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Can Machine Intelligence be Measured in the Same Way as Human intelligence?


In recent years the number of research projects on computer programs solving human intelligence problems in artificial intelligence (AI), artificial general intelligence, as well as in Cognitive Modelling, has significantly grown. One reason could be the interest of such problems as benchmarks for AI algorithms. Another, more fundamental, motivation behind this area of research might be the (implicit) assumption that a computer program that successfully can solve human intelligence problems has human-level intelligence and vice versa. This paper analyses this assumption.

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Fig. 1


  1. CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are tests (e.g., distorted letters) to detect bots in Internet applications.


  1. Amthauer R, Brocke B, Liepmann D, Beauducel A (1999) Intelligenz-Struktur-Test 2000 (I-S-T 2000). Hogrefe, Göttingen

    Google Scholar 

  2. Bartholomew DJ (2004) Measuring intelligence: facts and fallacies. Cambridge University Press, Cambridge

    Book  Google Scholar 

  3. Besold TR (2013a) Human-level artificial intelligence must be a science. In: Kühnberger K-U, Rudolph S, Wang P (eds) Artificial general intelligence, vol 7999 LNCS. Springer, Berlin, pp 174–177

  4. Besold TR (2013b) Turing revisited: a cognitively-inspired decomposition. In: Müller VC (ed) Philosophy and theory of artificial intelligence, SAPERE 5. Springer, Berlin, pp 121–132

    Chapter  Google Scholar 

  5. Besold TR (2014) A note on chances and limitations of psychometric AI. In KI 2014: Advances in artificial intelligence. Springer, Berlin, pp 49–54

  6. Bors DA, Vigneau F (2001) The effect of practice on raven’s advanced progressive matrices. Learn Individ Differ 13(4):291–312

    Article  Google Scholar 

  7. Bringsjord S (2011) Psychometric artificial intelligence. J Exp Theor Artif Intell 23(3):271–277

    Article  Google Scholar 

  8. Bringsjord S, Schimanski B (2003) What is artificial intelligence? Psychometric AI as an answer. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI’03), Morgan Kaufmann, pp 887–893

  9. Burghardt J (2005) E-generalization using grammars. Artif Intell 165:1–35

    MathSciNet  Article  MATH  Google Scholar 

  10. Cooper R, Fox J, Farringdon J, Shallice T (1996) A systematic methodology for cognitive modelling. Artif Intell 83:3–44

    Article  Google Scholar 

  11. Deary IJ, Der G, Ford G (2001) Reaction times and intelligence differences: a population-based cohort study. Intelligence 29(5):389–399

    Article  Google Scholar 

  12. Detterman D (2011) A challenge to Watson. Intelligence 39(2–3):77–78

    Article  Google Scholar 

  13. Dowe DL, Hernández-Orallo J (2012) IQ tests are not for machines, yet. Intelligence 40(2):77–81

    Article  Google Scholar 

  14. Harnad S (1990) The symbol grounding problem. Physica D 42:335–346

    Article  Google Scholar 

  15. Hernández-Orallo J (2000) Beyond the Turing test. J Log Lang Inf 9(4):447–466

    Article  MATH  Google Scholar 

  16. Hernández-Orallo J, Dowe DL, Hernández-Lloreda MV (2014) Universal psychometrics: measuring cognitive abilities in the machine kingdom. Cogn Syst Res 27:50–74

    Article  Google Scholar 

  17. Hofmann J, Kitzelmann E, Schmid U (2014) Applying inductive program synthesis to induction of number series a case study with IGOR2. In KI 2014: Advances in artificial intelligence, Springer, Berlin, pp 25–36

  18. Johnson-Laird PN (1988) The computer and the mind: an introduction to cognitive science. Fontana Press, London

    Google Scholar 

  19. Kühnberger KU, Hitzler P (2009) Facets of artificial general intelligence. KI 23(2):58–59

    Google Scholar 

  20. Legg S, Hutter M (2007) Universal intelligence: a definition of machine intelligence. Minds Mach 17(4):391–444

    Article  Google Scholar 

  21. Lovett A, Forbus K, Usher J (2010) A structure-mapping model of Raven’s Progressive Matrices. In: Proceedings of CogSci-10, pp 2761–2766

  22. Miller M (1999) The savant syndrome: intellectual impairment and exceptional skill. Psychol Bull 125(1):31–46

    Article  Google Scholar 

  23. Mueller ST, Jones M, Minnery BS, Hiland JMH (2007) The BICA cognitive decathlon: a test suite for biologically-inspired cognitive agents. In: Proceedings of behavior representation in modeling and simulation conference, Norfolk

  24. Newell A (1980) Physical symbol systems. Cogn Sci 4:135–183

    Article  Google Scholar 

  25. Newell A (1982) The knowledge level. Artif Intell 18:87–127

    Article  Google Scholar 

  26. Pylyshyn Z (1980) Computation and cognition: issues in the foundation of cognitive science. Behav Brain Sci 3:111–132

    Article  Google Scholar 

  27. Sanghi P, Dowe DL (2003) A computer program capable of passing I.Q. tests. In: Slezak PP (ed) Proceedings of ICCS/ASCS-2003, Sydney, AU, pp 570–575

  28. Siebers M, Schmid U (2012) Semi-analytic natural number series induction. In KI 2012: Advances in artificial intelligence, Springer, Berlin, pp 249–252

  29. Spearman C (1904) General intelligence objectively determined and measured. Am J Psychol 15:201293

    Google Scholar 

  30. Sternberg RJ (ed) (2000) Handbook of intelligence. Cambridge University Press, Cambridge

  31. Strannegård C, Amirghasemi M, Ulfsbäcker S (2013a) An anthropomorphic method for number sequence problems. Cogn Syst Res 22–23:27–34

    Article  Google Scholar 

  32. Strannegård C, Cirillo S, Ström V (2013b) An anthropomorphic method for progressive matrix problems. Cogn Syst Res 22–23:35–46

    Article  Google Scholar 

  33. Turing AM (1950) Computing machinery and intelligence. Mind 59:433–460

    MathSciNet  Article  Google Scholar 

  34. Turing AM (1969) Intelligent machinery. In: Meltzer B, Michie D (eds) Machine intelligence, vol 5. Edinburgh University Press, Edinburgh, pp 3–23

    Google Scholar 

  35. Von Ahn L, Blum M, Hopper NJ, Langford J (2003) CAPTCHA: using hard AI problems for security. In: Advances in cryptology—EUROCRYPT 2003, Springer, pp 294–311

  36. Wechsler D (1944) The measurement of adult intelligence. Williams & Wilkins, Baltimore

    Book  Google Scholar 

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Correspondence to Ute Schmid.

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Besold, T., Hernández-Orallo, J. & Schmid, U. Can Machine Intelligence be Measured in the Same Way as Human intelligence?. Künstl Intell 29, 291–297 (2015).

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  • Intelligence tests
  • Strong AI
  • Psychometric AI
  • Cognitive modelling