Artificial symbols and the essence of intelligent computing

摘要

A challenge for intelligent computing is translating the skills of innovation into mathematical theory and persistent learning algorithms. Computational intelligence differs from artificial intelligence in that artificial intelligence reasons over symbols while computational intelligence reasons over sub-symbolic data and information. Natural symbols arise from shared human experiences. The creative quality of human interaction suggests symbol generation involves a collection of cooperative agents capable of representing relative experience, negotiating innovation, and―finally―building consensus. As hybrids of sub-symbolic and symbolic reasoning become the norm, it is necessary to formalize the design and evaluation of artificial symbols. In this paper, we propose a delineation between sub-symbolic patterns and symbolic experience. Further, we demonstrate the construction of an artificial symbol which―we assert―is the ultimate culmination of an intelligent computation. We apply this theory to model selection among neural networks.