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From: shebs@CS.UTAH.EDU.UUCP
Newsgroups: comp.ai.digest
Subject: Re: Should AI be scientific?  If yes, how?
Message-ID: <8708251656.AA14266@cs.utah.edu>
Date: Tue, 25-Aug-87 12:56:51 EDT
Article-I.D.: cs.8708251656.AA14266
Posted: Tue Aug 25 12:56:51 1987
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In article <8708240436.AA19024@ucbvax.Berkeley.EDU>
mckee@CORWIN.CCS.NORTHEASTERN.EDU writes:
>
>[...] If AI researchers
>call themselves Computer Scientists (as many of them do), they're implicitly
>also claiming to be scientists.

Not necessarily.  "Computer Science" is an unfortunate term that should be
phased out.  I wasn't there when it got popular, but the timing is right for
the term to have been inspired by the plethora of "sciences" that got named
when the govt started handing out lots of money for science in the 60s.
I prefer the term "informatics" as the best of a bad lot of alternatives.
("Datology" sounds like a subfield of history; the study of dates :-) )

>[... tutorial on scientific method omitted ...]
>	In AI, one can trace the operation of a theory that's been instantiated
>as a program, as long as there's sharing of source code and the hardware is
>the same.  This gives you operational confirmation as well as implicational
>confirmation, since you can watch the computer's "mind" at work, pausing
>to examine the data, or single-step the inference engine.

Goedel's and Turing's ghosts are looking over our shoulders.  We can't do
conventional science because, unlike the physical universe, the computational
universe is wide open, and anything can compute anything.  Minute examination
of a particular program in execution tells one little more than what the
programmer was thinking about when writing the program.

>The points of
>divergence between multiple theories of the same phenomenon can thus be
>precisely determined.  But theories summarize data, and where does the
>data come from?  In academia, it's probably been typed in by a grad student;
>in industry, I guess this is one of the jobs of the knowledge engineer. 
>In either case there's little or no standard way to tell if the data that
>are used represent a reliable sample from the population of possible data
>that could have been used.  In other sciences the curriculum usually includes
>at least one course in statistics to give researchers a feel for sampling
>theory, among other topics.  Statistical ignorance means that when an AI
>program makes an unexpected statement, you have only blind intuition and
>"common sense" to help decide whether the statement is an artifact of sampling
>error or a substantial claim.

I took a course in statistics, but you don't need a course to know that
sampling from a population is not meaningful, if you don't know what the
population is in the first place!  In the case of AI, the population is 
"intelligent behavior".  Who among us can define *that* population precisely?
If the population is more restricted, say "where native-speaking Germans
place their verbs", then you're back in the Turing tarpit.  A program that
just says "at the end" (:-) is behaviorally as valid as something that
does some complex inferences to arrive at the same conclusion.  Worse,
Occam's razor makes us want to prefer the simpler program, even though
it won't generalize to other natural languages.  When we generalize the
program, the population to sample gets ill-defined again, and we're back
where we started.

>[...] It's not easy to think of statements about the content
>of AI (as opposed to its practice or techniques) that *could* be validated
>this way, much less hypotheses that actually *have* been experimentally
>validated. Hopefully, it's my ignorance of the field that leads me to
>say this.  The best I can think of at the moment is "all intelligent systems
>that interact with the physical world maintain multiple representations
>for much of their knowledge."

This could only be a testable hypothesis if we agreed on the definition
of "intelligent system".  Are gorillas intelligent because they use sign
language?  Are birds intelligent because they use sticks?  Are thermostats
intelligent?  I don't believe the above hypothesis is testable.  Almost the
only agreement you'd get is that humans are intelligent (ah, the hubris of
our species), but then you'd have to build a synthetic human, which isn't
going to be possible anytime soon.  Even if you did build a synthetic human,
you'd get a lot of disagreement about whether it was correctly built, since
the Turing Test is too slow for total verification.

>	- George McKee
>	  College of Computer Science [sic]
>	  Northeastern University, Boston 02115

AI people are generally wary of succumbing to "physics envy" and studying only
that which is easily quantifiable.  It's like the drunk searching under the
street light because that's where it's easy to see.  AI will most likely
continue to be an eclectic mixture of philosophy, mathematics, informatics,
and psychology.  Perhaps the only problem is the name of the funding source -
any chance of an "NAIF"? :-)

							stan shebs
							shebs@cs.utah.edu