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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.

Reviews some of the good reasons for looking to real neural nets for guidance on ways of implementing effective parallel computation. Discusses existing artificial neural nets with particular attention to the extent to which they model real neural activity. Indicates some serious mismatches, but shows that there are also important correspondences. The successful applications are to image processing, pattern classification and automatic optimization, in various guises. Reviews important issues raised by extension to the symbolic problem solving of “intellectual” thought, the prime concern of classical AI. These illustrate the importance of recursion, and of a degree of continuity associated with any evolutionary process.

A discussion and review of the important biography by Pesi R. Mansani of Norbert Wiener, with comment on the pre‐cybernetic era, Wiener's origins, his post‐doctoral studies, together with his achievements in mathematics, his connection with the USSR, his war‐time activities and, finally, his involvement with cybernetics.

The general problems of cybernetics were considered from an educational point of view in an address to the Convegno de Cibernetica Scolastica, in Messina, Sicily. To do this satisfactorily the meaning of cybernetics should be discussed and the history of the emergence of the subject area reviewed with special reference to the future. Important implications for education are presented.

In the current debate on appropriate representations of uncertainty, it is usual to assume a finite set of events to be discriminated. A similar bias is apparent in much discussion in neurophysiology and cognitive psychology, and it entails de‐emphasis of the property of continuity (“in the large”), necessary for interpolation, extrapolation and ranking. The importance of continuity is acknowledged in recent discussions related to fuzzy set theory, which acknowledge linguistic variables. Argues that this is an important development and that much other discussion is of restricted applicability because it fails to recognize continuity.

The attempt to understand the working of the brain suffers from difficulties of definition, since it is not clear that brains can understand brains, and the nature of “understanding” is essentially subjective. If it is accepted that our thought processes are products of evolution, the principles of rational discourse and science are reflections of the environment rather than inherent in us. Nevertheless they are strongly ingrained and well‐founded, but since their implications are essentially subjective they cannot be distinguished from other assertions that would be classed as “mystical”.