poly: Carbon; Learning algorithms?

I asked "Can you speed up a star's burn rate by dumping in carbon?"
Amara Graps asks, perhaps rhetorically,
> Do you have a particular reason why you choose carbon ??
and whether I want the carbon to be (1) extra mass (2) fuel (3) catalyst?

My intention was catalytic, and I picked carbon as the most available
ingredient in the CNO fusion cycle. I was wondering how much is needed ...
If we plunk the Earth in to the sun, and pretend it's pure carbon (haha)
what happens? From the two excellent answers to my first question, it's
clear that the extra mass doesn't matter much, and it's useless as fuel
to the sun (which is too small to burn carbon). But would adding .0003%
solar mass of a carbon catalyst affect the burn rate significantly?
If so, then the second and third generation stars should burn a lot faster;
and a star's burn rate might be affected by wandering through a carbon cloud;
and evolution studies should think about the solar wind carrying off heavy
elements selectively (or vice versa); and mass:luminosity would be a
mediocre predictor of lifetime without knowing carbon content.

-----
In his article about "How Long Till Superintelligence", Nick Bostrom sez ...

> scale well. The Hebbian learning rule, on the other hand, is perfectly
    scaleable (it scales linearly, since each weight update only involves
    looking at the activity of two nodes, independently of the size of the
    network). It is known to be a major mode of learning in the brain. It

What's really known about the role of the Hebbian rule?
Are there gerbil experiments? Cat scans of people? Etc?
Any info relevant to cryonics & memory survival?

Rich Schroeppel rcs@cs.arizona.edu
Received on Wed Jan 14 19:04:28 1998