Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!usc!apple!snorkelwacker!mit-eddie!media-lab!minsky
From: minsky@media-lab.MEDIA.MIT.EDU (Marvin Minsky)
Newsgroups: comp.ai
Subject: Re: What Has Traditional AI Accomplished?
Message-ID: <3670@media-lab.MEDIA.MIT.EDU>
Date: 11 Oct 90 22:44:44 GMT
References: <69460@lll-winken.LLNL.GOV> <1990Oct9.184502.106@watdragon.waterloo.edu> <3649@media-lab.MEDIA.MIT.EDU> <69607@lll-winken.LLNL.GOV>
Reply-To: minsky@media-lab.media.mit.edu (Marvin Minsky)
Organization: MIT Media Lab, Cambridge MA
Lines: 42

In article <69607@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren Petrich) writes:
>In article <3649@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes:

>>The big future, in my view, will come when common sense data bases
>>(none of which yet exist, expect perhaps for CYC as a prototype) help
>>the field move from "expert" applications to "commonsense"
>>applications.  Only then will general-purpose language translation be
>>feasible.
>
>	I see. I saw an article about CYC some time ago in _Discover_;
>it is apparently able to learn. I wonder how "intelligent" does it
>seem to those who have worked with it?
>>	I also get the impression that it is a rather monstrous
>system. I guess my ideal would be to have a relatively simple "kernel"
>system, which would proceed to build up a large database of
>information on whatever it was working on, by employing some learning
>algorithm.

CYC is not intelligent yet.  Its architect, Douglas Lenat, maintains
that his goal is to supply a commonsense database, and *then* work on
making the system be intelligent.  His reason, roughly, is that being
smart requires a lot of common sense knowledge.

Yes, it would be nice if we could do this with a learning program,
instead of having to program it.  Only no one knows how to do this
yet.

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.  So, let's
try to getr some such data bases so that other researchers can use
them to make smart machines.

My associate here, Ken Haase, is doing some experiments to try to
learn some such stuff from reading text and then questioning people.
But it will be a while before we can evaluate his experiments.