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Artificial Intelligence Review

, Volume 1, Issue 2, pp 95–109 | Cite as

Connectionist AI, symbolic AI, and the brain

  • P. Smolensky
Article

Abstract

Connectionist AI systems are large networks of extremely simple numerical processors, massively interconnected and running in parallel. There has been great progress in the connectionist approach, and while it is still unclear whether the approach will succeed, it is also unclear exactly what the implications for cognitive science would be if it did succeed. In this paper I present a view of the connectionist approach that implies that the level of analysis at which uniform formal principles of cognition can be found is the subsymbolic level, intermediate between the neural and symbolic levels. Notions such as logical inference, sequential firing of production rules, spreading activation between conceptual units, mental categories, and frames or schemata turn out to provide approximate descriptions of the coarse-grained behaviour of connectionist systems. The implication is that symbol-level structures provide only approximate accounts of cognition, useful for description but not necessarily for constructing detailed formal models.

Keywords

Cognitive Science Large Network Production Rule Great Progress Logical Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Intellect Ltd 1987

Authors and Affiliations

  • P. Smolensky
    • 1
  1. 1.Department of Computer Science and Institute of Cognitive ScienceUniversity of Colorado at BoulderUSA

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