HDTenEightyP/NetNews huggingface dataset:
From: timc@minnie.NoSubdomain.NoDomain (Tim Brabin Cooper)
Newsgroups: sci.cognitive
Subject: Re: Wetware
Date: 29 Jul 92 23:32:45 GMT
Organization: Basser Dept of Computer Science, University of Sydney, Australia

In article <RJC.92Jul28114213@daiches.cogsci.ed.ac.uk>, rjc@cogsci.ed.ac.uk (Richard Caley) writes:
|> In article <1992Jul28.000002.20205@cs.su.oz.au>, Tim Brabin Cooper (tbc) writes:
|> 
|> tbc> The human brain may be based on simple principles, but it is
|> tbc> better than a Universal Computer.
|> 
|> That's rather a big claime. It also seems to contradict...
|> 
|> tbc> Because I believe the principles behind the brain's operations are
|> tbc> quite simple, I believe we could make a computer that does the same.
|> 

	Allow me to defend this. I meant that the human brain is more
powerful than a computer which has the hardware to perform any computation
(Turing Equivalence) but has no special software. This is because it has 
special software (or _is_ special software).  A machine which has the power to
learn any computation by being presented raw experience is better than one which
can perform any computation by being programmed. The latter, of course, can be
programmed so that it too performs the functions of a 'Universal Intelligence'.


|> Ps. 
|> 	It is rarely possible to draw a line around what a
|> `traditional AI program' can and can't do since many, if not most,
|> contain subsystems which are capable of emulating turing machines.


	But those subsystems must be programmed by someone! Where does 
the intelligence come from about how to program these Turing Equivalent
subsystems?   (The line goes around what it can do on its own, receiving
only undigested information).
Newsgroups: sci.cognitive
From: mgreen@cs.toronto.edu (Marc Green)
Subject: re: Critical Thinking in Education
Organization: Department of Computer Science, University of Toronto
Date: 25 Jul 92 12:01:07 GMT


>From: jpk@Ingres.COM (Jon Krueger) writes:

Malcolm Bauer) writes:
> I've become involved in a project to develop a curriculum for high school
> students that will help them "think critically."
> I was wondering what researh exists on this subject, especially prescriptive 
> models of the steps students need to follow, with supporting empirical
> evidence of effectivenss? Of course, other references, ideas, and suggestions 
> will be greatly appreciated as well.

-Critical thinking is simply the defining goal of a liberal arts education.
-If you believe it's a good thing research doesn't add to your support; if
-you don't you don't care if it works.  Beyond that it seems outside the
-empirical model to assemble evidence for the skill to analyze evidence.
-How would you decide when you had increased critical thinking 30%?
-Speaking as an unrepentant empiricist, I don't see how objective tests

This is a perfect example of a lack of critical thinking. Obviously
Kreuger's education must have failed miserably.

Marc Green
Trent University
From: tgee@alfred.carleton.ca (Travis Gee)
Newsgroups: sci.cognitive
Subject: mental multiplication
Keywords: mental multiplication, adults
Date: 31 Jul 92 17:48:13 GMT
Organization: Carleton University

I am posting this for a friend who need some information on mental
multiplication. Please reply to this account with a subject line that
reads Re: mental multiplication. Many thanks in advance! (Post to the
net if you feel it's vital info: I'll be checking there too)

--------------------------------------------------------------

I need some information on mental multiplication. If you are working
in the area of neural networks and parallel processing in adults,
please contact me with a brief description of what you have been
doing. My particular interest is in spread of activation models in
mental multiplication, with a focus on the restricted population of
highly skilled adults (math graduate students, engineers, etc.). Any
help will be greatly appreciated. I hope to have net access in the
fall, and will contact you by e-mail ASAP.  Thank you very much in
advance!
----------------------------------------------------------------


P.S. please use the .carleton address, unless you encounter a problem
with bouncing mail.

(((((((((((((((((((((((((((((((((((((((((((((((((((((((((( 
Travis Gee () tgee@ccs.carleton.ca      ()
           ()  tgee@acadvm1.uottawa.ca  ()        ()()()()
           ()                           ()              ()
           ()                           ()()()()()()()()()
From: adr@ecs.soton.ac.uk (Tony Roberts)
Newsgroups: sci.cognitive
Subject: Re: Sexual identification of faces
Date: 21 Jul 92 07:53:53 GMT

In <GJH.92Jul14084725@ghiggins.hpl.hp.com> gjh@hplb.hpl.hp.com (Graham Higgins) writes:

>   
>vincent@reg.triumf.ca writes, about facial recognition:

>++ What has puzzled and amazed me is how rapidly and accurately we are able to
>++ identify the gender of a person by their face.

