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From: smoliar@vaxa.isi.edu (Stephen Smoliar)
Newsgroups: comp.ai
Subject: Re: What Has Traditional AI Accomplished?
Summary: putting it all in the computer
Message-ID: <15294@venera.isi.edu>
Date: 13 Oct 90 01:21:39 GMT
References: <69460@lll-winken.LLNL.GOV> <1990Oct9.184502.106@watdragon.waterloo.edu> <3649@media-lab.MEDIA.MIT.EDU> <69607@lll-winken.LLNL.GOV> <3670@media-lab.MEDIA.MIT.EDU>
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Reply-To: smoliar@vaxa.isi.edu (Stephen Smoliar)
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In article <3670@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu
(Marvin Minsky) writes:
>
>The problem with "building a large database of .. whatever it was
>working on" is, in my view, that this is why the expert systems have
>remained so limited and specialized.  Suppose you were making a system
>to help storekeepers.  What's a shirt.  As Lenat points out, you ought
>to know where they come from.  Clothing stores.  How do you know that.
>I buy socks in the drug store around the corner.  How long do you wear
>a shirt.  When it gets a stain, you can still wear it for fixing your
>car.  Unless you know more or less "everything that every ordinary
>person knows" you can't interact with them in a reasonable way,
>understand what they say, or help them when they need help.

I fear there is a tendency to underestimate the possible impact of these
observations.  We are so crazy about data bases that we tend to think that
they will solve all our problems if we just fill them up properly.  However,
it is not clear (to me, at least) that we really CAN build a data base which
will capture "everything that every ordinary person knows" . . . even about
the limited domain of shirts.  Our knowledge of shirts is very much a matter
of how we experience the world, in which we wear shirts, buy them, take them
to the laundry, and any number of other things, often dictated by the demands
of a specific situation.  (Had it been a warmer day, Walter Raleigh might not
have had a cloak, in which case I might have used his shirt, instead!)  Given
so much variety, I am not sure it makes sense to ask how much we can store away
about shirts in a data base, only to worry about how we are ever going to
retrieve any of it back and under what circumstances.  An alternative approach
is to ask what we need to know in order to behave properly when shirts are part
of the world around us.  Thus, we learn how to button up our shirts through a
process which evolves from having it done for us to doing it for ourselves.
(This reminds me of Minsky's observation--which I recently heard on CNN--that
a computer can take on the complexity of chess but not the simplicity of tying
shoe laces.)

As Minsky pointed out, we do not know how to build a learning program which
could "feed" CYC.  Perhaps we are worrying about the wrong kind of learning
program.  Perhaps it makes more sense to worry about learning patterns of
behavior, like buttoning a shirt or knowing when it is time to take it to
the cleaners.  Our preoccupation with neat declarative sentences (or entries
in a data base) tend to distract us from such questions of behavior;  but
perhaps that is a better front along which to attack issues of learning.

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