Boris Kazachenko
Sat, 04 Jan 2003 05:52:09 0800
Yes, if you have a huge
amount of space and time resources available, you can start your system with a
blank slate  nothing but a very simple learning algorithm, and let it learn
how to learn, learn how to structure its memory, etc. etc.
etc.
This is pretty much what
OOPS does, and what is suggested in Marcus Hutter's related
work.
It is not a practical
approach, in my view. My belief is that, given realistic resource
constraints, you can't take such a general approach and have to start off the
system with specific learning methods, and even further than that, with a
collection of functionallyspecialized combinations of learning
algorithms.
I could be wrong of
course but I have seen no evidence to the contrary, so
far...
***
A fixed collection
of methods won't scale,  power of a method should correspond to generality
(predictive power) of a pattern. The whole point of such patternspecific &
levelspecific scaling of methods IS computational efficiency,  it's
a lot less expensive to incrementally scale methods for individual patterns than
to indiscriminately apply a fixed set of them on patterns most of which are
either too complex or too simple for any given
method.
***
To select formulas you
must have an implicit criterion, why not try to make it explicit? I don't
believe we need complex math for AI, complex methods
can
Sorry, that was a typo, it should be
"can't"
be universal,  generalization is a reduction. What we
need is a an autonomously scalable method.
***
Well, if you know some simple math that is
adequate for deriving a practical AI design, please speak up. Point me to
the URL where you've posted the paper containing this math! I'll be very
curious to read it!!!! ;)
***
We both know that there is no
practical general AI yet, I'm trying to suggest a theoretically
consistent one. Given that the whole endeavor is contextfree it should
ultimately be the same thing. I don't have any papers, when the theory is
finished I'll write a program, not a
paper.
My method is
ultimately simple: sequential expansion of search for correlations
of sequentially increasing arithmetic power/derivation, for inputs which
had aboveaverage compression over the shorter range of search
/ lower arithmetic power/derivation. What's new here (correct me if
I'm wrong), is how I define compression, which determines value of a
pattern, & encode these patterns to preserve restorability
& enable analytical comparison (between individual variable types
within patterns). Both are necessary to selectively
scale the search, & I don't see it in
OOPS
It's in my
introduction, someplace, but I realize it must be mental torture to try to
figure it. Why would you work on it? Only if you agree with my theoretical
assumptions, I suppose, the method is uniquely consistent with
them.
Boris.
