The traditional approach to AI is limited because it fails to exploit continuity. The reliance on discrete logic has allowed the rapid initial advance of the subject, but constitutes an inherent deficiency. The limitations have become apparent, and are generally acknowledged by a revival of interest in neural‐net, or connectionist, techniques. This approach has become feasible because of technical developments allowing large‐scale parallel operation. Lessons can be learned by considering the evolution of natural intelligence. Recent studies from a biological viewpoint suggest that this has some unexpected features. The idea of concept formation should be extended to include quantifiable concepts, similar to the semantic variables of fuzzy set theory.
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