Path: utzoo!attcan!uunet!husc6!ogccse!blake!uw-beaver!fluke!ssc-vax!bcsaic!ray
From: ray@bcsaic.UUCP (Ray Allis)
Newsgroups: comp.ai
Subject: Re: Capabilities of "logic machines"
Message-ID: <8673@bcsaic.UUCP>
Date: 15 Nov 88 18:26:20 GMT
Organization: Boeing Computer Services ATC, Seattle
Lines: 93

In article <393@uceng.UC.EDU> dmocsny@uceng.UC.EDU (daniel mocsny) writes:
>In article <42136@yale-celray.yale.UUCP>, Krulwich-Bruce@cs.yale.edu
 (Bruce Krulwich) writes:
>
>[ in reply to my doubts about ``logic-machine'' approaches to learning ]
>
>> If you're claiming that it's possible to do something with connectionist 
>> models that its not possible to do with "logical machines," you have to
>> define "logical machines" in such a way that they aren't capable of
>> simulating connectionist models.  
>
>Good point, and since simulating a connectionist model can be easily
>expressed as a sequence of logical operations, I would have to be
>pretty creative to design a logical machine that could not do that.

Whoa!  Wrong!  (Well, sort of.)  I think you conceded much too quickly.
'Simulate' and 'model' are trick words here.  The problem is that most
'connectionist' approaches are indeed models, and logical ones, of some
hypothesized 'reality'.  There is no fundamental difference between such
models and more traditional logical or mathematical models; of course
they can be interchanged. 

A distinction must be made between digital and analog; between form and
content; between symbol and referent; between model and that which is
modelled.  

Suppose you want to calculate the state of a toy rubber balloon full of
air at ambient temperature and pressure as it is moved from your office
to direct sunlight outside.  To do a completely accurate job, you're
going to need to know the vector of every molecule of the balloon and
its contents, every external molecule which affects the balloon, or
affects molecules which affect the balloon, the photon flux, the effects
of haze and clouds drifting by, and whether passing birds and aircraft
cast shadows on the balloon.  And of course even that's not nearly enough,
or at fine enough detail.  To diminishing degrees, everything from
sunspots to lunar reflectivity will have some effect. Did you account for
the lawn sprinkler's effect on temperature and humidity? "Son of a gun!"
you say, "I didn't even notice the lousy sprinkler!"

Well, it's impossible.  In any case most of these are physical quantities
which we cannot know absolutely but can only measure to the limits of our
instruments.  Even if we could manage to include all the factors affecting
some real object or event, the values used in the arithmetic calculations
are approximations anyway.  So, we approximate, we abstract and model.
And arithmetic is symbolic logic, which deals, not directly with quantities,
but with symbols for quantities.  

Now with powerful digital computers, calculation might be fast enough to
produce a pretty good fake, one which is hard for a person to distinguish
from "the real thing", something like a movie.  But I don't think this is
likely to be really satisfactory.  Consider another example I like, the
modelling of Victoria Falls.  Water, air, impurities, debris and rock all
interacting in real time on ninety-seven Cray Hyper-para-multi-3000s. Will
you be inspired to poetry by the ground shaking under your feet?  No?

You see, all the ai work being done on digital computers is modelling using
formal logic.  There is no reason to argue over whether one type of logical
model can simulate another.  The so-called "neurologically plausible"
approach, when it uses real, physical devices is an actual alternative to
logical systems.  In my estimation, it's the most promising game in town.

>much like a logical machine -- pushing symbols around, performing
>elementary operations on them one at a time, until the input vector
>becomes the output vector. I have trouble imagining that is what is
>going on when I recognize a friend's face, predict a driver's
>unsignaled turn by the sound of his motor, realize that a particular
>computer command applies to a novel problem, etc.

Me, too!

>Can a system that only does logical inferences on symbols with direct
>semantic significance achieve a similar information gain through
>experience?

Key here is "What constitutes experience?"  How is this system in touch
with its environment?

>I will appreciate pointers to significant results. Is anyone making
>serious progress with the classical approach in non-toy-problem
>domains? 
[...]
>                                                         Can a
>purely logical machine demonstrate a convincing ability to spot
>analogies that don't follow directly from explicit coding or
>hand-holding?  Is any logical machine demonstrating information gain
>ratios exceeding (or even approaching) unity? Are any of these
>machines _really_ surprising their creators?
>
>Dan Mocsny

Excellent questions.  I'd also like to hear of any significant results.

Ray Allis, Boeing Computer Services, Seattle, Wa. ray@boeing.com