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From: ong@uiucdcsp.cs.uiuc.edu
Newsgroups: comp.ai
Subject: Re: Goal of AI: where are we going?
Message-ID: <76600014@uiucdcsp>
Date: Tue, 6-Oct-87 13:40:00 EDT
Article-I.D.: uiucdcsp.76600014
Posted: Tue Oct  6 13:40:00 1987
Date-Received: Sat, 10-Oct-87 06:07:42 EDT
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Nf-From: uiucdcsp.cs.uiuc.edu!ong    Oct  6 12:40:00 1987


/* Written  1:14 am  Oct  3, 1987 by czhj@vax1.UUCP in uiucdcsp:comp.ai */
/* ---------- "Re: Goal of AI: where are we going?" ---------- */
In article <46400008@uxe.cso.uiuc.edu> morgan@uxe.cso.uiuc.edu writes:
>
>Maybe you should approach it as a scientist, rather than an engineer.  Think
>...
>What AI really ought to be is a
>science that studies intelligence, with the goal of understanding it by
>rigorous theoretical work, and by empirical study of
>systems that appear to have intelligence, whatever that is.  The best work
>in AI, in my opinion, has this scientific flavor.  Then it's up to the
>engineers (or society at large) to decide what to do with the knowledge
>gained, in terms of constructing practical systems.


I wholeheartedly support this idea.  I'd go even further however, and say that 
most "AI" research is a huge waste of time.  I liken it to using trial and
error methods like those used by Edison which led him to try thousands of
possibilities before hitting one that made a good lightbulb.  With AI, the
problem is infinitely more complicated, and the chance of finding a solution
by blind experimentation is nil.

On the other hand, if we take an educated approach to the problem, and study
'intelligent' systems, we have a much greater chance of solving the mysteries
of the mind.

Some of you may remember my postings from last year where I expounded on the
virtues of cognitive psychology.  After investigating research in this field
in more detail, I came up very disillusioned.  Here is a field of study in 
which the soul purpose is to scientifically discover the nature of thought.
Even with some very bright people working on these problems, I found that the
research left me cold.  Paper after paper describe isolated phenomena, then go
on to present some absurdly narrow minded theory of how such phenomena could
occur.

I've reached the conclusion that we cannot study the mind in isolated pieces
which we try to put together to form a whole.  But rather we have to study
the interactions between the pieces in order to learn about the pieces 
themselves.  For example, take vision research.  Many papers have been written
about edge detection algorithms, possible geometries, and similarly
reductionist algorithms for making sense of scenes.  I assert that the
interplay between the senses and the experiential memory is huge.  Further,
because of these interactions, no simple approach will ever work well.  In
fact, what we need is to study the entire set of processes involved in seeing
before we can determine how we perceive objects in space.

This is but a single example of the complexity of studying such aspects of the
mind.  I found that virtually every aspect of cognition has such problems. 
That is, no aspect is isolated!

Because of this immensely complex set of interactions, I believe that the 
connectionist theories are heading in the right direction.  However, these
theories are somewhat too reductionistic for my tastes as well.  I want to 
understand how the mind works at a high level (if possible).  The actual
implementation is the easy part.  The understanding is the hard part.

---Ted Inoue
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