What artificial experts can and cannot do

Abstract

One's model of skill determines what one expects from neural network modelling and how one proposes to go about enhancing expertise. We view skill acquisition as a progression from acting on the basis of a rough theory of a domain in terms of facts and rules to being able to respond appropriately to the current situation on the basis of neuron connections changed by the results of responses to the relevant aspects of many past situations. Viewing skill acquisition in this ways suggests how one can avoid the problem currently facing AI of how to train a network to make human-like generalizations. In training a network one must progress, as the human learner does, from rules and facts to wholistic responses. As to future work, from our perspective one should not try to enhance expertise as in traditional AI by attempting to construct improved theories of a domain, but rather by improving the learner's access to the relevant aspects of a domain so as to facilitate learning from experience.

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Correspondence to Hubert L. Dreyfus.

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Dreyfus, H.L., Dreyfus, S.E. What artificial experts can and cannot do. AI & Soc 6, 18–26 (1992). https://doi.org/10.1007/BF02472766

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Keywords

  • Artificial Intelligence
  • Cognition
  • Connectionism
  • Expertise
  • Expert systems
  • Learning
  • Neural networks
  • Skill