Abstract

This paper concerns the relationship between neural-like computational networks, numerical pattern recognition, and intelligence. Extensive research that proposes the use of neural models for a wide variety of applications has been conducted in the past few years. Sometimes the justification for investigating the potential of neural nets (NNs) is obvious. On the other hand, current enthusiasm for this approach has also led to the use of neural models when the apparent rationale for their use has been justified by what is best described as “feeding frenzy.” In this latter instance there is at times a concomitant lack of concern about many “side issues” connected with algorithms (e.g., complexity, convergence, stability, robustness, and performance validation) that need attention before any computational model becomes part of an operational system. These issues are examined with a view toward guessing how best to integrate and exploit the promise of the neural approach with other efforts aimed at advancing the art and science of pattern recognition and its applications in fielded systems in the next decade. A further purpose of the present paper is to characterize the notions of computational, artificial, and biological intelligence; our hope is that a careful discussion of the relationship between systems that exhibit each of these properties will serve to guide rational expectations and the development of models that exhibit or mimic “human behavior.”

Author Keywords: neural networks; pattern recognition; intelligence

Article Outline

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Corresponding author. Address correspondence to James C. Bezdek, Division of Computer Science, University of West Florida, Pensacola, FL 32514.