🚀 go-pugleaf

In article <2al902Zg8bnn01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes:
>The specifications for the analog hardware must be finitely
>stated, the accuracy of the construction can be verified only down to
>a finite tolerance, the components' behavior can be predicted with only
>finite accuracy, and the initial state can be measured with limited
>precision.  So if there is something very very small, but very very
>important about being born (for example), and we don't notice it, and
>therefore we can't give the same property to something that we build,
>then it won't help us at all to have a non-symbolic AI.  If nobody
>noticed and described "that certain something", then no way can we build
>it into anything.  But if we can build it, we can also program it.

I disagree. The analog builders might "get lucky," and be able to
reproduce their success even though they can't explicate any
programmable underlying mechanism. Remember, *loads* of fine
engineering with real-world machines has preceded solid theoretical
models of what is going on. (The compass, the optical telescope,
gunpowder, rockets, selective breeding, public sanitation, civil
engineering in the ancient world, etc., in short, almost everything
discovered before the Second World War.)

You don't have to have any idea of how to simulate combustion before
you can invent gunpowder and conquer nations. All you have to know is
how to find white crystals in certain caves, a yellow funny-smelling
substance in certain rocks, how to roast logs down to charcoal, and a
repeatable formula for mixing these things together. Your recipe for
gunpowder is a reproduceable, logical description of the process, but
it is nowhere near complete enough *by* *itself* to allow a computer
to reproduce much of the sensory information an observer would obtain
from watching you mix up a big batch of gunpowder and set a match to
it.

The real world contains many useful phenomena that give us an enormous
head start in the game of artificially creating complex sensory
experiences. Trying to recreate the same sensory experiences via
logical computation and general-purpose actuators is vastly harder,
which is why our efforts to virtualize greatly lag our success at
actualizing. We may consider the real world to be like one massive
ongoing computation, with many hooks for us to get in and call
subroutines we don't comprehend beyond their interfaces.

Even in this day of computing machines and extensive theories,
experiment still leads theory at least as often as otherwise. Consider
the recent example of high-temperature superconductors. A viable
industry may emerge without much of a theoretical model. And it will
have ample precedent. In my field (chemical engineering) everything is
curve fits and correlations. (Thumb through the _Chemical Engineer's
Handbook_ the next time you get bored with determinism. The extent to
which our economy depends on practically uncomputable phenomena is
rather appalling.)

I concede that the likely complexity of an intelligent machine greatly
lowers the probability that a dirty-handed empiricist could build one
by accident. But I don't think empiricism and theory have to be so 
nicely interchangeable as you imply, especially in the short run.

In the long run, who knows? Can any phenomenon be so truly uncomputable
that no logical process could behave equivalently (if not exactly)?
The presence of such phenomena would seem to imply a universe where
theory is of no value at all. I hope we all agree that's not true... :-)

Dan Mocsny
dmocsny@uceng.uc.edu