--- Matt Mahoney <[EMAIL PROTECTED]> wrote:

> 
> --- Tom McCabe <[EMAIL PROTECTED]> wrote:
> 
> > 
> > --- Matt Mahoney <[EMAIL PROTECTED]> wrote:
> > 
> > > 
> > > --- Tom McCabe <[EMAIL PROTECTED]> wrote:
> > > 
> > > > You cannot get large amounts of computing
> power
> > > simply
> > > > by hooking up a hundred thousand PCs for
> problems
> > > that
> > > > are not easily parallelized, because you very
> > > quickly
> > > > run into bandwidth limitations even with
> gigabit
> > > > Ethernet. Parts of the brain are constantly
> > > > communicating with one another; I would be
> very
> > > > surprised if you could split up the brain
> > > effectively
> > > > enough to be able to both run one tiny piece
> on a
> > > PC
> > > > and have the PCs communicate effectively in
> > > realtime.
> > > > 
> > > >  - Tom
> > > 
> > > It is not that hard, really.  Each of the 10^5
> PCs
> > > simulates about 10 mm^3 of
> > > brain tissue.  Axon diameter varies but is
> typically
> > > 1-2 microns.  This means
> > > each bit of brain tissue has at most on the
> order of
> > > 10^7 inputs and outputs,
> > > each carrying 10 bits per second of information,
> or
> > > 100 Mb/s.  This was barely
> > > within Google's network capacity in 2000, and
> > > probably well within it now.
> > > http://en.wikipedia.org/wiki/Google_platform
> > 
> > Hmmm...This is an interesting issue. Do you have a
> > link to a paper on brain bandwidth?
> 
> I just googled "axon diameter" and found several
> references.  There is a wide
> range so I used the low end to be conservative and
> did the math.  I probably
> should consider dendrites too, but these tend not to
> be very long.  I figure
> it's close enough for an order of magnitude
> estimate.
> 
> > > I think individuals and small groups trying to
> build
> > > AGI will have a hard time
> > > competing with Google due to the cost of
> hardware.
> > 
> > Hardware cost will not be a primary issue. The
> cost of
> > hardware decreases exponentially with Moore's Law;
> the
> > cost of solving a whole tangle of confusing
> problems
> > does not. Nobody is anywhere near the stage where
> they
> > have a program to run and they're looking for a
> > computer. It's like saying that anyone trying to
> build
> > an airplane will find it impossible to compete
> with
> > existing shipbuilders, because of their vast
> > metalworking capacity.
> 
> It's true we can do theoretical work but the lack of
> computing power is
> definitely an obstacle.

What? That's the exact opposite of what I said! Why is
computing power an obstacle? We're not even near the
stage where you put the program into the computer,
because there *is no program*! Suppose you had a
computer with 10^50 FLOPS. What would you run on this
computer?

> It has a strong effect on
> the direction of research. 
> In the early days of AI when hardware was inadequate
> by a factor of a billion,
> we used symbolic approaches in narrow domains with
> hand coded rules.  More
> recently when hardware was only inadequate by a
> million, we were able to
> experiment with statistical approaches, machine
> learning, and low level vision
> and language models.  It is possible that a lot of
> the brain's computing power
> is used to overcome the limitations of individual
> neurons (speed, noise,
> reliability, fatigue) and we will find more
> efficient solutions.  This hasn't
> happened yet, but I can't say that it won't.

If this is so, then where are the great, working AI
algorithms that we supposedly already have that run
very slowly or can only be run on Blue Gene-type
supercomputers? Can you name a single, important,
functioning AI algorithm that requires a supercomputer
to run?

> 
> -- Matt Mahoney, [EMAIL PROTECTED]
> 
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