Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!wuarchive!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: <3649@media-lab.MEDIA.MIT.EDU>
Date: 9 Oct 90 22:29:46 GMT
References: <69367@lll-winken.LLNL.GOV> <1990Oct7.003647.1666@watdragon.waterloo.edu> <69460@lll-winken.LLNL.GOV> <1990Oct9.184502.106@watdragon.waterloo.edu>
Reply-To: minsky@media-lab.media.mit.edu (Marvin Minsky)
Organization: MIT Media Lab, Cambridge MA
Lines: 37



In article <69367@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren
Petrich) writes: (I may have the attribution wrong)

>	I know that this question may well start a big flame war, but
>I would like an idea of exactly what traditional AI has accomplished.

Someone at Arthur Anderson Inc told me that half of their
multi-billion dollar income was coming from the expert systems
supplied by them, about a year or so ago.  The ES part of the company
had become as large as the rest of it, and this led to the company's
splitting into two parts.

It is hard to say what AI is, after a few years, because the heuristic
methods get refined.  For example, there are many computer vision
systems working in different places, but no one regards them as AI any
more.  OCR and speech recognizers are today on the edge of being major
industries; they were called AI when the pioneering work was done, but
we merely call them Pattern Recognition today.

Computer algebra was AI when Slagle did his 1961 thesis on
integration.  The methods became more refined and reliabkle by the
time of Moses' thesis in 1966 (I think) and the heuristic search was
almost completely eliminated by the time of MACSYMA, because the
mathematical foundations had been highly developed (by Risch and
Caviness, among others).

Look at any bookshelf of texts on ES's to see industrial applications,
or any shelf of books on computational linguistics, to see what has
become of earlier AI language systems.

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.