🚀 go-pugleaf

In article <3750@uceng.UC.EDU> dmocsny@uceng.UC.EDU (daniel mocsny) writes:
>In article <2al902Zg8bnn01@amdahl.uts.amdahl.com> kp@amdahl.uts.amdahl.com (Ken Presting) writes:
>> . . . 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. . . .
> . . .  (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.)

Here (and below) I think I can salvage some useful conclusions from my
original argument.  I had focused on Penrose' arguments, which depend on
the possible existence of uncomputable functions in the laws of physics.
But I propose a simulation technique which depends on deduction rather
than numerical solution of differential equations. The standard
logical operation which represents the determinist metaphysical thesis
is *deduction*, not computation.  Of course, computers are entirely
adequate to implement deductive systems, by the Church-Turing thesis.
If there is any way at all to predict a future state from a past state,
computers can do it as well as any mere physicist.  Using deduction
doesn't really change the power of the simulator, it just puts the focus
on a different issue.

I do think this eliminates any concerns with the computability of the
functions which are used to state the laws, given that the laws are
stated in a complete logic. Recursive enumerability is sufficient,
supposing that real-time performance is not required, or that machines
with non- deterministic performance exist (such as associative memory).
The computability or uncomputability of every other process is irrelevant.

Even if we lack a theoretical account of a measured phenomenon, so that
we are unable to deduce our measurements from a more general theory, we
still have the expedient of adding the tabulated data points themselves
to the data base of a simulation.  One point I am urging against Penrose
is that whatever the scientists can state about the phenomenon we want
to model, AI workers can implement, because deduction itself is a
computable process.

Penrose goes on to suppose that non-deductive processes have a role in
mathematics and science, but that is a different subject, with huge
controversies of its own.  Hypothesis formation is non-deductive, but
you need deductions from the hypothesis to get testable predictions,
and it's the test of the hypothesis that gets published.  The only
relevant non-deductive process I can think of is measurement, which
causes no problems for my argument.  Quite the contrary:


>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.

This is why I made a very big deal out of *measuring* the robot.  In
engineering, theory most often has a background role if anything, and
need have no role at all.  As you observe, the bang is no smaller for
being unexplainable.  But a measuring device without a theoretical
explanation is no measuring device at all - it's just another mysterious
correlation!  This point is crucial to my argument.  Penrose can speculate
at will about possible laws.  But if he is presented with a robot, and
wants to criticize its behavior, he'll need some sort of observation on
which to base the criticism.  If he knows how to make the measurement,
then there is a way to simulate the same effect.

This dependency of measurement on theory holds pretty well for complex
measuring devices, but stops irritatingly short of providing a rationalist
reduction of empiricism (it irritates me, anyway).  And I should grant
right away that a device (X-rays are a good example) can make useful
measurements before it's explained by the theorists, once the
experimentalists have a resonably thorough description of its
performance. But a new form of measurement is not going to topple any
old theories until it's explained by a new theory itself.

(Via E-mail, Dan made further comments, which I'll address indirectly
here.  btw, Dan, that letter made my day!)

It is possible to finesse the actuator issue by restricting the observer
in a Turing test to a limited interface with the simulator.  Dan suggests
using the simulator to drive a light and sound source, thus reproducing
most of an observer's experience of interacting with a person.  This
would be like substituting a "virtual reality" environment for Turing's
original teletype.  I have considered an even more radically restricted
interface - present the "observer" with two sets of descriptions, one set
generated by the simulator, and the other set recorded by hand by
technicians watching, measuring, X-raying, etc a live human.  The observer
then must decide which is the description of the real person versus the
output of the simulator.  The observer is allowed to specify any
situation, any measurement, any dialogue, to his heart's content.  The
technicians who report on the real human are required to couch their
reports in the same notation as used by the simulator.

My approach suffers severely from the "simulations don't fly" problem,
which is why I stayed with the actuators.  But any proposal for a success
condition for AI which involves restricting the observations allowed to
the judges has a deeper problem.  In order to claim that "observations
of types X, Y ... provide adequate information to determine the presence
of thought in the observed system" it is necessary to show that other
types of observations are irrelevant.  For example, the familiar Turing
test says "Observations of language behavior are sufficient...".  This
is OK if you can show that (at least some aspect of) thought is
independent of all the other things people do, and all the other
attributes they posess.

But you *can't* do that until you have some independently motivated
account of what thought is.  I don't think AI is going to get the
amateur speculators off its back (and out of the general press)
until there is a good reason for saying "We don't care that our duck
don't waddle.  It's STILL a duck".  Or else settle for Weak AI.

<dismount soapbox>


>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... :-)

Agreed!  I'd emphasize deduction as the crucial logical process, rather
than computation.  And we have to be quite forgiving wrt the time scale
of the simulation (cf Dunning's comments on chaotic systems), or else
focus on the finiteness of the equivalency-checking process.

I have a hard time believing that we could ever convince ourselves that
we have discovered an absolutely non-simulable process.  I bet that if
a candidate ever arose, there'd be lots of talk about "hidden variables"
or some such nonsense... :-}

>Dan Mocsny
>dmocsny@uceng.uc.edu

Thanks again for a very stimulating article.

Ken Presting