Abstract
► Chapter 1 presented the philosophical, mathematical, and engineering ideas that led to the creation of the artificial intelligence discipline. ► Chapter 1 also asked whether actions considered truly intelligent could be produced by a machine. Alan Turing’s (1950) test in the journal Mind was proposed as a possible answer to that question. ► Chapter 1 concluded by presenting the proposal for the summer workshop at Dartmouth College in 1956 to address Turing’s project of creating intelligent machines. ► Chapter 2 begins with a discussion of definitions for AI. The chapter then asks how various complex world situations might be represented on a computer. Finally, ► Chapter 2 introduces the primary research and application paradigms of current AI.
We are just an advanced breed of monkeys on a minor planet of a very average star. But we can understand the universe. That makes us something very special.
STEPHEN HAWKING
Our age of anxiety is, in a great part, the result of trying to do today’s jobs with yesterday’s tools…
MARSHALL MCLUHAN
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Bayes, T. 1763. Essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London. London: The Royal Society, pp. 370–418.
Bledsoe, W.W. and Browning, I. 1959. Pattern recognition and reading by machine. Proceedings of the eastern joint computer conference. New York: IEEE Computer Society.
Collins A. and Quillian, M.R. 1969. Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8: 240–247.
Davis, K.H., Biddulph, R., & Balashek, S. 1952. Automatic recognition of spoken digits. Journal of the Acoustical Society of America, 24(6): 637–642.
Dreyfus, H., 1972. What computers can’t do: The limits of artificial intelligence. New York: Harper and Row.
Dreyfus, H., 1992. What computers still can’t do: A critique of artificial reason. Cambridge MA: MIT Press.
Gelernter, H. and Rochester N. 1958. Intelligent behavior in problem-solving machines. IBM Journal of Research and Development, 2(4): 336–345.
Haugeland, J. 1985. Artificial intelligence: The very idea. Cambridge/Bradford, MA: MIT Press.
Hinton, G.E., & Sejnowski, T.J. 1983. Analyzing cooperative computation. In Proceedings of the 5th Annual Congress of the Cognitive Science Society, Rochester, New York.
Hinton, G.E., Osindero, S., Teh, Y.W. 2006. A fast learning algorithm for deep belief nets. Neural Computation 18 (7): 1527–1554.
Holland, J.H. 1975. Adaptation in natural and artificial systems. Michigan, University of Michigan Press.
Jurasky, D., & Martin, J.H. 2020. Speech and language processing, 3rd ed, Upper Saddle River, NJ: Prentice Hall-Pearson.
Luger, G.F. 1986. Introduction to artificial intelligence: Professional materials for four day course, Los Angeles, CA: Learning Tree International.
Luger, G.F. 2009. Artificial intelligence: Structures and strategies for complex problem solving. New York: Addison Wesley-Pearson.
Luger, G.F. 2021. Knowing our world: An artificial intelligence perspective. New York: Springer Nature.
Minsky, M. 1985. The society of mind, New York: Simon and Schuster.
Mosteller, F. and Wallace D.L. 1963. Inference in an Authorship Problem. Journal of the American Statistical Association vol. 58, (302): 275–309.
Newell, A. and Simon, H.A. 1956. The logic theory machine. IRE Transactions of Information Theory, 2: 61–79.
Pearl, J. 1988. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Los Altos, CA: Morgan Kaufmann.
Pearl, J. 2000. Causality. New York: Cambridge University Press.
Perlis, D. 1988. Autocircumscription. Artificial Intelligence 36: 223–236.
Rosenblatt, F. 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386–408.
Rumelhart, D.E., McClelland, J.L., & The PDP Research Group. 1986a. Parallel distributed processing. Cambridge, MA: MIT Press.
Rumelhart, D.E., Hinton G.E., and Williams, R.J. 1986b. Learning representations by representing errors. Nature 323: 533–536.
Samuel, A.L. 1959. Some studies in machine learning using the game of checkers, IBM Journal of R& D, 3: 211–229.
Smith, B.C. 1985. Prologue to reflection and semantics in a procedural language. In Brachman and Levesque (1985).
Turing, A.M. 1950. Computing machinery and intelligence. Mind, 59: 433–460.
Burks, A.W. 1966. Theory of self reproducing automata. Chicago: Univ. of Illinois Press.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Luger, G.F. (2025). Computing, Representations, and Definitions of Artificial Intelligence. In: Artificial Intelligence: Principles and Practice. Springer, Cham. https://doi.org/10.1007/978-3-031-57437-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-57437-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57436-8
Online ISBN: 978-3-031-57437-5
eBook Packages: Artificial Intelligence (R0)