>You might want to try checking out Vicky Bruce and Mike Burton's work on face
>recognition. They have been making significant advances in this field.

I did my PhD with Vicki on this topic - it includes some anatomical
work to show what the differences are, and some experimental work to
show how these differences are perceived. Some of the experiments are
published in:

Roberts, A. D. & Bruce, V.
Feature saliency in judging the sex and familiarity of faces.
Perception, 1988, 17, 4, 475-481

E-mail me if you like,

Cheers,

Tony R.
Newsgroups: sci.cognitive
From: timc@minnie.NoSubdomain.NoDomain (Tim Brabin Cooper)
Subject: Re: Wetware
Organization: Basser Dept of Computer Science, University of Sydney, Australia
Date: Fri, 31 Jul 1992 00:01:12 GMT


|> Wasn't it Douglas Hofstadter who noted that a grain of sand has the same
|> relationship to the sand dune (in terms of respective properties) as the
|> neuron does to the brain? 
|> 
	What are you saying?  (Is a sand dune an emergent property, or more
	a sum of its parts? There are certainly better examples of emergent
	properties).

|> 
|> I'm not so sure about the often-assumed simplicity of neuronal operations.
|> The structure and function of synapses, neurons, and small connections of 
|> neurons is much more complex than is generally accounted for in current 
|> NN simulations. 

	I believe that simple neuronal models are sufficient for all the things
people want out of AI & Connectionism, and that it's all in their interaction
and in the learning rules. What evidence can you give to support your view
that more sophisticated models are needed?

|> 
|> In sum, I think perhaps that researchers twenty years from now will
|> look at our current clunky models ("backprop? you must be joking!")
|> as being as foolishly simplistic as we see the "classical AI" models
|> of twenty years ago ("Mycin? you must be joking!").  

	Yeah.
|> 
|> > 	Like the rest of the Neural Net community, I'm holding my breath
|> > for a big breakthrough (& working on one myself).
|> 
|> Good luck!  Care to share any of your expectations and hunces with us?
|> 
	I'm working in clustering & concept formation & creation of neural
nets out of that....specialising in deep networks that do things like
character recognition & speech recognition.  I'm hoping I or someone else
will come along with a great new learning rule that creates neural nets
capable of competing with humans in things like recognising handwriting.
I'm hoping it will be some really nice & simple learning rule that generalises
to a large set of behaviours.

			Tim Cooper.
Newsgroups: sci.cognitive
From: tom@cs.su.oz.au (Thomas James Jones)
Subject: concepts...
Reply-To: tom@cs.su.oz.au
Organization: Basser Department of Computer Science
Date: Wed, 29 Jul 1992 05:44:28 GMT

	defn of abstraction/conceptualization:

	To conceptualize something is surely to reduce the
	amount of space required to store it, for example 
	representing a keyboard/terminal/disk/cpu as a 
	computer.  If the abstraction does not immediately
	meet this requirement, then perhaps it has a simple
	mapping to a phrase that does.


	any suggestions?


	tom
  

Keywords: 

Newsgroups: sci.cognitive
From: frank@i1.msi.umn.edu (Loren Frank)
Subject: Re: concepts...
Organization: University of Minnesota
Date: Wed, 29 Jul 1992 22:01:57 GMT

tom@cs.su.oz.au (Thomas James Jones) writes:

>	defn of abstraction/conceptualization:

>	To conceptualize something is surely to reduce the
>	amount of space required to store it, for example 
>	representing a keyboard/terminal/disk/cpu as a 
>	computer.  If the abstraction does not immediately
>	meet this requirement, then perhaps it has a simple
>	mapping to a phrase that does.


>	any suggestions?


>	tom
>  

It seems to me that an abstraction does not truly reduce the amount of space
required to store it. Assuming that you starts with keyboard/terminal/disk/cpu
as four spearate concepts, you continue to have a concept for each of those
items when you add the higher level concept of computer. Instead of replacing
the memory space required to store all of the separate items, conceptualizing
adds the "chunked" concept of computer to all of the rest of the stored 
concepts. If that were not the case, we would not be able to say that a computerwas made up of a keyboard/terminal etc.
	Instead of saving on memory space, I would imagine that concepts or 
abstractions save processing time (assuming that we want to stick with the
serial computer metaphor). Abstractions allow the brain (or whatever) to 
reference a concept without being forced to reference all of the lower level
concepts associated with it. Or at least that is my best guess...


