On Nov 30, 2008, at 7:31 AM, Philip Hunt wrote:
2008/11/30 Ben Goertzel <[EMAIL PROTECTED]>:
In general, the standard AI methods can't handle pattern recognition
problems requiring finding complex interdependencies among multiple
variables that are obscured among scads of other variables....
The human mind seems to do this via building up intuition via drawing
analogies among multiple problems it confronts during its history.
Yes, so that people learn one problem, then it helps them to learn
other similar ones. Is there any AI software that does this? I'm not
aware of any.
To do this as a practical matter, you need to address *at least* two
well-known hard-but-important unsolved algorithm problems in
completely different areas of theoretical computer science that have
nothing to do with AI per se. That is no small hurdle, even if you
are a bloody genius.
That said, I doubt most AI researchers could even tell you what those
two big problems are which is, obliquely, the other part of the problem.
I have proposed a problem domain called "function predictor" whose
purpose is to allow an AI to learn across problem sub-domains,
carrying its learning from one domain to another. (See
http://www.includipedia.com/wiki/User:Cabalamat/Function_predictor )
In Feder/Merhav/Gutman's 1995 "Reflections on..." followup to their
1992 paper on universal sequence prediction, they make the
observation, which can be found at the following link, that it is
probably useful to introduce the concept of "prediction error
complexity" as an important metric which is similar to what you are
talking about in the theoretical abstract:
http://www.itsoc.org/review/meir/node5.html
Our understanding of this area is better in 2008 than it was in 1995,
but this is one of the earliest serious references to the idea in a
theoretical way. Somewhat obscure and primitive by current standards,
but influential in the AIXI and related flavors of AI theory based on
computational information theory. Or at least, I found it very
interesting and useful a decade ago.
Cheers,
J. Andrew Rogers
-------------------------------------------
agi
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