Path: utzoo!attcan!uunet!husc6!babbage!reiter
From: reiter@babbage.harvard.edu (Ehud Reiter)
Newsgroups: comp.ai
Subject: Re: Turing test and Chinese room
Message-ID: <1441@husc6.harvard.edu>
Date: 19 Mar 89 19:24:01 GMT
References: <53875@yale-celray.yale.UUCP>
Sender: news@husc6.harvard.edu
Reply-To: reiter@harvard.UUCP (Ehud Reiter)
Organization: Aiken Computation Lab Harvard, Cambridge, MA
Lines: 33

As a followup to Drew McDermott's excellent article on the Turing test
and the Chinese Room problem, let me add one small note.  There is
a certain mindset that equates intelligence with whatever humans do.
Now, we know that there are plenty of types of reasoning which humans
are pretty bad at.  Multiplying large numbers is an obvious case.
More interesting perhaps are

     - probabilistic reasoning.  Kahneman and Tversky have shown that
people make fundamental mistakes, such as ignoring priors and assuming
that p(A&B) can be greater than p(A).

     - predictive tasks.  A long literature, dating back to Paul Meehl,
shows that statistical techniques usually out-perform expert human judgement,
provided that the data is quantifiable.
(I sometimes wonder what the expert system people have to say about Meehl's
findings.  If a simple linear regression can do a better job than a human
expert, why bother building a computer system that attempts to emulate
human judgements?).

(see JUDGEMENT UNDER UNCERTAINTY: HEURISTICS AND BIASES, edited by D.
Kahneman, P. Slovic, and A. Tversky.  Especially chapters 1 and 28).

A machine that could pass the Turing test would have to be programmed to do
as badly as humans at multiplying, probabilistic reasoning, and predictive
tasks.  But is it really critical to the definition of intelligence that, say,
an entity ignore prior probabilities when making probabilistic judgements?
I doubt it, and suggest that finding out how to do a good job on the above
reasoning tasks is more important than finding out how to replicate the
mistakes humans make.

					Ehud Reiter
					reiter@harvard	(ARPA,BITNET,UUCP)
					reiter@harvard.harvard.EDU  (new ARPA)