Eugene Eric Kim wrote:
> IBM's e-business Management Services has announced business software
> that has some ability to "self-heal":
> The Deep Blue approach strikes me as being similar to Doug Lenat's Cyc
> approach, which is to give the computer as much information as
> possible so that it can make "intelligent" decisions. The problems in
> both systems is that the information must be structured in a
> highly-constrained manner.
> The question is, how do we take terabytes of mostly unstructured
> information, and structure it so that high-powered computers can do
> intelligent things with them? Having a small group of people spend
> decades manually structuring that information, like Lenat's team has
> been doing, is not a very scaleable solution.
Lenat's exciting assertion (the truth of which remains to be proven)
is that there is a certain threshhold at which the system is smart
enough to begin filling in gaps in its knowledge on its own -- to
recognize what it needs to know, and either ask for it or find it
from sources at its disposal.
He stated that Cyc had just now "crossed that threshhold", and held
out the promise that knowledge acquisition was now at the "take off"
point in the exponential curve.
I think the brute-force approach compensates for the kind of
"conceptual chunking" that creates structure by grouping together
similar things, and then building on that partial ordering to create
It is fascinating to contemplate a cyc-style version of Deep Blue.
It should start "overlooking things", the same way a person does,
by slighly inaccurate categorization, after which analysis should
lead to an improved categorizing strategy that rectifies the oversight.
Basically, the answer to "how do we structure terabytes of information",
is with cyc, topic maps, or RDF -- with meta-information that builds
a network of interrelationships, and applications which can deal with
that meta-information to:
a) Find the information you need
b) Map the new ontological framework into an ontology you are
familiar with (the essence of teaching)
I confess to finding item (b) particularly fascinating. If my personal
knowledge base has an ontology of things I know about, and I want
to learn about some new thing, then the system can map the new
ontology into terms I'm familiar with, constructiing analogies to help
me "get it". It would figure out where to start by looking for central
concepts in the new ontology that have similar structures in the
ontologies I know. It would then build outward, adding more concepts.
In such a system, the teaching process would be crafted to suit
the individual, building on familiar things as much as possible to
introduce new ones.
For example, to teach Java to a C programmer, a lot of the
constructs are an exact match. But teaching the object oriented
aspects of the language requires an appeal to analogies taken
from life (like cars), since the C language doesn't have a whole
lot of similar constructs.
But suppose the person had built a routine that functioned like
an object! By inspection of the person's personal knowledge
base, the system could recognize the object-orientedness of
that example, and use it to give the person new terms for
concepts they had already intuited.
Or the system might discover very little familiarity with cars, and
instead build examples based on totally different kinds of examples.
I think we're a long way from being able to do those things, but
those are the kinds of things that I suspect will become possible
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This archive was generated by hypermail 2.0.0 : Fri Nov 02 2001 - 08:15:51 PST