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From: shebs@utah-cs.UUCP (Stanley Shebs)
Newsgroups: net.ai
Subject: Re: taxonomizing in AI: useless, harmful
Message-ID: <3679@utah-cs.UUCP>
Date: Wed, 12-Feb-86 12:02:35 EST
Article-I.D.: utah-cs.3679
Posted: Wed Feb 12 12:02:35 1986
Date-Received: Fri, 14-Feb-86 03:06:06 EST
References: <3600038@iuvax.UUCP>
Reply-To: shebs@utah-cs.UUCP (Stanley Shebs)
Organization: University of Utah, Salt Lake City
Lines: 55

In article <3600038@iuvax.UUCP> marek@iuvax.UUCP writes:

>... Taxonomizing is a debatable art of empirical
>science, usually justified when a scientist finds itself overwhelmed with
>gobs and gobs of identifiable specimens, e.g. entymology.  But AI is not
>overwhelmed by gobs and gobs of tangible singulars; it is a constructive
>endeavor that puts up putatative mechanisms to be replaced by others.  The
>kinds of learning Michalski so effortlessly plucks out of the thin air are not
>as incontrovertibly real and graspable as instances of dead bugs.

Now I'm confused!  Were you criticizing Michalski et al's taxonomy of
learning techniques in pp. 7-13 of "Machine Learning", or the "conceptual
clustering" work that he has done?  I think both are valid - the first
is basically a reader's guide to help sort out the strengths and limitations
of dozens of different lines of research.  I certainly doubt (and hope)
no one takes that sort of thing as gospel.

For those folks not familiar with conceptual clustering, I can characterize
it as an outgrowth of statistical clustering methods, but which uses a
sort of Occam's razor heuristic to decide what the valid clusters are.
That is, conceptual "simplicity" dictates where the clusters lie.  As an
example, consider a collection of data points which lie on several
intersecting lines.  If the data points you have come in bunches at
certain places along the lines, statistical analysis will fail dramatically;
it will see the bunches and miss the lines.  Conceptual clustering will
find the lines, because they are a better explanation conceptually than are
random bunches.  (In reality, clustering happens on logical terms in
a form of truth table; I don't think they've tried to supplant statisticians
yet!)

>Please consider whether taxonomizing kinds of learning from the AI perspective
>in 1981 is at all analogous to chemists' and biologists' "right to study the
>objects whose behavior is ultimately described in terms of physics."  If so,
>when is the last time you saw a biology/chemistry text titled "Cellular
>Resonance" in which 3 authors offered an exhaustive table of carcinogenic
>vibrations, offered as a collection of current papers in oncology?...

Hmmm, this does sound like a veiled reference to "Machine Learning"!
Personally, I prefer a collection of different viewpoints over someone's
densely written tome on the ultimate answer to all the problems of AI...

>More constructively, I am in the process of developing an abstract machine.
>I think that developing abstract machines is more in the line of my work as
>an AI worker than postulating arbitrary taxonomies where there's neither need
>for them nor raw material.
>
>				-- Marek Lugowski

I detect a hint of a suggestion that "abstract machines" are Very Important
Work in AI.  I am perhaps defensive about taxonomies because part of my
own work involves taxonomies of programming languages and implementations,
not as an end in itself, but as a route to understanding.  And of course
it's also Very Important Work... :-)

							stan shebs