
 Eysenck, H.J., 1979, The Structure and Measurement of Intelligence, Berlin: SpringerVerlag.
 Fostel, G., 1993, “The Turing test is for the birds,” SIGART Bulletin 4, 7–8.
 Gammerman, A. and Vovk, V., eds., 1999, Special Issue on Kolmogorov Complexity, The Computer Journal 42.
 Gold, E.M., 1967, “Language identification in the limit,” Information & Control 10, 447–474.
 Harman, G., 1965, “The inference to the best explanation,” Philosophical Review 74, 88–95.
 Harnad, S., 1992, “The Turing test is not a trick: Turing indistinguishability is a scientific criterion,” SIGART Bulletin 3, 9–10.
 Herken, R., 1994, The Universal Turing Machine: A HalfCentury Survey, 2nd edn., Oxford: Oxford University Press.
 HernándezOrallo, J., 2000, “Constructive reinforcement learning,” International Journal of Intelligent Systems 15, 241–264.
 HernándezOrallo, J. and GarcíaVarea, 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.
 HernándezOrallo, J. and MinayaCollado, 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.
 Hofstadter, D.R., 1979, Gödel, Escher, Bach. An Eternal Golden Braid, New York: Basic Books.
 Johnson, W.L., 1992, “Needed: A new test of intelligence,” SIGART Bulletin 3, 7–9.
 Kolmogorov, A.N., 1965, “Three approaches to the quantitative definition of information,” Problems Information Transmission 1, 1–7.
 Koppel, M., 1987, “Complexity, depth, and sophistication,” Complex Systems 1, 1087–1091.
 Larsson, J.E., 1993, “The Turing test misunderstood,” SIGART Bulletin 4, 10.
 Levin, L.A., 1973, “Universal search problems,” Problems Information Transmission 9, 265–266.
 Li, M. and Vitányi, P., 1997, An Introduction to Kolmogorov Complexity and Its Applications, 2nd edn., Berlin: SpringerVerlag.
 Marcus, G.F., Vijayan, S., Bandi Rao, S., and Vishton, P.M., 1998, “Rule learning by sevenmonthold infants,” Science 283, 77–80.
 Millican, P.J.R. and Clark, A., eds., 1996, Machines and Thought. The Legacy of Alan Turing, Vol. I, Oxford: Clarendon Press.
 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.
 Popper, K.R., 1962, Conjectures and Refutations: The Growth of Scientific Knowledge, New York: Basic Books.
 Preston, B., 1991, “AI, anthropocentrism, and the evolution of “intelligence”,” Minds and Machines 1, 259–277.
 Rissanen, J., 1996, “Fisher information and stochastic complexity,” IEEE Transactions on Information Theory IT42, 40–47.
 Schnorr, C.P., 1973, “Process complexity and effective random tests,” Journal of Computer and Systems Sciences 7, 376–388.
 Shapiro, S.C., 1992, “The Turing test and The Economist,” SIGART Bulletin 3, 10–11.
 Shieber, S.M., 1994, “Lessons from a restricted Turing test,” Communications of the ACM 37, 70–78.
 Simon H. and Kotovsky, K., 1963, “Human acquisition of concepts for sequential patterns,” Psychological Review 70, 534–46.
 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.
 Solomonoff, R.J., 1964, “A formal theory of inductive inference,” Information & Control 7, 1–22, March, 224–254, June.
 Solomonoff, R.J., 1978, “Complexitybased induction sytems: Comparisons and convergence theorems,” IEEE Transactions on Information Theory IT24, 422–438.
 Solomonoff, R.J., 1997, “The discovery of algorithmic probability,” Journal of Computer and System Sciences 55, 73–88.
 Solomonoff, R.J., 1999, “Two kinds of probabilistic induction,” The Computer Journal 42, 256–259 (Special Issue on “Kolmogorov Complexity”).
 Spearman, C., 1904, ““General Intelligence” objectively determined and measured,” American Journal of Psychology 15, 201–293.
 Sternberg, R.J., 1977, Intelligence, Information Processing, and Analogical Reasoning, NewYork: John Wiley & Sons.
 Sternberg, R.J. and Detterman, D.K., 1986, What is Intelligence? Contemporary Viewpoints on Its Nature and Definition, Norwood, NJ: Ablex.
 Stonier, T., 1992, Beyond Information. The Natural History of Intelligence, Berlin: SpringerVerlag.
 Suttner, C.B. and Sutcliffe, G., 1998, “The TPTP problem library: CNF release v1.2.1,” Journal of Automated Reasoning 21, 177–203.
 Thagard, P., 1989, “Explanatory coherence,” Behavioural and Brain Sciences 12, 435–502.
 The Economist (Editorial), 1992, “Artificial stupidity,” The Economist, 324, no. 7770, August 1, p. 14.
 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.
 Turing, A.M., 1950, “Computing machinery and intelligence,” Mind 59, 433–460.
 Valiant, L., 1984, “A theory of the learnable,” Communications of the ACM 27, 1134–1142.
 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: SpringerVerlag.
 Watanabe, S., 1972, “Pattern recognition as information compression,” pp. 31–60 in Frontiers of Pattern Recognition, S. Watanabe, ed., New York: Academic Press.
 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.
 Title
 Beyond the Turing Test
 Journal

Journal of Logic, Language and Information
Volume 9, Issue 4 , pp 447466
 Cover Date
 20001001
 DOI
 10.1023/A:1008367325700
 Print ISSN
 09258531
 Online ISSN
 15729583
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

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

 Jose HernandezOrallo ^{(1)}
 Author Affiliations

 1. Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera, s/n, E46022, València, Spain