[comp.ai.digest] What can we learn from computers

oded@WISDOM.BITNET (Oded Maler) (07/26/88)

From: Oded Maler <oded%WISDOM.BITNET@CUNYVM.CUNY.EDU>
Date: Sun, 24 Jul 88 09:06 EDT
To: ai-list@ai.ai.mit.edu
Subject: What can we learn from computers



What Can We Learn From Computers and Mathematics?
=================================================

A few digests ago Gilbert Cockton raised the above rhetoric question as
part of an attack on the computationally-oriented methodologies of AI
research.  As a side remark let us recall that many AIers see their main
question as "What can we teach computers?" or "What are the limits to
the application of Mathematics to real-world modeling?"

Although I consider a lot of Cockton's criticism as valid, his claim
about the non-existent contribution of Mathematics and Computers to the
understanding of intelligence is at least as narrow-minded as the
practice of those AI researchers who ignore the relevance of Psychology,
Sociology, Philosophy and disciplines alike.

I claim that experience with computers, machines, formal systems, in
all levels, (starting from typing, programming and hacking, through
computer science and up to pure mathematics) can teach a person (and a
scientist) a lot, and may build relevant intuitions, metaphors and
perspectives.  Some of it cannot be gained through a life-long traditional
humanistic scholarship.

Just imagine what a self-aware person can learn from using a
word-processor by introspecting his own performance: Context-sensitive
interpretation of symbols, learning by analogy (when you move from one
WP to another), reversible and unreversible operations, eye-brain-hand
coordination. I'm sure Mr. Cockton's thoughts have benefited from such
an experience, so think of those who were using or designing such devices
for some decades.

And what about programming? Algorithms, nested loops, procedures and
hierarchical organization, stacks and recursive calls, data-structures
in general, input and output, buffers, parameter passing and communication,
efficiency of programs, top-down refinement, and the most important
experience: debugging.  No time before in history had so many people and
so often been involved in the process of making theories (about the
behavior of programs), watching them being refuted, fixing them and
testing again.  It is very easy to criticize the simplistic incorporation
of such paradigms into models for human thinking, as many hackers and
so-called AI researchers often do, but to think that these metaphors are
useless and irrelevant to the study of intelligence is just making an
ideology out of one's ignorance.

And let's proceed to theoretical computer science: the limits of
what is computable by certain machines, the results from
complexity theory about what can be performed in principle and yet
is practically impossible, the paradigm of a finite-state machine
with input, output and internal states, mathematical logic and its
shortcomings, the theory and practice of databases, the research
in distributed systems, the formal treatment of knowledge and belief:
is none of these relevant to the humanities?

Not the mention the mathematical ideas concerning sets, infinity,
order relations, distance functions, convergence, algebraic structures,
the foundations of probability, dynamical systems, games and strategies
and many many others.

Mr. Cockton was implicitly concerned with the following question:
"What is the best selection of formal and informal experience, a
scientist must have during his/her (hopefully ongoing) development, in
order to contribute to the advance of the cognitive sciences?" Excluding
experiences of the above kind from the candidate list is not what I
expect from adherents of scholar tradition.  Every scientist grows
within some sub-sub-discipline, learns its methodologies, tricks, success
criteria and its most influential bibliographic references.  When we
turn later to inter-disciplinary areas, we cannot go back to the kindergarten,
and start learning a new discipline as undergraduates do. We must learn
to discover those parts of other disciplines that are essential and
relevant to our object of study. Because of our inherent limitations
(complexity..) we are doomed to neglect and ignore a lot of work done
within other disciplines and even within our own. C'est la vie.

I agree with Mr. Cockton that by reading some AI work one gets the impression
that history begun just around the production of the paper: no references
to prior work and to past philosophical and psychological treatments of
the same issues. But going the other extreme, by adopting the scholar
(and sometimes almost snobbish) tradition to the modern cognitive sciences
is ridiculous too. Does a physicist or a chemist need to give references to
pre-Newtonian works, to astrology or alchemistry? (I'm not speaking of
the historian or philosopher of science).

I got the impression that Mr. Cockton views informaticians and
mathematicians in a rather stereotyped way: technocrats, misantropes that
can only deal with machines, persons that want to put the lively world
into their cold formulae and programs, individuals who are insensitive
to the arts and to human-human interaction.  All this might be partially
true with respect to a certain fraction, but to generalize to the whole
community is like saying that humanists are nothing but a bunch of guys,
incapable of clear and systematic thinking, who use the richness and
ambiguity of natural language in order to hide their either self-evident
or vacuouss and meaningless ideas.  I don't want to continue this
local-patriotic type of propaganda, but one can fill several screens
with similar deficiencies of the current traditions in the humanities.
The applicability of such descriptions to any fraction of the ongoing
work in the humanities, still does not justify a claim such as "What
Can We Learn from (say) Sociology?".

To conclude, I think that a good cognitive scientist can learn A LOT
from mathematics and computers. A humanist may still do important
work in spite of his mathematical ignorance, but I suspect that
in some fields this will become harder and harder.

Oded Maler
Dept. of Applied Mathematics
Weizmann Institute
Rehovot 76100
Israel

oded@wisdom.bitnet