Path: utzoo!utgpu!news-server.csri.toronto.edu!bonnie.concordia.ca!uunet!wuarchive!psuvax1!psuvm!djg3
From: DJG3@psuvm.psu.edu
Newsgroups: comp.ai.philosophy
Subject: Re: Turing Test: opinions on an idea
Message-ID: <91150.092953DJG3@psuvm.psu.edu>
Date: 30 May 91 13:29:53 GMT
References: <1577@ucl-cs.uucp>
Organization: Penn State University
Lines: 64

In article <1577@ucl-cs.uucp>, G.Joly@cs.ucl.ac.uk (Gordon Joly) says:
>
>D. Gilman <DJG3@psuvm.psu.edu> writes
>>> The model/reality distinction looks to be another version of Searle's
>>> argument that AI systems merely simulate intelligence and do not
>>> instantiate it (though I'm not sure about what you're after in
>>> suggesting that AI models can only *approximate* genuine intelligence).
>
>All models are "inexact"; they must be falsifiable. Newton's gravity
>all that is needed for terrestrial calculation. Some experiments have
>been performed, but most are done in space. The Einstein view of
>gravity, General Relativity (GR) is rarely apparent or needed; it did
>however give reason for the precession of the perihelion of Mercury.
>There is the lower gravitational field Newtonian limit to GR.
>Philosophically however, they are poles apart.
>
What's the connection between models being inexact and being falsifiable?
I take it that the first point has to do with the fact that models
typically are idealized stand-ins for complex, variable or otherwise
difficult to observe real-world phenomena.  Or are you just thinking
that models can only be based upon measurements accurate to some degree
of specificity?  My problem here is one of not knowing what the measure
is supposed to be for intelligence.  Aren't we thinking of something
like rough performance standards for disparate problems posed, and
tasks taken up, in different environments?  And couldn't a model meet
these sorts of strictures (allowing that for actual instantiation of
intelligence we'll at least need to add some ability to run the model on
a system which provides for some sort of interface with a larger world,
and not just a model) just by being in the right ballpark and not by
perfectly matching some elusive particular values of genuine
intelligence?  The second point--falsifiability--has to do with our
wanting models to be subject to empirical tests.  Here we want only
the potential of bad fit with data, not the necessity of bad fit due
to inexactitude inherent to modeling, no?  And lots of models don't
seem falsifiable per se;  we're frequently more concerned with criteria
such as accuracy and utility--and these come in degrees--than with
truth or falsehood.

>>> One difference between AI models and the physics models to which you
>>> refer is that AI models--certain of them at any rate--can be run.  What
>>> Searle has no idea about in claiming that AI simulations are missing
>>> essential *biological* features of genuine intelligence is just what
>>> sorts of biological phenomena are essential to thought; without these
>
>Penrose claims it is the quantum effects of a real, very compact,
>bio-system like the brain that gives (human) intelligence/self-awareness.
>
I haven't read P's book.  Does he have an account of how the quantum
effects in such a system might give rise to (human) intelligence and
self-awareness or is he just stuck with a fancier (or micro) version
of Searle's problem (something like, I'm convinced that the difference
is right here but I don't know why)?

>>> it's hard to fathom his conviction about the missing stuff being
>>> essentially biological.  If AI models--running ones--cannot have the
>>> right stuff (or if, as mere approximations, they cannot have the
>>> right values) then what exactly is missing, or holding them back?
>
>Good question...
Having already used a ton o' space I'll leave off the last part.  I
don't think I understand your response to my remark about models and
exemplars.  It's probably not important but I'd be happy to try again
if you want to pitch it a different way.
D. Gilman