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AIList Digest Volume 8 Issue 125
AIList Digest Monday, 14 Nov 1988 Volume 8 : Issue 125
Philosophy:
Epistemology of common sense
The study of intelligence
Computer science as a subset of artificial intelligence
Lightbulbs and Related Thoughts
IJCAI Panels
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Date: Mon, 7 Nov 88 11:21:08 EST
From: "Bruce E. Nevin" <bnevin@cch.bbn.com>
Subject: epistemology of common sense
In AIList Digest for Monday, 7 Nov 1988 (Volume 8, Issue 121), in a
message dated 31 Oct 88 2154 PST on the topic "AI as CS and the
scientific epistemology of the common sense world", John McCarthy
<JMC@SAIL.Stanford.EDU> has persuasive words for colleagues who prefer
to limit their research to things that are amenable to tidy mathematical
formulation.
The audience of "neats" he was addressing should ignore this. I want to
talk about aspects of common sense that seem even less tidy. (But there
is hope, cf. references at the end.)
JMC> Intelligence can be studied
| . . .
| (3) through studying the tasks presented in the achievement of
| goals in the common sense world.
| . . .
| I have left out sociology, because I think its
| contribution will be peripheral.
| . . .
| AI is the third approach. It proceeds mainly in computer science
There is more to common sense than the study of tasks and goals
specified in physical terms. Much of common sense involves social
facts, not just physical facts. A telltale of social facts is that they
are matters of convention. Absent intelligent agents conforming to
them, they do not exist.
Restricted to physical facts, common sense concerns things like "I can't
put the blue pyramid in the box, it's already in there" or "I can't put
the lintel on yet, I need to move the second column closer to the
first."
Suppose we had an AI equipped with common sense defined solely in terms
of physical facts. This is somewhat like the proverbial person who
knows the price of everything but the value of nothing.
We deceive ourselves when we put labels on things like "road" or
"vehicle" or even "arch" in a knowledge base. We have many expectations
and other associations with these terms that a knowledge base
lacks--unless we explicitly include those associations.
If and when we do begin to include such associations (that line defines
my lane, this is the slow-speed lane, drive on the right--unless in
England or Sweden or . . . that joker's trying to pass me in the
breakdown lane . . . this must be Boston . . . ) we are involved with
the sociology of knowledge.
Look at Erving Goffman on, say, presentation of self or interaction
rituals. Look at W. Pearce (UMass Amherst) on communication rules and
rules for constituting the social order. For starters.
An AI must be responsive as a member of the social order if it is to be
regarded as intelligent by humans. It does not need the physiological
or psychological mechanisms of humans, but it does need to understand
their conventions.
Bruce Nevin
bn@bbn.com
<usual_disclaimer>
------------------------------
Date: Mon, 7 Nov 88 10:22:14 PST
From: norman%ics@ucsd.edu (Donald A Norman-UCSD Cog Sci Dept)
Reply-to: danorman@ucsd.edu
Subject: The study of intelligence
Time for comment from a Cognitive Scientist on the appropriate
approach to the study of Intelligence.
As usual, John McCarthy has provided us with a cogent and coherent
analysis of the approaches one might take, but although his approach
appears sensible, I wish to disagree about the importance of several
aspects he downplayed.
McCarthy states:
Intelligence can be studied
(1) through the physiology of the brain,
(2) through psychology,
(3) through studying the tasks presented in the achievement of goals
in the common sense world.
True enough, except that I would add several others:
(4) through an analysis of intelligent behavior (in the abstract, as
is most frequently done in philosophy, and in some AI and
Cognitive Science endeavors)
(5) Through an analysis of how intelligent behavior results from
an interaction of individual cognition, the cognitions of
others, the social structures and cultures, and the physical
environment, [In part, what we here at UCSD call
"Distributed Cognition," which is highly related to the
recent work on "Situated Action" (See Lucy Suchman's book or
the papers of Agre and Chapman, for example).]
Real intelligence takes place as an interaction among people, in a
social environment, constrained by the particular experiences of the
participants and by the biological structures of the organism (not
just the brain, but also the sensory systems, the locomotive and
grasping mechanisms, and the whole regulatory system which interacts
dramatically with our cognitions.
Traditional analyses of intelligent behavior leave out the role of
emotions, of limited sensory and reasoning capabilities, of the
example-driven aspects of interpretation and memory retrieval and
decision making. These analyses make logical sense and can lead to
the development of intelligent machines, but they are not accurate
portrayals of human intelligence. They also (and as a direct result)
miss the creative aspect of human intelligence and fail to
characterize properly real human behavior, both the insightful
variety, and the class of things called "human error."
