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AIList Digest Volume 7 Issue 039
AIList Digest Monday, 20 Jun 1988 Volume 7 : Issue 39
Today's Topics:
Fuzzy systems theory
Human-Human Communication
Philosophy:
Biological relevance and AI
determinism a dead issue?
Cognitive AI vs Expert Systems
Biological relevance and AI
Consensual realities are structurally unstable
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Date: 19 Jun 88 11:57:24 GMT
From: uflorida!novavax!proxftl!bill@umd5.umd.edu (T. William Wells)
Subject: Re: Fuzzy systems theory was (Re: Alternative to Probability)
In article <1073@usfvax2.EDU>, pollock@usfvax2.EDU (Wayne Pollock) writes:
> On the other hand, set theory, which underlies much of current theory, is
> also based on fallacies; (given the basic premses of set theory one can
> easily derive their negation).
Just where DID you get that idea? While it was true of the set
theory of around a century ago, it is NOT true of set theory
today.
> As long as fuzzy logic provides a framework
> for dicussing various concepts and mathematical ideas, which would be hard
> to describe in traditional terms, the theory serves a purpose.
You seemed to miss my point: fuzzy systems theory MIGHT be an
interesing form of mathematics (but ask a mathematician, don't
ask me); BUT in its current form it is not valid as a means of
representing the real world.
------------------------------
Date: Wed, 15 Jun 88 13:14:29 EDT
From: "William J. Joel" <JZEM%MARIST.BITNET@MITVMA.MIT.EDU>
Subject: Human-Human Communication
It seems to me that recent discussion on this topic has been running
around in circles. First off, all communication is coded. The types
that humans use are merely ways to encapsulate thought so that another
human might attempt to understand what the first human meant.
In order to truely 'understand' each other we would first have to
understand exactly how the brain works ... exactly. Since that's far
off, then anything we do is but an approximation.
------------------------------
Date: 17 Jun 88 20:45:01 GMT
From: uflorida!novavax!proxftl!tomh@gatech.edu (Tom Holroyd)
Subject: Re: Human-human communication
In article <33343@linus.UUCP>, bwk@mitre-bedford.ARPA (Barry W. Kort) writes:
> How can we talk about that which cannot be encoded in language?
[stuff deleted]
> I know how to walk, how to balance on a bicycle, and how to reduce
> my pulse. But I can't readily transmit that knowledge in English.
> In fact, I don't even know how I know these things.
You ride a bicycle by transforming input signals from your sensory system
into output signals for your muscles. On the way, these signals are modified
by a large number of factors, including some conscious ones which we will
ignore. The input/output signals can be represented as vectors, and the
transformation is a mapping from one vector space to another. If you train
a neural net to learn the mapping from sense data to leg movement (and I'm
only talking about simple motion here), the connections of the network encode
the knowledge of how to ride a bicycle. Enough to build a robot that can
ride a bike. Maybe not cross an intersection safely.. :-)
Or, I could list a bunch of differential equations that describe the dynamics
of riding a bike.
Neither of these is complete, and the connectionist form would include a
lot of floating point data, so they don't really count as describing anything
in English. However, by analyzing the forms of the equations, it is often
possible to develop an understanding of what is going on.
Does reducing the problem to a mathematical description count? The next step
would be to develop a jargon to cover the dynamics of the situation. Maybe
we just don't have terms for many of the actions required for bike riding.
Summary: Everything can be described mathematically, and the mathematics
can be described in English. Caveat: we haven't figured out how to describe
everything using mathematics yet. To me, this is the real problem. Some
subjective phenomena may well prove to be irreducible in the sense that
in order to understand why a person thinks something is beautiful (say),
we'll need to have a large part of that person's brain state, and no amount
of mathematical gymnastics will make the data any less complex. (For
example, a list of numbers describing a stone falling can be reduced to
a simple quadratic equation. Brain states don't seem to be this simple.)
Tom Holroyd
UUCP: {uunet,codas}!novavax!proxftl!tomh
The white knight is talking backwards.
------------------------------
Date: 15 Jun 88 16:34:15 GMT
From: wlieberm@teknowledge-vaxc.arpa (William Lieberman)
Subject: Re: Biological relevance and AI (was Re: Who else isn't a
science?)
Just to add slightly to Ben and Mike's discussion, Ben's naturally good
question about why should it be that anyone can assume that we humans on
earth uniquely possess capabilities in intellgence, etc. (i.e. the
biological system that makes us up), and Mike's reply that such an assumption
is not really made, reminds me of the question asked in a not-too-long
ago earlier age when scientists asked, 'How likely is
it that the chemistry of the world, as we know it, exists in the same
state outside the earth?'
