kort@hounx.UUCP (B.KORT) (08/21/86)
Notes on AAAI
Barry Kort
Abstract
The Fifth Annual AAAI Conference on Artificial Intelligence
was held August 11-15 at the Philadelphia Civic Center.
These notes record the author's personal impressions of the
state of AI, and the business prospects for AI technology.
The views expressed are those of the author and do not
necessarily reflect the perspective or intentions of other
individuals or organizations.
* * *
The American Association for Artificial Intelligence held
its Fifth Annual Conference during the week of August 11,
1986, at the Philadelphia Civic Center.
Approximately 5000 attendees were treated to the latest
results of this fast growing field. An extensive program of
tutorials enabled the naive beginner and technical-
professional alike to rise to a common baseline of
understanding. Research and Science Sessions concentrated on
the theoretical underpinnings, while the complementary
Engineering Sessions focused on reduction of theory to
practice.
Dr. Herbert Schorr of IBM delivered the Keynote Address.
His message was simple and straightforward: AI is here
today, it's real, and it works. The exhibit floor was a sea
of high-end workstations, running flashy applications
ranging from CAT scan imagery to automated fault diagnosis,
to automated reasoning, to 3-D scene animation, to
iconographic model-based reasoning. Symbolics, TI, Xerox,
Digital, HP, Sun, and other vendors exhibited state of the
art hardware, while Intellicorp, Teknowledge, Inference,
Carnegie-Mellon Group, and other software houses offered
knowledge engineering power tools that make short work of
automated reasoning.
Knowledge representation schema include the ubiquitous tree,
as well as animated iconographic models of dynamic systems.
Inductive and deductive reasoning and goal-directed logic
appear in the guise of forward and backward chaining
algorithms which seek the desired chain of nodes linking
premiss to predicted conclusion or hypothesis to observed
symptoms. Such schema are especially well adapted to
diagnosis of ills, be it human ailment or machine
malfunction.
Natural Language understanding remains a hard problem, due
to the inscrutable ambiguity of most human-generated
utterances. Nevertheless, silicon can diagram sentences as
well as a precocious fifth grader. In limited domain
vocabularies, the semantic content of such diagrammatic
representations can be reliably extracted.
Robotics and vision remain challenging fields, but advances
in parallel architectures may clear the way for notable
progress in scene recognition.
Qualitative reasoning, model-based reasoning, and reasoning
by analogy still require substantial human guidance, perhaps
because of the difficulty of implementing the interdomain
pattern recognition which humans know as analogy, metaphor,
and parable.
Interesting philosophical questions abound when AI moves
into the fields of automated advisors and agents. Such
systems require the introduction of Value Systems, which may
or may not conflict with individual preferences for
benevolent ethics or hard-nosed business pragmatics. One
speaker chose the provocative title, "Can Machines Be
Intelligent If They Don't Give a Damn?" We may be on the
threshold of Artificial Intelligence, but we have a long way
to go before we arrive at Artificial Wisdom. Nevertheless,
some progress is being made in reducing to practice such
esoteric concepts as Theories of Equity and Justice, leading
to the possibility of unbiased Jurisprudence.
AI goes hand in hand with Theories of Learning and
Instruction, and the field appears to be paying dividends in
the art and practice of knowledge exchange, following the
strategy first suggested by Socrates some 2500 years ago.
The dialogue format abounds, and mixed initiative dialogues
seem to capture the essence of mutual teaching and
mirroring. Perhaps sanity can be turned into an art form
and a science.
Belief Revision and Truth Maintenance enable systems to
unravel confusion caused by the injection of mutually
inconsistent inputs. Nobody's fool, these systems let the
user know that there's a fib in there somewhere.
Psychology of computers becomes an issue, and the Silicon
Syndrome of Neuroses can be detected whenever the machines
are not taught how to think straight. Machines are already
sapient. Soon they will acquire sentience, and maybe even
free will (nothing more than a random number generator
coupled with a value system). Perhaps by the end of the
Millenium (just 14 years away), the planet will see its
first Artificial Sentient Being. Perhaps Von Neumann knew
what he was talking about when he wrote his cryptic volume
entitled, On the Theory of Self-Reproducing Automata.
