kadie@m.cs.uiuc.edu (09/02/89)
I just paged through a book called Cognizers by R. Colin Johnson. The
author is the former editor of a trade newspaper.
The book is about artificial neural nets (ANNs), which the author prefers
call cognizers, a term he coined because it has a nice verb form.
Here are the three most provocative arguments of the book:
+ "The Brain is Not A Computer" A computer can perhaps
*simulate* the brain, but it cannot *model* the brain. ANN's
can perhaps *model* the brain.
I won't try to explain the distinction he sees between simulating and modeling.
Or how it is that ANNs overcome the distinction while computers do not.
He draws an analogy between the skyline of a city and the skyline of a
model of a city. I think most people in computer science believe that
computation is different than skylines. Something that simulates a
skyline, may not be a skyline. Something that fully simulates
a computer *is* a computer. Whether the brain/mind/sole follows the
pattern of computers or skylines is a philosophical question that
the books does little to settle.
+ ANNs are better than computers because they don't use symbols. Symbols
are a problem because no one knows how to tie them to things in the
real world (the symbol-grounding problem).
One could just as easily make the opposite argument. People use
symbols (for example, in language). ANNs don't. Computers do. Therefore,
computers are better than ANN's. Is symbol use a bug or a feature?
The book does little to settle the question.
+ AI systems can't learn.
"Although the expert system can take the knowledge acquired by a human
expert and automate the reasoning process, they still cannot learn any
thing new on their own." (p. 42)
"While cognizers [ANNs] will probably fare no better as a general
problem solver, they do hold out the promise of easing the development
of specific problem solvers, because they can learn. An AI system
must be explicitly programmed by a knowledge engineer after human experts
in the domain have be interviewed." (p. 156)
It appears that the author is not familiar with the AI subfield of
machine learning. Like ANN systems, these systems learn
from examples. Unlike (most) ANN systems, (most) machine learning
systems produced rules that can be understood by people.
The recent International Joint Conference on Artificial Intelligence
included several studies that compared AI systems to ANNs on
a variety of learning problems. Both approaches learned with high
highly accuracy. The AI systems were generally much faster (they
learned in seconds rather than in hours).
In sum, the author does a disservice to ANN's by hyping them
for the wrong reasons. The publisher apparently realizes
this: the book begins with a disclaimer.
Carl Kadie
Beckman Institute
Department of Computer Science
University of Illinois at Urbana-Champaign
kadie@uiuc.edu