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From: rpw3@fortune.UUCP
Newsgroups: net.ai
Subject: Re: Information sciences vs. physical sc - (nf)
Message-ID: <1978@fortune.UUCP>
Date: Wed, 14-Dec-83 23:01:52 EST
Article-I.D.: fortune.1978
Posted: Wed Dec 14 23:01:52 1983
Date-Received: Sat, 17-Dec-83 01:25:17 EST
Sender: notes@fortune.UUCP
Organization: Fortune Systems, Redwood City, CA
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#R:sdcsvax:-8400:fortune:21500002:000:998
fortune!rpw3    Dec 14 19:15:00 1983

I have to throw my two bits in:

The essence of science is "prediction". The missing steps in the classic
paradigm of hypothesis-experiment-analysis- presented above is
that "hypothesis" should be read "theory-prediction-"

That is, no matter how well the hypothesis explains the current data, it
can only be tested on data that has NOT YET BEEN TAKEN.

Any sufficiently overdetermined model can account for any given set of data
by tweaking the parameters. The trick is, once calculated, do those parameters
then predict as yet unmeasured data, WITHOUT CHANGING the parameters?
("Predict" means "within an reasonable/acceptable confidence interval
when tested with the appropriate statistical methods".)

Why am I throwing this back into "ai"? Because (for me) the true test
of whether "ai" has/will become a "science" is when it's theories/hypotheses
can successfully predict (c.f. above) the behaviour of existing "natural"
intelligences (whatever you mean by that, man/horse/porpoise/ant/...).