Everyone has heard about the water analogy for electrical operation. I have
a mechanical analogy for neural operation that just might be "solid" enough
to compute at least some characteristics optimally.

No, I am NOT proposing building mechanical contraptions, just using the
concept to compute neuronal characteristics (or AGI formulas for learning).

Suppose neurons were mechanical contraptions, that receive inputs and
communicate outputs via mechanical movements. If one or more of the neurons
connected to an output of a neuron, can't make sense of a given input given
its other inputs, then its mechanism would physically resist the several
inputs that didn't make mutual sense because its mechanism would jam, with
the resistance possibly coming from some downstream neuron.

This would utilize position to resolve opposing forces, e.g. one "force"
being the observed inputs, and the other "force" being that they don't make
sense, suggest some painful outcome, etc. In short, this would enforce the
sort of equation over the present formulaic view of neurons (and AGI coding)
that I have suggested in past postings may be present, and show that the
math may not be all that challenging.

Uncertainty would be expressed in stiffness/flexibility, computed
limitations would be handled with over-running clutches, etc.

Propagation of forces would come close (perfect?) to being able to identify
just where in a complex network something should change to learn as
efficiently as possible.

Once the force concentrates at some point, it then "gives", something slips
or bends, to unjam the mechanism. Thus, learning is effected.

Note that this suggests little difference between forward propagation and
backwards propagation, though real-world wet design considerations would
clearly prefer fast mechanisms for forward propagation, and compact
mechanisms for backwards propagation.

Epiphany or mania?

Any thoughts?

Steve



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agi
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