Path: utzoo!utgpu!attcan!uunet!husc6!mailrus!tut.cis.ohio-state.edu!uccba!uceng!dmocsny
From: dmocsny@uceng.UC.EDU (daniel mocsny)
Newsgroups: comp.ai
Subject: Re: Capabilities of "logic machines" (was Re: Limits of AI)
Summary: On explicit programming
Keywords: Intelligence
Message-ID: <393@uceng.UC.EDU>
Date: 7 Nov 88 03:41:20 GMT
References: <1651@ndsuvax.UUCP> <349@uceng.UC.EDU> <42136@yale-celray.yale.UUCP>
Organization: Univ. of Cincinnati, College of Engg.
Lines: 78

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.
(By ``logical machine,'' I mean any algorithmic device with sufficient
generality to implement any of the instances you cited in your
article.)

I have a vague concept of a ``universal computer,'' gleaned from the
occasional Wolfram or Hopfield paper, distorted somewhat through the
transfer function of my inadequate understanding, but retaining some
conceptual utility nonetheless. A sufficiently capable computer,
whether based on a Von Neumann or PDP model, should be able to
simulate all other computers, given enough time and memory. A machine
works best in its own ``native mode,'' but that does not limit all the
things we might kludge it up to do. An occasional human brain can
(under appropriate duress) be made to operate at least momentarily
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.

Upon a microsecond's reflection I must admit that all connectionist
models require explicit programming of some sort. Before they can
start learning, someone must specify their structure, to ``get the
ball rolling,'' so to speak. Indeed, our own brains start off with
explicit genetic programming. The difference, I suppose, is all in the
amount of programming required, compared to the total information
gain. The information content of the human genome is ~750 MB, of which
a sizable fraction determines our basic brain structure. The human
brain goes on to absorb a terrific amount of information during its
service life. (Terabytes? With electric stimulus, your brain can
recall past experiences in vivid detail -- sights, sounds, smells,
textures. If you've done any graphics or audio work, you'll know
that's scary.)

Can a system that only does logical inferences on symbols with direct
semantic significance achieve a similar information gain through
experience? Can we really, truly, specify a set of logical constructs
that will fit on a Maxtor, turn it loose in the real world, and have
it come back twenty years later to regale us with its discoveries?  

> On the other hand, I think your claim is incorrect even if 
> simulating connectionist models on "logical machines" is ignored.

Time will tell. I long to be proven wrong. I would dearly love to have
a computer that was not so brittle and helpless as the ones to be had
today. I hope that I did not sound too critical of logical machines in
my earlier post. I did say that they have many strengths where we have
weaknesses. But the original question was whether they would exceed
human intelligence. And that is a very tall order.

> it looks as if you are unfamiliar with 
> any recent work in the more "classical" areas of AI (ie, machine learning, 
> case based reasoning, etc).

I will appreciate pointers to significant results. Is anyone making
serious progress with the classical approach in non-toy-problem
domains? (One serious problem with the logical machine approach is
that the bigger these systems get, the more likely they are to
collapse.  Success in toy domains is not easy to scale up.) 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