----- Original Message ----
From: Matt Mahoney <[EMAIL PROTECTED]>
--- Jim Bromer <[EMAIL PROTECTED]> wrote:
> I don't want to get into a quibble fest, but understanding is not
> necessarily constrained to prediction.
What would be a good test for understanding an algorithm?
-- Matt Mahoney, [EMAIL PROTECTED]
------------------------------------------------------
I don't have a ready answer for this. First of all, (maybe I do have a ready
answer to this), understanding has to be understood in the context of partial
understanding. I can understand something about a subject without being an
expert in the subject, and I am Skeptical of anyone who claims that total
understanding is feasible, (except for a bounded discussion of a bounded
concept in which case I would only be skeptical with a small s.)
So to start with, I could say that understanding an algorithm could be defined
by various kinds of partial knowledge of it. What kinds of input does it react
to, and what kinds of internal actions does it take? What kind of output does
it produce. Can generalizations of the input it takes, its internal actions and
its output be made. What was it designed to do? Can relations between
specific examples or derived generalizations of its input, its internal actions
and its output be made.
While some of this kind of knowledge would require some kind of intelligence,
others could be expressed in simpler data-concepts. Harnad's categorical
grounding comes to mind. An experimental AI program would be capable of
deriving data from the operation of an algorithm if its program was created
around this paradigm of examining an algorithm. It could then create its own
kind of analyses of the algorithm, and even though it might not be the same as
an analysis that we might create, it still might be usable to produce something
that would border on understanding.
The capacity of prediction is significant in the kind of derived
generalizations and categorical exemplars that I am thinking of, but the
concept of understanding must go beyond simple prediction.
Jim Bromer
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