Loren Frank
From: timc@minnie.NoSubdomain.NoDomain (Tim Brabin Cooper)
Subject: Re: Wetware
Organization: Basser Dept of Computer Science, University of Sydney, Australia
Date: Tue, 28 Jul 1992 00:00:02 GMT

In article <1992Jul23.090231.24605@cs.su.oz.au>, tom@cs.su.oz.au (Thomas James Jones) writes:
|> 	Is intelligence an emergent property of 	
|> 	reducable (and electronically reproducable)
|> 	biological matter (ie neurons), or is our
|> 	brain more than the sum of many explicable parts?
|>
	The brain is the _product_ of many explicable parts, i.e. has properties
which you don't get even in minute quantities in the parts. In other words,
Yes to the first one.
	This means that AI can theoretically produce intelligence. Of course,
I believe it'll be the Connectionist side of AI, rather than the AI part of AI,
that does it.
	All the traditional AI programs that have been written, are such that
you can draw a clear line around what they can do & what they can't. The human
brain may be based on simple principles, but it is better than a Universal
Computer. It is like a 'Universal Intelligence' - it can learn operations by
being given experience in a very raw form, and by learning from them, rather
than by being programmed.
	Because I believe the principles behind the brain's operations are
quite simple, I believe we could make a computer that does the same.
	
	Like the rest of the Neural Net community, I'm holding my breath
for a big breakthrough (& working on one myself).

|> 	If the former, then AI can produce intelligence,
|> 	if the latter, then AI can never fully succeed?
|> 
|> 	tom

	Thanks for the posting, Tom.

			Tim Cooper, Sydney.
From: frank@i1.msi.umn.edu (Loren Frank)
Newsgroups: sci.cognitive
Subject: Re: <none>
Date: 31 Jul 92 17:59:13 GMT
Organization: University of Minnesota

swann@divsun.unige.ch (SWANN Philip) writes:


>This proposal is like claiming that we can understand Unix
>by simulating a specific computer hardware architecture in
>software: just because Unix runs on a 386 or whatever doesn't
>mean you`ll learn *anything* about Unix by laboriously
>imitating the 386. To understand Unix you have to look at
>the history of computing, at the types of problems people
>want to solve using computers etc. etc.

>philip swann
>university of geneva

Forgive me, but Huh? I understand your point, that understanding the hardware
level does not get one very far in terms of understanding the software, but
what is this in reference to?

Loren
From: pollack@dendrite.cis.ohio-state.edu (Jordan B Pollack)
Newsgroups: sci.cognitive
Subject: Re: Questions about SOAR
Date: 27 Jul 92 20:27:46 GMT
Reply-To: pollack@cis.ohio-state.edu
Organization: Ohio State Computer Science
Originator: pollack@dendrite.cis.ohio-state.edu

where did you get the tape?  I would have liked to watch the lectures
as I read the book, and sometimes wish there was a "science access
Cable channel which ran videos of famous scientific lectures...


-- 
Jordan Pollack                            Assistant Professor
CIS Dept/OSU                              Laboratory for AI Research
2036 Neil Ave                             Email: pollack@cis.ohio-state.edu
Columbus, OH 43210                        Phone: (614)292-4890 (then * to fax)
Newsgroups: sci.cognitive
From: rbody@cuug.ab.ca (Richard Body 233-7639)
Subject: Re: Position of conciousness
Keywords: Consciousness, Mind, Dennett
Organization: Calgary UNIX Users' Group
Date: Thu, 23 Jul 1992 04:59:42 GMT

	I think your question about consciousness' position in the
head is indeed appropriate to this newsgroup. A long-time philosopher
of mind from Tufts University has used this question as a starting
point in his recently-published book

"Consciousness Explained" by Daniel C. Dennett 1st edition.

