I have to say that I am proud of David Jone's efforts.  He has really
matured during these last few months.  I'm kidding but I really do respect
the fact that he is actively experimenting.  I want to get back to work on
my artificial imagination and image analysis programs - if I can ever figure
out how to get the time.

As I have read David's comments, I realize that we need to really leverage
all sorts of cruddy data in order to make good agi.  But since that kind of
thing doesn't work with sparse knowledge, it seems that the only way it
could work is with extensive knowledge about a wide range of situations,
like the knowledge gained from a vast variety of experiences.  This
conjecture makes some sense because if wide ranging knowledge could be kept
in superficial stores where it could be accessed quickly and economically,
it could be used efficiently in (conceptual) model fitting.  However, as
knowledge becomes too extensive it might become too unwieldy to find what is
needed for a particular situation.  At this point indexing becomes necessary
with cross-indexing references to different knowledge based on similarities
and commonalities of employment.

Here I am saying that relevant knowledge based on previous learning might
not have to be totally relevant to a situation as long as it could be used
to run during an ongoing situation.  From this perspective
then, knowledge from a wide variety of experiences should actually be
composed of reactions on different conceptual levels.  Then as a piece of
knowledge is brought into play for an ongoing situation, those levels that
seem best suited to deal with the situation could be promoted quickly as the
situation unfolds, acting like an automated indexing system into other
knowledge relevant to the situation.  So the ongoing process of trying to
determine what is going on and what actions should be made would
simultaneously act like an automated index to find better knowledge more
suited for the situation.
Jim Bromer



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agi
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