NON-SYMBOLIC APPROACHES TO ARTIFICIAL-INTELLIGENCE AND THE MIND

Current theories of artificial intelligence and the mind are dominated by the notion that thinking involves the manipulation of symbols. The symbols are intended to have a specific semantics in the sense that they represent concepts referring to objects in the external world and they conform to a syntax, being operated on by specific rules. I describe three alternative, non-symbolic approaches, each with a different emphasis but all using the same underlying computational model. This is a network of interacting computing units, a unit representing a nerve cell to a greater or lesser degree of fidelity in the different approaches. Computational neuroscience emphasizes the development. and functioning of the nervous system; the approach of neural networks examines new algorithms for specific applications in, for example, pattern recognition and classification; according to the sub-symbolic approach, concepts are built up of entities called sub-symbols, which are the activities of individual processing units in a neural network. A frequently debated question is whether theories formulated at the sub-symbolic level are 'mere implementations' of symbolic ones. I describe recent work due to Foster,who proposes that it is valid to view a system at many different levels of description and that, whereas any theory may have many different implementations, in general sub-symbolic theories may not be implementations of symbolic ones.