Thanks everyone for helping me "wring out" the whole dp/dt thing. Now for
the next part of "Steve's Theory..."

If we look at learning as extracting information from a noisy channel, in
which the S/N ratio is usually <<1, but where the S/N ratio is sometimes
very high, the WRONG thing to do is to engage in some sort of slow averaging
process as present slow-learning processes do. This especially when dp/dt
based methods can occationally completely separate (in time) the "signal"
from the "noise".

Instead, it would appear that the best/fastest/cleanest (from an information
theory viewpoint) way to extract the "signal" would be to wait for a
nearly-perfect low-noise opportunity and simply "latch on" to the "principal
component" therein.

Of course there will still be some noise present, regardless of how good the
opportunity, so some sort of successive refinement process using future
"opportunities" could further trim NN synapses, edit AGI terms, etc. In
short, I see that TWO entirely different learning mechanisms are needed, one
to initially latch onto an approximate principal component, and a second to
refine that component.

Processes like this have their obvious hazards, like initially failing to
incorporate a critical synapse/term, and in the process dooming their
functionality regardless of refinement. Neurons, principal components,
equations, etc., that turn out to be worthless, or which are "refined" into
nothingness, would simply trigger another epineuronal reprogramming to yet
another principal component, when a lack of lateral inhibition or other
AGI-equivalent process detects that something is happening that nothing else
recognizes.

In short, I am proposing abandoning the sorts of slow learning processes
typical of machine learning, except for use in gradual refinement of
opportunistic instantly-recognized principal components.

Any thoughts?

Steve Richfield



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agi
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