From: Matt Gingell (mjg223@nyu.edu)
Date: Wed Sep 08 1999 - 00:35:16 MDT
----- Original Message -----
From: Eliezer S. Yudkowsky <sentience@pobox.com>
>There's a subjective factor involved in judging between what constitutes
>a "knowledge base" and what constitutes "intelligence", but any sane
>AIer should be able to distinguish them easily. Previous AIers will
>argue interminably about philosophical definitions and triumphantly
>point out minor crossovers; also, they'll conflate the distinction
>between intelligence and knowledge with the distinction between
>procedural and declarative knowledge. The paradigm of pragmatism lets
>us ignore them, since the distinction is obvious most of the time. The
>practical distinction between intelligence and knowledge is easy enough,
>even when both take the form of declarative data. If the static data is
>used in analogies and similarity analysis, that's knowledge; if the data
>is used to direct operations on other data, if it contains the pattern
>of nontrivial procedures that can operate on data, that's intelligence.
>Again, those are just practical guidelines, not definitions.
I think you’re making a bogus distinction – whether a piece of data is knowledge
or, to use your word, intelligence depends on how it’s being used. If I pass a
string of bits to a compression algorithm, it’s data. If I feed the same string
through a Turing machine, it’s an algorithm.
To use a high level example, a concept like ‘car’ is a pattern matching elements
of the infinite set ‘cars.’ The same pattern can be used generatively to produce
instances of that set, perhaps matching constraints imposed by some creative
process. Such a pattern must surely be described algorithmically.
Elements of the learning substrate would certainly treat such a concept as data,
however. For instance, If there were two sets, one matching red cars and one
matching every other kind, the learning system would have to notice the abstract
overlap and unify the two patterns.
I don’t know why you think it’s useful to talk about classifying these things.
Learning generates knowledge which in turn influences future learning, to the
point where it’s angles on a pin to ask whether it’s data considered in the
process of learning or programs driving the learning machinery. In a very real
sense, we learn how to learn.
-matt
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