Abram Demski said:
To be honest, I am not completely satisfied with my conclusion on the
post you refer to. I'm not so sure now that the fundamental split
between logical/messy methods should occur at the line between perfect
& approximate methods. This is one type of messiness, but one only. I
think you are referring to a related but different messiness: not
knowing what kind of environment your AI is dealing with. Since we
don't know which kinds of models will fit best with the world, we
should (1) trust our intuitions to some extent, and (2) try things and
see how well they work...
Mathematics and mathematical proof is a very important tool...
Mine is a system built out of somewhat smart pieces,
cooperating to build somewhat smarter pieces, and so on. Each piece
has provable smarts.
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Mathematics can be extended to include new kinds of relations and systems. One
of the problems I have had with AI-probability buffs is that there are other
ways to deal with knowledge that is only partially understood and this kind of
complexity can be extended to measurable quantities as well. Notice that
economics is not just probability. There are measurable quantities in
economics that are not based solely on the economics of money.
We cannot make perfect decisions. However, we can often make fairly good
decisions even when based on partial knowledge. A conclusion however, should
not be taken as a reliable rule unless it has withstood numerous tests. These
empirical tests of a conclusion usually cause them to be modified. Even a good
conclusion will typically be modified by conditional variations after be
extensively tested. That is the nature of expertise.
Our conclusions are often only approximations, but they can contain
unarticulated links to other possibilities that may indicate other ways of
looking at the data or conditional variations to the base conclusion.
Jim Bromer
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agi
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