	He explores this illusion and others related to what he calls
the "Cartesian Theatre". I'll reveal the jist of his argument by
quoting his summary on page 254:

	There is no single, definitive "stream of consciousness",
because there is no central Headquarters, no Cartesian Theater where
"it all comes together" for the perusal of a Central Meaner. Instead
of such a single stream (however wide), there are multiple channels in
which specialist circuits try, in parallel pandemoniums, to do their
various things, creating Multiple Drafts as they go. Most of these
fragmentary drafts of "narrative" play short-lived roles in the
modulation of current activity but some get promoted to further
functional roles, in swift succession, by the activity of a virtual
machine in the brain. The seriality of this machine (its "von
Neumannesque" character) is not a "hard-wired" design feature, but
rather the upshot of a succession of coalitions of these specialists.

	The basic specialists are part of our animal heritage. They
were not developed to perform peculiarly human actions, such as
reading and writing, but ducking, predator-avoiding, face-recognizing,
grasping, throwing, berry-picking and other essential tasks. They are
often opportunistically enlisted in new roles, for which their native
talents more or less suit them. The result is not bedlam only because
the trends that are imposed on all this activity are themselves the
product of design. Some of this design is innate, and is shared with
other animals. But it is augmented, and sometimes even overwhelmed in
importance, by microhabits of thought that are developed in the
individual, partly idiosyncratic results of self-exploration and
partly the designed gifts of culture. Thousands of memes, most borne
by language, but also by wordless "images" and other data structures,
take up residence in an individual brain, shaping its tendencies and
thereby turning it into a mind.


	I recommend this book for anyone interested in consciousness
in general, but in particularly for researchers looking for philosophical
framework in which to link artificial and natural consciousness.

Newsgroups: sci.cognitive
From: timc@minnie.NoSubdomain.NoDomain (Tim Brabin Cooper)
Subject: Re: What is "cognitive science"?
Organization: Basser Dept of Computer Science, University of Sydney, Australia
Date: Tue, 21 Jul 1992 01:27:25 GMT

|> What is cognitive science?

	Natural & Artificial Intelligence     ?
Newsgroups: sci.cognitive,sci.psychology,sci.image.processing
From: loverock@fraser.sfu.ca (David Sean Loverock)
Subject: Image analysis and object categorization
Summary: Image analysis and object categorization
Keywords: Morphometrics/shape (2-D outlines)/category learning
Organization: Simon Fraser University, Burnaby, B.C., Canada
Date: Fri, 31 Jul 1992 07:27:56 GMT

Recently a number of morphometric techniques for the quantitative
analysis of shape have appeared in the Botanical and Zoological
literature.  Both 2-D outline (e.g., Fourier analysis) and landmark
(e.g., Truss analysis) approaches have been reported.  A technique
combining both of these approaches has also been reported (Landmark
Eigenshape Analysis).
I am interested in examining the role of shape (treated as a quantitative
rather than as a qualitative dimension) in category learning.  By using 
one of the above techniques I would hope to characterize very subtle and 
difficult to describe differences in shape and relate these to subject
performance in a category learning task.  Stimuli could involve 2-D outlines
of Botanical (e.g., Red Oak vs White Oak leaves) or Zoological (e.g., wings
of different species of mosquitoes) objects.
As my ideas on this project are just at the formative stages, answers to 
the following questions or any general comments would be greatly appreciated

Existing literature on quantitative analysis of 2-D shape and categorization?

Shape image analysis and psychophysics (jnd's)?

Other potential applications involving quantitative shape analysis within
the domain of Cognitive Psychology (involving human subjects as opposed
to artificial systems)?

Please reply to:
loverock@sfu.ca
Thanks in advance!
From: mike@isgtec.com (Mike Sellers)
Newsgroups: sci.cognitive
Subject: Re: Wetware
Date: 29 Jul 92 20:12:36 GMT
Organization: ISG Technologies Inc., Mississauga Ontario

Tim Brabin Cooper writes:
> Thomas James Jones writes:
> |> 	Is intelligence an emergent property of 	
> |> 	reducable (and electronically reproducable)
> |> 	biological matter (ie neurons), or is our
> |> 	brain more than the sum of many explicable parts?
> |>
> 	The brain is the _product_ of many explicable parts, i.e. has properties
> which you don't get even in minute quantities in the parts. In other words,
> Yes to the first one.

Wasn't it Douglas Hofstadter who noted that a grain of sand has the same
relationship to the sand dune (in terms of respective properties) as the
neuron does to the brain? 

