[From Shannon Williams (96011.02:30)]
Bill Powers (960113.0530 MST)]
If you subtract a delayed version of an input signal from its current
value, the resulting output signal represents the rate of change of the
input signal.
You are thinking: output = (input2-input1)/time
I am thinking: input1 = reference
input2 = input
where: [input] ---> [neural network] ---> [output]
/|\ |
[adjust] \|/
[ment ] <---------C
/|\
>
R
The output is LEARNED by making it equal the reference
for whatever is input.
Your alphabet-learning system is pretty sketchy. Have you realized
that your scheme won't work if the alphabet isn't presented in
the normal sequence?
The neural network will learn to predict only when prediction is viable.
If you teach it that A follows B, then that is what it learns. If you do
not give it any consistant input, then it does not learn anything. It only
recognizes the order/predictableness of its universe.
You must have some idea of what you mean by all this, but so far you
haven't got it across.
Don't blame it all on me.
How can an output be generated to avoid situations that are unpleasant?
This question should not be causing problems for you. This is a question
you must ask yourself even in your current version of PCT. The question
for you is: How do we learn which outputs we need to generate to control
a perception?
In answer to your question: We try an output. If it helps, we try it
again. If it doesn't help, we try something else.
Is there any one output that can do that?
No.
What defines a situation, to the behaving system, as unpleasant?
Intrinsic errors:
1) We don't want to feel hungry.
2) We want to be able to predict
3) We don't want to be cold.
4) etc.
···
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One thing to remember is that the input to the neural network is a pattern.
This means that you can map a behavior to a pattern. If that pattern
later fails to remove an error when the behavior is output, then you can
alter your network to generate another output. BUT- the effect of what you
are doing is this:
at first: neural input = 0X11XX01 ==> neural output = 11111111
after adjustment: neural input = 00110X01 ==> neural output = 11111111
= 01110X01 ==> neural output = 11101110
-Shannon