Jim, etc al,

This discussion was greatly extended and played out in the early days on
neural networks. I will outline how it went. Someone (me?) should write a
paper about it. Anyway...

Me (and some others): There MUST be a math of adaptive learning, etc, and
if so it almost certainly must not only make sense in value, but also
significance and dimensionality. Further, given more than a hundred million
years of evolution, many of the answers are waiting to be seen in the lab -
if only an astitude mathematician would provide guidance.

Several people showed how NNs could be created that fit ALL of these
criteria - but it took different kinds of neurons to do different kinds of
jobs, e.g. logic vs PID mechanical control.

It soon became obvious that maybe 5% of the people in the field could
follow the math - leaving the other 95% clueless about what was even being
discussed. The mathematicians were ignored or shouted down (sound
familiar?), and with some interesting exceptions, could not find funding,
in part because they were being actively blocked by the non-mathematicians.

My own major contributions were:

1.  Showing that one of the two biological neurons whose transfer function
had ever been characterized was almost certainly computing based on the
logarithms of probabilities, while the other neuron was computing something
ELSE.

2.  Showing how and why differentiating inputs and integrating outputs of a
NN, as is routinely done in biology, left it's transfer function unchanged,
but transformed it's learning to temporal.

These are HUGE, but less than worthless in a world that disparages
mathematics, disparages biology, disparages..., etc. Doing this, I have
become a sort of parriah in NN, AI, AGI, etc.

So, I hereby pass this torch onto you to, because there appears to be no
way you could have less success with it than I have had.

Steve Richfield

On Sat, Jun 8, 2019, 8:43 PM <[email protected]> wrote:

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