> 	This means that AI can theoretically produce intelligence. 
> [...]
> 	Because I believe the principles behind the brain's operations are
> quite simple, I believe we could make a computer that does the same.

I'm not so sure about the often-assumed simplicity of neuronal operations.
The structure and function of synapses, neurons, and small connections of 
neurons is much more complex than is generally accounted for in current 
NN simulations.  For example, small groups of neurons containing gated 
feedback loops (A excites B which excites C which inhibits A unless 
excited by D) are common; yet this is beyond current theory and practice
in the NN community.  Also, the dynamic properties of synapses are nowhere
near as simple as the computer-esque binary model imposed upon them, and 
subtle factors such as spatial summation on a neuron are below the level 
of granularity of current models.  This is not to say that such intracies
will not be accounted for as the field moves on, but only that the level
of simplicity we see is born more of our ignorance and technical limits
than it is of the actual simplicity of the brain.  

In sum, I think perhaps that researchers twenty years from now will
look at our current clunky models ("backprop? you must be joking!")
as being as foolishly simplistic as we see the "classical AI" models
of twenty years ago ("Mycin? you must be joking!").  

> 	Like the rest of the Neural Net community, I'm holding my breath
> for a big breakthrough (& working on one myself).

Good luck!  Care to share any of your expectations and hunces with us?

> 			Tim Cooper, Sydney.

--
                    Mike Sellers       mike@isgtec.com
                        User Interface Team Leader
                    ISG Technologies, Toronto, Canada
            "Actum ne agas" -- Do not do what has been done.
From: rjc@cogsci.ed.ac.uk (Richard Caley)
Newsgroups: sci.cognitive
Subject: Re: Wetware
Date: 28 Jul 92 10:42:13 GMT
	<1992Jul28.000002.20205@cs.su.oz.au>
Organization: Human Communication Research Center
In-reply-to: timc@minnie.NoSubdomain.NoDomain's message of 28 Jul 92 00:00:02 GMT

In article <1992Jul28.000002.20205@cs.su.oz.au>, Tim Brabin Cooper (tbc) writes:

tbc> The human brain may be based on simple principles, but it is
tbc> better than a Universal Computer.

That's rather a big claime. It also seems to contradict...

tbc> Because I believe the principles behind the brain's operations are
tbc> quite simple, I believe we could make a computer that does the same.

Ps. 
	It is rarely possible to draw a line around what a
`traditional AI program' can and can't do since many, if not most,
contain subsystems which are capable of emulating turing machines.

--
rjc@cogsci.ed.ac.uk				_O_
						 |<
From: lever@voyager.corp.sgi.com (Karen Lever)
Newsgroups: sci.cognitive
Subject: Free Classes in Tantric Meditation
Keywords: consciousness, meditation, Enlightenment
Date: 30 Jul 92 08:27:27 GMT
Followup-To: poster
Distribution: ba
Organization: Silicon Graphics, Inc.
Originator: root@voyager.corp.sgi.com

Free intensive workshops will be offered in the Bay Area:

Stanford University                       U.C. Berkeley
    Thursday, July 30                         Monday, August 3
    Thursday, August 6                        


Topics include: Meditation, Buddhism, Power, Balance, Wisdom,
Enlightenment, Personal Happiness, Reincarnation, Career Success
and Miracles.  Classes consist of discussion and meditation, 
and are designed for individuals age 29 and under. 

Stanford classes will be held in the Escondido Village Center 
(next to the E.V. office), from 7:30 to 9:30 p.m.

U.C. Berkeley classes will be held in Wheeler Hall, Room 223,
from 7:30 to 9:30 p.m.

Additional classes will be offered in the Bay Area this summer
and fall.  For information, please call 415-995-4721.
Newsgroups: sci.cognitive
From: pautler@ils.nwu.edu (David Pautler)
Subject: source for Wm. James Lectures tapes
Organization: The Institute for the Learning Sciences
Date: Tue, 28 Jul 1992 17:43:13 GMT

Some people have expressed an interest in viewing the Allan Newell
1987 William James Lectures I mentioned yesterday.  I borrowed the
tapes from a friend who got them from another friend, and neither of
us is sure of any commercial or university source.  But the Harvard
University Bookstore is probably a good place to try.

One warning: the quality of the video I saw made many of the overhead
projections nearly illegible.  There are also short gaps, and no coverage
of the question-and-answer sessions.

	-dp-
Newsgroups: sci.cognitive