On Mon, Aug 13, 2012 at 5:26 PM, Steve Richfield
<[email protected]>wrote:

> AI/AGI has long presumed that we first go through a self-organizing phase,
> followed by a learning phase.


Where did you get that idea? The usual assumption is an engineering phase
followed by a machine learning phase (followed by more engineering based on
the results thereof, etc).

"Self-organizing" is a rather vague term, but I'll accept in this context
your usage of it to mean learning with an unusually large set of variables.


> The distinction between learning and self-organization seems to disappear
> if you simply connect everything to everything else, which is actually seen
> in some of the lower life forms. This is possible in more complex cases in
> a computer than in biology. Sure this adds another 2-3 (or more) orders of
> magnitude to the problem


No, it adds thousands (or more) orders of magnitude to the problem (every
variable you add, multiplies the size of the search space). Got a computer
capable of 10^1000 operations per second? Me neither. So let's try to
formulate problems we have a prayer of solving.

In practice, the key to making machine learning work is to move in the
opposite direction and find ways of constraining the search space, reducing
the effective number of variables, so the machine has some chance of
finding a solution.


> I thought of this while reflecting on my recent glaucoma cure, where it
> became obvious that simple changes to my glasses were making sweeping
> changes in the *organization* of my visual system despite my age - enough
> to reverse ongoing physical changes that would have eventually led to the
> loss of vision in my right eye. This isn't simple "learning", but something
> much more powerful.
>

I guarantee your brain was using some tightly constrained learning
algorithm with very few free variables or the process would have been
interrupted, not only by the loss of vision, but by the heat-death of the
universe.



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