Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T13:22:55.828Z Has data issue: false hasContentIssue false
  • English
  • Français

The fork in the road

Published online by Cambridge University Press:  10 November 2017

Robert J. Sternberg
Robert J. Sternberg*
Affiliation:
Department of Human Development, College of Human Ecology, Cornell University, Ithaca, NY 14853. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Machines that learn and think like people should simulate how people really think in their everyday lives. The field of artificial intelligence originally traveled down two roads, one of which emphasized abstract, idealized, rational thinking and the other, which emphasized the emotionally charged and motivationally complex situations in which people often find themselves. The roads should have converged but never did. That's too bad.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e278
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abelson, R. P. & Carroll, J. D. (1965) Computer simulation of individual belief systems. The American behavioral scientist (pre-1986) 8(9):24–30.CrossRefGoogle Scholar
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C. & Qin, Y. (2004) An integrated theory of the mind. Psychological Review 111:1036–60.CrossRefGoogle ScholarPubMed
Colby, K. M. (1975) Artificial paranoia: Computer simulation of paranoid processes. Pergamon.Google Scholar
Feigenbaum, E. & Feldman, J., eds. (1995) Computers and thought. AAAI Press.Google Scholar
Mayer, J. D. & Salovey, P. (1993) The intelligence of emotional intelligence. Intelligence 17:442–43.CrossRefGoogle Scholar
Minsky, M. (2003) Semantic information processing. MIT Press.Google Scholar
Minsky, M. & Papert, S. A. (1987) Perceptrons: An introduction to computational geometry, expanded edn. MIT Press.Google Scholar
Newell, A., Shaw, J. C. & Simon, H. A. (1957) Problem solving in humans and computers. Carnegie Technical 21(4):34–38.Google Scholar
Stanovich, K. E. (2009) What intelligence tests miss: The psychology of rational thought. Yale University Press.Google Scholar
Sternberg, R. J. (1997) What does it mean to be smart? Educational Leadership 54(6):20–24.Google Scholar
Sternberg, R. J., ed. (2002) Why smart people can be so stupid. Yale University Press.Google Scholar
Sternberg, R. J. & Jordan, J., eds. (2005) Handbook of wisdom: Psychological perspectives. Cambridge University Press.CrossRefGoogle Scholar
Weizenbaum, J. (1966) ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM 9(1):36–45.CrossRefGoogle Scholar
Winograd, T. (1972) Understanding natural language. Cognitive Psychology 3:1–191.CrossRefGoogle Scholar