[From Shannon Williams (960226.17:00 CST)]
Bill Powers (960222.1410 MST)--
the HPCT model can, in theory, accomplish through a fixed
organization much of what would otherwise have to be treated as
learning or adaptation. This makes the problems that have to
be solved by adaptation simpler, and perhaps makes the system that
carries out the adaptations simpler also.
I cannot argue with you about HPCT. If you believed in AI, or had a goal
to make a conscious computer, you would quickly see the weakness of HPCT.
Right now, however, HPCT answers all of your questions, and you are
satisfied.
My goals center around the generation of artificial intelligence.
Unfortunately, I cannot find people, except the people on this list, who
discuss intelligence and behavior in terms that I agree with. That is why
I keep coming back to this list to talk about neural nets and learning.
All of the AI literature that I have read refer to the manipulation of
data in some symbolic form. In other words, even the neural net people
seem to visualize thought in terms of a sequence of meanings or
'symbols'. This mode of thinking currently limits designers to building
"learned" stimulous-response units with their neural nets. They need
another way to visualize thinking.
PCT removes so many limitations from our visualizations of thinking:
1) We can visualize 'thinking' without without giving (pre-determined)
meaning to thoughts.
2) We can visualize thinking without visulizing chunks of data (or
symbols).
3) We can visualize why we think.
4) And much more...
ยทยทยท
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Martin (60226 12:00) said earlier today:
"a control hierarchy is a form of neural network"
I do not exactly agree. I think that we need to design neural networks
that control. And when we do this, I do not think that we will need
hierarchies. (We need a method of resolving conflicts, but we do not
need the hierarchy for that.) Just in designing the neural networks to
control and to learn to control, I think that we will go very far in
modeling intelligent/adaptive behavior.
-Shannon