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From: markh@csd4.csd.uwm.edu (Mark William Hopkins)
Newsgroups: comp.ai.philosophy
Subject: Re: Chinese Room Experiment: empirical tests
Summary: Covert propaganda ploy for neural nets.
Keywords: Adaptation, Convergence
Message-ID: <7890@uwm.edu>
Date: 27 Nov 90 02:29:27 GMT
References: <7852@uwm.edu> <15799@venera.isi.edu>
Sender: news@uwm.edu
Organization: University of Wisconsin - Milwaukee
Lines: 53

In article <15799@venera.isi.edu> smoliar@vaxa.isi.edu (Stephen Smoliar) writes:
>(Note:  I am cross-posting this to comp.ai.philosophy, since that is where
>these discussions belong;  and I hope future debate will be conducted there.)

Actually, I was going to use the experiment (which is more than just a
thought experiment) as the basis for an argument simultaneously against Searle
and advocates of strong AI showing why neurocomputers provide a better means
for realising true AI, compared to purely symbolic architectures ... and why a
machine (with a connectionist architecture) might be able to 'understand'
language, rather than just imitate it.

(I'm assuming here that Searle's position entails an argument against *any*
machine ever 'understanding' language).

(The experiment was):
>>The concept is real simple.  Try to learn a new language by imitation.
...
>>So here's what you should do: take out about 10 books written in a language
>>you don't understand (like 10 books written in Spanish)... Rewrite the exact
>>contents of each book.

>I noticed that the first reaction to this proposal was one of self-righteous
>skepticism.  Unfortunately, it is very hard to deal with this situation with
>anything other than anecdotal evidence.  Having said that, let me throw out
>two such anecdotes.

Both of your anecdotes actually relate to a general situation that you might
call learning-by-imitation, which bears an obvious relation to the ancient
method of learning-by-apprenticeship.

It seems to me that this is by far the most natural and most efficient way for
a human being to learn new information and to accquire new skills and new
expertise.  It's also obviously the oldest method, since it's the only one that
doesn't require formal schooling or reading and writing.

Symbolic architectures can only incorporate this desireable feature in a
roundabout way ... because they're not explicitly designed to adapt over
time.  On the other hand, the ability for the system to adapt itself to the
regularities of its environment is something that almost characterises
connectionist learning architectures (like backpropagation).

Another part of the experiment relates to a situation (which will eventually
occur) where you can actually become fluent or knowledgeable in an area
(particularily in a new language) without actually having the slightest idea
of what it is you're saying or doing (or why it works).

Though I expressed an opinion that an understanding of the language probably
will suddenly emerge like a bolt out of the blue, even without the aid of
translation resources, I'd have to also say that a symbolic architecture would
never be able to do this unless it's design were eventually modified in a way
that it came to more and more resemble a neural net.  There's a phenomena of
'convergence' pervading this entire experiment that in and of itself seems to
entail the existence of an underlying connnectionist architecture.