Brad Wyble

Brad Wyble
Email: B.Wyble@kent.ac.uk
Address: Computing Laboratory
University of Kent
Canterbury
Kent, CT2 7NF
Telephone: +44 (0)1227 827553 (direct line)
Facsimile: +44 (0)1227 762811
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Imagine,... you've got your 10^6 CPU's and you want to make
an AI. You have to devote some percentage of those CPU's to "thinking"
(ie analyzing and representing information) and the remainder to
restricting that thinking to some useful task. No one would argue, I
hope, that it's useful to blindly analyze all available information.

The part that's directing your resources is the control architechture and
it requires meticulous engineering and difficult design decisions.
What percentage do you allocate?

5%? 20%? The more you spend, the more efficiently the remaining CPU
power is spent. There's got to be a point at which you achieve a maximum
efficiency for your blob of silicon.

The brain is thoroughly riddled with such control architechture, starting
at the retina and moving back, it's a constant process of throwing out
information and compressing what's left into a more compact form. That's
really all your brain is doing from the moment a photon hits your eye,
determining whether or not you should ignore that photon. And it is a
Very Hard problem.

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I used to think AGI was
practically a done deal. I figured we were 20 years out.

7 years in Neuroscience boot-camp changed that for good. I think anyone
who's truly serious about AI should spend some time studying at least one
system of the brain. And I mean really drill down into the primary
literature, don't just settle for the stuff on the surface which paints
nice rosy pictures.

Delve down to network anatomy, let your mind be blown by the precision and
complexity of the connectivity patterns.

Then delve down to cellular anatomy, come to understand how tightly
compact and well engineered our 300 billion CPUs are. Layers and layers
of feedback regulation interwoven with an exquisite perfection, both
within cells and between cells. What we don't know yet is truly
staggering.

I guarantee this research will permanently expand your mind.

Your idea of what a "Hard" problem is will ratchet up a few notches, and
you will never again look upon any significant slice of the AGI pie as
something simple enough that it can can be done by GA running on a few kg
of molecular switches.