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From: rej%Cornell@sri-unix.UUCP
Newsgroups: net.ai
Subject: The AI Challenge
Message-ID: <13841@sri-arpa.UUCP>
Date: Sat, 19-Nov-83 09:22:42 EST
Article-I.D.: sri-arpa.13841
Posted: Sat Nov 19 09:22:42 1983
Date-Received: Tue, 22-Nov-83 02:25:29 EST
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From:  rej@Cornell (Ralph Johnson)

The recent discussions on AIlist have been boring, so I have another
idea for discussion.  I see no evidence that that AI is going to make
as much of a change on the world as data processing or information
retrieval.  While research in AI has produced many results in side areas
such as computer languages, computer architecture, and programming
environments, none of the past promises of AI (automatic language
translation, for example) have been fulfilled.  Why should I expect
anything more in the future?

I am a soon-to-graduate PhD candidate at Cornell.  Since Cornell puts
little emphasis on AI, I decided to learn a little on my own.  Most AI
literature is hard to read, as very little concrete is said.  The best
book that I read (best for someone like me, that is) was the three-volume
"Handbook on Artificial Intelligence".  One interesting observation was
that I already knew a large percentage of the algorithms.  I did not
even think of most of them as being AI algorithms.  The searching
algorithms (with the exception of alpha beta pruning) are used in many
areas, and algorithms that do logical deduction are part of computational
mathematics (just my opinion, as I know some consider this hard core AI).
Algorithms in areas like computer vision were completely new, but I could
see no relationship between those algorithms and algorithms in programs
called "expert systems", another hot AI topic.

  [Agreed, but the gap is narrowing.  There have been 1 or 2 dozen
  good AI/vision dissertations, but the chief link has been that many
  individuals and research departments interested in one area have
  also been interested in the other.  -- KIL]

As for expert systems, I could see no relationship between one expert system
and the next.  An expert system seems to be a program that uses a lot of
problem-related hacks to usually come up with the right answer.  Some of
the "knowledge representation" schemes (translated "data structures") are
nice, but everyone seems to use different ones.  I have read several tech
reports describing recent expert systems, so I am not totally ignorant.
What is all the noise about?  Why is so much money being waved around?
There seems to be nothing more to expert systems than to other complicated
programs.

  [My own somewhat heretical view is that the "expert system" title
  legitimizes something that every complicated program has been found
  to need: hackery.  A rule-based system is sufficiently modular that
  it can be hacked hundreds of times before it is so cumbersome
  that the basic structures must be rewritten.  It is software designed
  to grow, as opposed to the crystalline gems of the "optimal X" paradigm.
  The best expert systems, of course, also contain explanatory capabilities,
  hierarchical inference, constrained natural language interfaces, knowledge
  base consistency checkers, and other useful features.  -- KIL]

I know that numerical analysis and compiler writing are well developed fields
because there is a standard way of thinking that is associated with each
area and because a non-expert can use tools provided by experts to perform
computation or write a parser without knowing how the tools work.  In fact,
a good test of an area within computer science is whether there are tools
that a non-expert can use to do things that, ten years ago, only experts
could do.  Is there anything like this in AI?  Are there natural language
processors that will do what YACC does for parsing computer languages?

There seem to be a number of answers to me:

1)  Because of my indoctrination at Cornell, I categorize much of the
    important results of AI in other areas, thus discounting the achievements
    of AI.

2)  I am even more ignorant than I thought, and you will enlighten me.

3)  Although what I have said describes other areas of AI pretty much, yours
    is an exception.

4)  Although what I have said describes past results of AI, major achievements
    are just around the corner.

5)  I am correct.

You may be saying to yourself, "Is this guy serious?"  Well, sort of.  In
any case, this should generate more interesting and useful information
than trying to define intelligence, so please treat me seriously.

        Ralph Johnson