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From: smoliar@VAXA.ISI.EDU (Stephen Smoliar)
Newsgroups: comp.ai.digest
Subject: Re: Gilding the Lemon
Message-ID: <8711031557.AA05337@vaxa.isi.edu>
Date: Tue, 3-Nov-87 10:57:49 EST
Article-I.D.: vaxa.8711031557.AA05337
Posted: Tue Nov  3 10:57:49 1987
Date-Received: Wed, 11-Nov-87 04:43:02 EST
References: <12346288066.15.LAWS@KL.SRI.Com>
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Reply-To: vaxa.isi.edu.uucp!smoliar (Stephen Smoliar)
Organization: Information Sciences Institute
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Approved: ailist@kl.sri.com
Summary: Progress in AI:  Engineering vs. Science?

In article <12346288066.15.LAWS@KL.SRI.Com> Laws@KL.SRI.COM (Ken Laws) writes:
>
>Progress also comes from applications -- very seldom from theory.

A very good point, indeed:  Bill Swartout and I were recently discussing
the issue of the respective contributions of engineering and science.
There is a "classical" view that science is responsible for those
fundamental principles without which engineering could "do its thing."
However, whence come those principles?  If we look at history, we see
that, in most fields, engineers are "doing their thing" long before
science has established those principles.  Of course things don't always
go as smoothly as one would like.  This pre-scientific stage of engineering
often involves sometimes-it-works-sometimes-it-doesn't experiences;  but
the engineering practices are still useful.  Often a major contribution
of the discovery of the underlying scientific principles is a better
understanding of WHEN "it doesn't work" and WHY that is so.  Then
engineering takes over again to determine what is to be done about
those situations in which things don't work.  At the risk of being
called on too broad a generality, I would like to posit that science
is concerned with the explanation of observed phenomena, while engineering
is concerned with achieving phenomena with certain desired properties.
From this point of view, engineering provides the very substance from
which scientific thought feeds.

I fear that what is lacking in the AI community is a respect for the
distinction between these two approaches.  A student is likely to get
a taste of both points of view in his education, but that does not
necessarily mean that he will develop an appreciation for the merits
of each or the ways in which they relate to each other.  As a consequence,
he may very well become very quickly channeled along a narrow path
involving the synthesis of some new artifact.  If he has any form
of success, then he assumes that all his thesis requires is that he
write up his results.

I hope there is some agreement that theses which arise from this process
are often "underwhelming" (to say the least).  There are usually rather
hefty tomes which devote significant page space to the twists and turns
in the path that leads to the student's achievement.  There is also usually
a rather heavy chapter which surveys the literature, so that the student
can demonstrate the front along which his work has advanced.  However,
such retrospective views tend to concentrate more on the artifacts of
the past than on the principles behind those artifacts.

Is it too much to ask that doctoral research in AI combine the elements
of both engineering and science?  I have nothing against that intensely
focused activity which leads up to a new artifact.  I just worry that
students tend to think the work is done once the artifact is achieved.
However, this is the completion of an engineering phase.  Frustrating
as it may sound, I do not think the doctoral student is done yet.  He
should now embark upon some fundamental portion of a scientific phase.
Now that he has something that works, he should investigate WHY it
works;  and THIS is where the literature search should have its true
value.  Given a set of hypothesized principles regarding the behavior
of his own artifact, how applicable are those principles to those
artifacts which have gone before?  Once such an investigation has been
pursued, the student can write a thesis which provides a balanced diet
of both engineering and science.