McCarthy talks of "common sense" but has he really studied what common
sense is about? One person's common sense is another's nonsense.
Common sense varies widely from culture to culture. I highly
recommend the paper by Geertz (an anthropologist -- one field McCarthy
left out):
Geertz, G. (1983). Local knowledge: Further essays in interpretive
Anthropology. New York: Basic Books. (Especially see the essay
"Common sense as a cultural system," pp. 73-93.)
In conclusion: John McCarthy has given a logical set of procedures to
follow in the study of Artificial Intelligence. They make sense and
will lead to advancement in the understanding of one form of
Artificial Intelligence.
But there are many possible forms of Artificial Intelligence, and it
is highly likely that dramtically different other approaches will also
prove fruitful.
However, I am interested in Real Intelligence, and for this domain,
McCarthy's approach is much too limited, for it neglects the powerful
and important contribution of biological structure, of social
interaction, of the role of cultural knowledge, and of the interaction
among individuals and the environment. We work in a world of
incomplete and erroneous knowledge, ambiguous situations and
communications, and partial specifications of all sorts, where much of
behavior is driven by the accidents of the environment or by
biological needs and limits. And almost all of our intelligent
behavior results from social interaction and by the use of artificial
artifacts (which, of course, were created by us to aid our thought and
communication proceses -- cognitive artifacts, I call them).
We can only study Real Intelligence by studying Real Organisms in
interaction with other organisms, their cultural knowledge, and their
environment.
don norman
Donald A. Norman [ danorman@ucsd.edu BITNET: danorman@ucsd ]
Department of Cognitive Science C-015
University of California, San Diego
La Jolla, California 92093 USA
UNIX: {gatech,rutgers,ucbvax,uunet}!ucsd!danorman
[e-mail paths often fail: please give postal address and all e-mail addresses.]
------------------------------
Date: 8 Nov 88 01:46:40 GMT
From: quintus!ok@Sun.COM (Richard A. O'Keefe)
Reply-to: quintus!ok@Sun.COM (Richard A. O'Keefe)
Subject: Re: Computer science as a subset of artificial intelligence
In a previous article, Ray Allis writes:
>I was disagreeing with that too-limited definition of AI. *Computer
>science* is about applications of computers, *AI* is about the creation
>of intelligent artifacts. I don't believe digital computers, or rather
>physical symbol systems, can be intelligent. It's more than difficult,
>it's not possible.
There being no other game in town, this implies that AI is impossible.
Let's face it, connectionist nets are rule-governed systems; anything a
connectionist net can do a collection of binary gates can do and vice
versa. (Real neurons &c may be another story, or may not.)
------------------------------
Date: 10 Nov 88 12:23:41 GMT
From: oodis01!uplherc!sp7040!obie!wsccs!dharvey@tis.llnl.gov (David
Harvey)
Subject: Re: Lightbulbs and Related Thoughts
In a previous article, Tony Stuart writes:
>
> On a similar track, I have often thought that once we find a
> solution to a problem it is much more difficult to search for
> another solution. Over evolutionary history it is likely that
> life was sufficiently primitive that a single good solution was
> sufficient. The brain might be optimized such that the first
> good solution satisifies the problem seeking mode and to go
> beyond that solution requires concious effort. This is an
> argument for not resorting to a textbook as the first line of
> problem solving.
>
Usually, advances by humans comes on top of what has gone before,
not inside a vacuumn. I realize that this is not exactly what you
intended to present here, but it comes out that way regardless.
As to the better solution, that usually is the way it happens.
For examples, consider Keppler seeing inconsistencies between the
model proposed by Aristotle and the calculations (just think how
much faster his work would have been with a computer!) he made.
This of course prompted him to devise a new model. Galileo and
Newton also saw inconsistencies between what was commonly believed
and the effects of gravity, ie, that accelaration was a constant
not affected by the mass of the object. Einstein saw inconsistencies
even in this model and developed the theory of relativity. In other
words, these people KNEW the textbook solutions. What characterized
them as being different from the masses is that they had the tenacity
to reject the 'textbook' solution when a better model came to mind.
Just how this can be emulated in a computer is not that easy. The
only thing that can be said is that inconsistencies of data with the
rule base must allow for a retraction of the rule and assertion for
new ones.
>
> I've often wondered about the differences between short term
> and long term memory.
>
Don't forget to include the iconic memory. This is the buffers
so to speak of our sensory processes. I am sure that you have
saw many aspects of this phenomenon by now. Examples are staring
at a flag of the United States for 30 seconds, then observing the
complementary colors of the flag if you then look at a blank wall
(usually works best if the wall is dark). There are other ways
of observing that there really is such a thing as iconic memory,
but these must be performed in a lab setting with blind studies.
