Article,
On the relationship between neural networks, pattern recognition and intelligence
International Journal of Approximate Reasoning, 6 (2): 85--107 (1992)
DOI: http://dx.doi.org/10.1016/0888-613X(92)90013-P
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.”