A reasonable question. Then when helium was demonstrated to exist
on the sun (through spectrographic analysis around the 1860's??) and around
the same time when the table of the elements was being built up empirically
and intuitively, the evidence favored the idea that our local chemical and
physical laws were probably universal. As a youngster I used to wonder
why chemists, etc. kept saying there are only around 100 or so elements
in the universe. Why couldn't there be millions? But the data do suggest
the chemists are correct - with relatively few elements, such is the matter
of the universe existing. What I'm saying here is that it may be prudent
to expect not too many diverse 'forms' of intelligence around. Rough
analogy, I agree; but sometimes the history of science can provide useful
guideposts. Right now we have some sensible ideas about what it takes to
do certain kinds of analyses; but no one really knows what it takes to
enable a state of consciousness to exist, for example. One answer surely
lies in research in biophysics (and probably CS-AI).
Bill Lieberman
------------------------------
Date: Fri, 17 Jun 88 08:42:08 EDT
From: "Bruce E. Nevin" <bnevin@cch.bbn.com>
Subject: determinism a dead issue?
Is the notion of determinism not deeply undercut by developments in
study of nonlinearity and Chaos?
There is sufficient nonlinearity in the workings of brains, bodies, and
interacting agents in the world to ensure that simple billiard ball
click click click in the pocket determinism is not even an
approximation.
There seems to me a parallel to Bateson's discussion of creatura vs
pleroma, terms borrowed from Jung. If I remember correctly which is
which, creatura is the deterministic cause-effect realm amenable to
description in simple, linear, Newtonian terms; pleroma (the term
derives from a root having to do with "fullness", as in "plenary
session") involves metabolism, where outputs are not directly
predictable from inputs in terms of forces and impacts and what Bateson
elaborates as "cybernetic explanation" applies. He argued that imagery
of forces and impacts were inappropriate for most of what is important
to us. He was not aware of or at any rate did not write about
the relationship of this to nonlinearity and chaos before his death.
What is the relationship between the two? Is it the case that systems
involving nonlinearity always involve feedback or feedforward loops? My
impression from reading is yes. (Isn't it mutual effect of the values
of two or more variables on one another that makes an equation
nonlinear, and isn't that a way of expressing feedback or feedforward?
The effect of friction in a physical system varies according to
velocity, even as it affects velocity.) Is it the (stronger) case that
systems with such cybernetic loop structure always involve nonlinearity?
No, computers are generally advertised as deterministic. Is it that
nonlinear systems are not error correcting? Or perhaps that they are
analog rather than digital systems? Are massively parallel systems
nonlinear, or do they tend to be? Does the distinction apply to now
familiar characterizations of brain hemisphere specialization?
This has relevance to how an AI based on deterministic, linear systems
can do what nonlinear organisms do.
Bruce Nevin
bn@cch.bbn.com
<usual_disclaimer>
------------------------------
Date: Fri, 17 Jun 88 09:15:29 -0400 (EDT)
From: David Greene <dg1v+@andrew.cmu.edu>
Subject: Re: Cognitive AI vs Expert Systems (was Re: Me, Karl,
Stephen, Gilbert)
In article <digest.cWiAujy00Ukc40RHNs@andrew.cmu.edu>
krulwich-bruce@yale-zoo.arpa (Bruce Krulwich) writes:
>This says something about expert systems papers, not about papers
>discussing serious attempts at modelling intelligence. It is wrong to
>assume (as both you and Mr. Cockton are) that the expert system
>work typical of the business world (in other words, applications
>programs) is at all similar to work done by researchers investigating
>serious intelligence. (See work on case based reasoning,
>explanation based learning, expectation based processing, plan
>transformation, and constraint based reasoning, to name a few areas.)
Since my researchs concerns developing knowledge acquisition approaches (via
machine learning) to address real world environments, I'm well aquainted with
not only the above literature, but psych, cog psych, JDM (judgement and
decision making), and BDT (behavioral decision theory).
While I suspect AI researchers who work in Expert System might resent being
excluded from work in "serious intelligence", I think my point is that, for a
given phenomena, multiple viewpoints from different disciplines (literature)
can provide important breadth and insights.