I helped perform one of these at the University of Utah. How do
you implement this into your model? I don't know, and I doubt
anyone else does either since much research must yet be done to
see the relationship between iconic, short term, and long term
memory. Also, the differences between conscious and subconscious
memory processes must be considered. Much of this iconic information
makes its way into memory via the subconscious track, which I
would cite as evidence the studies being performed by various
researchers in Psychology.
You have observed the linking process that takes place in our
long term memories. This is of course a dandy model until you
begin to look at some of our links. They have some of the
following characteristics:
[1] Some of them seem to link together totally randomly.
I am sure you have observed the phenomenon that some
of your own links are rather mysterious, where the
items are not logically related at all. Nevertheless
most of them ARE logically related. Maybe we can
randomly throw in a time frame for the other links.
This of course supposes that we can prove that time
is indeed the model that determines them. By time
I mean close time proximity for the linked structures.
[2] There are a massive amount of them that we search,
sometimes in vain. As witness to this consider the
tip-of-the-tongue phenomenon that we are cursed with.
I am sure that we all have experienced it. Perhaps
those with photographic memory are not cursed with it,
but not being so blessed I would not know. Also, some
of these sturctures unlink with time and fall away.
This last tidbit of course goes against the conventional
textbook wisdom that they stay there forever.
[3] Since there are so many, we MUST use parallel processing
to search them all. Also realize that they are massive
in nature, perhaps to the point of exceeding most mass
storage devices (disks) in use today.
The short term memory does not necessarily have to have a different
data representation. It still has a linking type nature. The main
difference I see between the two is that short term memory has far
fewer links than long term. What needs to be done is to study why and
how this short term memory links up with the long term memory. Perhaps
frequency of use could be researched as the causal factor. Initially,
we must establish a linking base for these short term facts to attach
themselves to. As I see it there are several ways it will link into
the established long term memory. First of course is the logical link.
Another would be a time frame link where what was considered immediately
before or after would be what we attach it to. Also, since it is a well
established fact that we can chain things much better via poetry than
prose, rhythm and actual morphemes must be considered for chaining.
> A side effect of this model is that information in short
> term memory cannot be used unless there is a hole in the long
> term memory. This leads to problems in bootstrapping the
> process, but assuming there is a solution to that problem, it
> also models behavior that is present in humans. This is the
> case of feeling that one hears a word or phrase a lot after
> he knows what it means. Another part of the side effect is
> that one cannot use information that he has unless it fits.
> This means that it must be discarded until the long term
> memory is sufficiently developed to accept it.
>
The problem is that there are more than enough holes for something
to be fit in. Inconsistency seems to thrive in human beings. It
is only when new information conflicts enough with old that we attempt
to rationalize the two conflicting 'facts'. Unless the new information
outweighs the old in some way it never replaces it. It can and does
coexist with the old in tension in many cases. It is only when we reach
the discomfort level that we attempt to resolve the disparity of the
two in our fact base.
Well, now that I have given enough for Psychologists and AI researchers
to work on for the next 50 years (:-) I can go back to such mundane
chores as homework and sleeping. Hmm, are we going to model the
activity of sleeping in our machine?
dharvey@wscss
The only thing you can know for sure,
is that you can't know anything for sure.
------------------------------
Date: 10 Nov 88 18:36:41 GMT
From: umix!umich!itivax!ttf@uunet.UU.NET (Fejel)
Subject: IJCAI Panels
At the IJCAI Local Arrangements Committee meeting this past Friday, we
were urged to submit panel suggestions. I have noticed that the net
explodes whenever AI "infringes" on people's values. It seems that
ethical and moral issues generally stir up much interest and
controversy, though unfortunately it is often the case that more heat
than light is generated.
With this in mind, I would like to propose a panel
discussion on AI, Ethics, and Morality.
It could have three viewpoints:
1)The ethics of AI application (ie. defense-related domains).
2) The ethics of building artificial persons
(as presented by Michael LaChat)(admittedly a bit blue-sky),
and most important,
3) The view of ethics and morality as cognitive tasks,
and therefore legitimate objects of research within
the AI community.
If you're interested, and especially if you are thinking of
coming to Detroit, please email me back your comments and
suggestions, and maybe send a panel discussion request to
the IJCAI organizing commmittee.
Tihamer
P.S. Unfortunately, I am not familiar with any work being done
in this domain. Anyone have any pointers?
arpanet: ttf%iti@umix.cc.umich.edu
uucp: ...{pur-ee,well}!itivax!ttf (Tihamer T. Toth-Fejel)
Industrial Technologies Institute, Ann Arbor, Michigan 48106
work phone: (313) 769-4248 or 4345
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End of AIList Digest
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