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From: lishka@uwslh.UUCP (Hang loose...)
Newsgroups: comp.ai
Subject: Re^2: Building a brain
Message-ID: <450@uwslh.UUCP>
Date: 19 Oct 89 15:00:05 GMT
References: <14079@well.UUCP> <10175@venera.isi.edu> <246@carmine9.UUCP>
Distribution: comp
Organization: U of Wisconsin-Madison, State Hygiene Lab
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schultz@cell.mot.COM (Rob Schultz) writes:
>I see several methods for shortening this process to a (nearly?) manageable
>level:

>    1.  Memory/retention.  Presumably, an intelligent machine will not
>        forget information it has learned. (This assumes we do not model
>        the system after ourselves :-)) Therefore, the machine would not
>        have to waste time re-learning something it should already know.

     The possibility exists that if a machine is patterned after the
human brain enough, it *will* forget information that was previously
entered into it.  After all, research into neural nets has shown that
it is possible to lose information put into a neural network (i.e.
forget) if too much information is fed to it.  I am not convinced that
models sufficiently close to the structure of the human brain will be
able to retain everything.

>    2.  Countinuous Input.  Such a machine will not require sleep, nourishment,
>        or any other such distractions. So, instead of losing 8 to 15 hours 
>        out of every 24, it should be able to receive continuous input of
>        information.

     How do we know this?  I believe there is still some puzzle as to
exactly what sleep provides for a person psychologically.  I have read
at least one general article that mentioned the possibility of sleep
having to do with "sorting out" or "processing" events that happened
during the day.  If this is the case, truly intelligent machines might
require sleep (or some equivalent) as well.

>    3.  Input Speed.  Information may be input directly in electronic form, 
>        thus reducing or even eliminating the time required to translate/digest
>        information. This leads to several interesting possibilities:

     Yes, this might be a possible speed up.

>    4.  Restricted Domain.  If we decide to create function-specific machines,
>        we can restrict the domain of information to the required function.
>        For example, if a system is to be a medical diagnosis/treatment
>        prescription system, it would have to learn little or nothing abou
>        meteorology. Of course, this does not help us with a general-purpose
>        system, but we can't have everything, eh? :-)
>    

     Look how long it takes humans to learn a great deal of knowledge
in a restricted domain (kindergarden -> grade school -> middle school
-> high school -> college (undergraduate) -> graduate school ).  A
restricted domain *might* save some time, but again I would think
there is still uncertainty as to what role "common sense" knowledge
has in relation to domain specific knowledge.  Does domain specific
knowledge require a firm basis of common sense knowledge to be truly
effective?  Does common sense knowledge aid in drawing conclusions in
a domain that is little prior information for?  Expert systems have a
large degree of domain specific knowledge and little common sense, and
they seem to be restricted to *very* narrow problem domains.  Is
common sense knowledge needed to stretch this.

     It seems to me that recent arguments comparing the "computing
capacity" of the human brain to computers are fairly useless.  A lot
of people seem to want to say "yeah, we have the power, now all we
have to do is create the program."  But the second part is exactly the
point: we still don't know that much on how humans think, and as the
history of artificial intelligence research has demonstrated, a lot of
the stuff that we thought was easy to duplicate (e.g. common sense
reasoning, spatial and temporal reasoning, etc.) has turned out to be
much harder.  Consider this: the brain is a massively parallel system
of neurons which are connected in a definite non-random structure.
We are *still* having problems trying to develop reasonable methods for
writing algorithms and programs that work in parallel, even though the
hardware to run parallel programs is now available.  Yet the parallel
hardware around today is very simple when compared to the human brain.
If we are having so much trouble designing parallel programs on simple
parallel structures, what makes certain people think that having more
computing power than the human brain is going to get us artificial
intelligence?  We can't even program the simple parallel systems yet,
let alone tackle a massive parallel system such as the brain.

>     rms          Rob Schultz, Motorola General Systems Group

-- 
Christopher Lishka                 ...!{rutgers|ucbvax|...}!uwvax!uwslh!lishka
Wisconsin State Lab of Hygiene                   lishka%uwslh.uucp@cs.wisc.edu
Data Processing Section  (608)262-4485                       lishka@uwslh.uucp

"What a waste it is to lose one's mind -- or not to have a mind at all.
How true that is." -- V.P. Dan Quayle, garbling the United Negro College
Fund slogan in an address to the group (from Newsweek, May 22nd, 1989)