There were no Cybernauts in Philadelphia this year, but many
of the piece parts were in evidence. Perhaps it is just a
matter of time until the Golem takes its first step.
In the mean time, we have entered the era of the Competent
System, somewhat short on world-class expertise, but able to
hold it's own in today's corporate culture. It learns about
as fast as its human counterpart, and is infinitely
clonable.
Once upon a time it was felt that machines should work and
people should think. Now that machines can think, perhaps
people can take more time to enjoy the state of being called
Life.
* * *
Lincroft, NJ
August 17, 1986hsgj@batcomputer.TN.CORNELL.EDU (Mr. Barbecue) (08/29/86)
(not really a followup article, more of a commentary) I find it very interesting that there is so much excitement generated over parallel processing computer systems by the AI community. Interesting in that the problems of AI (the intractability of: language, vision, and general cognition to name a few) are not anywhere near limited by computational power but by our lack of understanding. If somebody managed to create a truely intelligent system, I think we would have heard about it by now, even if the program took a month to run. Fact of the matter is that our knowledge of such problems is minimal. Attempts to solve them leads to researchers banging their heads against a very hard wall, indeed. So what is happening? The field that was once A.I. is very quickly headed back to it's origins in computer science and is producing "Expert Systems" by the droves. The problem isn't that they aren't useful, but rather that they are being touted as the A.I., and true insights into actual human thinking are still rare (if not non-existant). Has everybody given up? I doubt it. However, it seems that economic reality has set in. People are forced to show practical systems with everyday appli- cations. Financers can't understand why we would be overjoyed if we could develop a system that learns like a baby, and so all the money is being siphoned away and into robotics, Expert Systems, and even spelling checkers! (no, I don't think that welding cars together requires a great deal of true intelligence, though technically it may be great) So what is one to do? Go into cog-psych? At least psychologists are working on the fundamental problems that AI started, but many seem to be grasping at straws, trying to find a simple solution (i.e., familly resemblance, primary attribute analysis, etc.) What seems to be lacking is a cogent combination of theories. Some attempts have been made, but these authors basically punt on the issue, stating like "none of the above theories adequately explain the observed phenomena, perhaps the solution is a combination of current hypothesis". Very good, now lets do that research and see if this is true! My opinion? Well, some current work has dealt with computer nervous systems, (Science --sometime this summer). This is similar in form to the hypercube systems but the theory seems different. Really the work is towards computer neurons. Distributed systems in which each element contributes a little to the final result. Signals are not binary, but graded. They combine with other signals from various sources and form an output. Again, this could be done with a linear machine that hold partial results. But, I'm not suggesting that this alone is a solution, it's just interesting. My real opinion is that without "bringing baby up" so to speak, we won't get much accomplished. The ultimate system will have to be able to reach out, grasp (whether visually or physically, or whatever) and sense it's world around it in a rich manner. It will have to be malleable, but still have certain guidelines built in. It must truely learn, forming a myriad of connections with past experiences and thoughts. In sum, it will have to be a living animal (though made of sand..) Yes, I do think that you need the full range of systems to create a truely intelligent system. Hellen Keller still had touch. She could feel vibrations, and she could use this information to create a world that was probably perceptually much different than ours. But, she had true intelligence. (I realize that the semantics of all these words and phrases are highly debated, you know what I'm talking, so don't try to be difficult!) :) Well, that's enough for a day. Ted Inoue. Cornell -- ARPA: hsgj%vax2.ccs.cornell.edu@cu-arpa.cs.cornell.edu UUCP: ihnp4!cornell!batcomputer!hsgj BITNET: hsgj@cornella
kort@hounx.UUCP (B.KORT) (09/01/86)
I appreciated Ted Inoue's commentary on the State of AI. I especially agree with his point that a cogent combination of theories is needed. My own betting card favors the theories of Piaget on learning, coupled with the modern animated-graphic mixed-initiative dialogues that merge the Socratic-style dialectic with inexpensive PC's. See for instance the Mind Mirror by Electronic Arts. It's a flashy example of the clever integration of Cognitive Psychology, Mixed Initiative Dialogues, Color Animated Graphics, and the Software/Mindware Exchange. Such illustrations of the imagery in the Mind's Eye can breathe new life into the relationship between silicon systems and their carbon-based friends. Barry Kort hounx!kort