Not an earth shattering assumption I admit, but then again, if you examine work
in the fields you suggested, you'll frequently find a very narrow scope of
references. Many of the papers I was describing come from various learning
approaches to knowledge acquisition (eg. Workshop on Knowledge Acquisition for
Knowledge Based Systems). @admittedsarcasm(Perhaps this was an unfortunate
example since these indidviduals don't qualify as representative AI
researchers.)
Actually I think the proposition is that it would be encouraging to see more AI
lit reviews which offered some viewpoints from different fields... not only
might they suggest new issues to address but they might also identify useable
solutions to be transferred.
- David Greene
dg1v@andrew.cmu.edu
------------------------------
Date: 17 Jun 88 17:28:36 GMT
From: uhccux!lee@humu.nosc.mil (Greg Lee)
Subject: Re: Biological relevance and AI (was Re: Who else isn't a
science?)
>From article <23201@teknowledge-vaxc.ARPA>, by William Lieberman:
" ...
" A reasonable question. Then when helium was demonstrated to exist
" on the sun (through spectrographic analysis around the 1860's??) and around
" the same time when the table of the elements was being built up empirically
"...
" the chemists are correct - with relatively few elements, such is the matter
" of the universe existing. What I'm saying here is that it may be prudent
" to expect not too many diverse 'forms' of intelligence around. Rough
" analogy, I agree; but sometimes the history of science can provide useful
" ...
It's not even analogous unless you have a table of intelligence. Maybe
you do. If so, how many entries does it have room for?
Greg Lee, uhccux.uhcc.hawaii.edu
------------------------------
Date: Sat, 18 Jun 88 13:27:34 EDT
From: George McKee <mckee%corwin.ccs.northeastern.edu@RELAY.CS.NET>
Subject: Consensual realities are structurally unstable
(another comment, better late than never, I hope.)
As Pat Hayes points out, the right way to interpret the phrase
"consensual reality" is as a belief system held by some group of
participants about the nature of the universe. However, given a
universe that contains more than one group and group-reality, it's
reasonable to look at the origin, scope, and structure of the different
systems and evaluate them with respect to each other. Now it's
conceivable that you may find two or more systems with equivalent
descriptive and predictive power, and with equally compact
representations in the minds of the participants, and in this situation
you might be justified in saying that there is more than one
fundamental reality. But this doesn't seem to be the case, and there
is in fact one description of the collective experience of humanity,
namely science, that clearly outranks all the alternatives in just
about any respect you may wish to examine it, except perhaps promises
of present or future happiness. This is not to say that the scientific
description of reality is complete or without weak spots, just that
it's so much better than the others that it surprises me that people
can argue against the primacy of scientific, physical reality and use a
computer at the same time.
But even leaving the content of a description of reality aside, I think
it's provable that a constructive, exterior description of the
universe, one that posits a single fundamental reality that generates
the thoughts and perceptions of each observer, is more *stable* than an
interior one that assumes the primacy of mental activity and doesn't
assume a physical origin of thought, and consequently permits the
observer to accept the validity of multiple descriptions. That is, as
long as both the interior and exterior viewpoints are sensitive to new
data, many if not all of the potential realities consistent with the
interior view are susceptible to catastrophic reorganizations triggered
by single new datums, while the single reality assumed by the exterior
view can only be incrementally modified by any single fact.
The proof of this is, as they say, "too long for this page", but one
part of it rests on the observation, implicit in Turing's proof of
universal computability, that a computer can't determine its microcode
by executing instructions. That is, a mind can't determine its
fundamental principles of operation by thinking. You have to look at
the implementation -- the hardware and microcode. For computational
minds we'll be sure to know the details of the implementation, because
we did the design. For the human mind, designed as it is by the random
processes of genetic variation and historical accident, it's very hard
to know what aspects of its structure and organization are essential or
important, and which ones aren't. But it's clear that we now have
tools that are only a quantitative step away from telling us what we
need to know about how the brain implements the mind. Those people who
say "we have no idea about how the brain works" are just announcing
their own ignorance.
The best that a mind can do by thought alone is to determine an
infinite equivalence class of possible implementations of itself. This
is apparently one of the major conclusions of Hilary Putnam's
soon-to-be-released book "Representation and Reality." It'll be
interesting to read it to find out if he's able to take the next step
and show the determination of a unique implementation of each human
mind in the brain of each individual member of H. sapiens. I sure hope
so...
- George McKee
NU Computer Science
p.s. And you thought I was going to write about Catastrophe Theory.
Not today...
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End of AIList Digest
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