--- 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 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 costs $2 million/month
> just for electricity for their server farms.  Google
> is building a
> supercomputer in Oregon that will have cooling
> towers 4 stories high.
> http://en.wikipedia.org/wiki/Project_02
> 
> > 
> > --- Matt Mahoney <[EMAIL PROTECTED]> wrote:
> > 
> > > --- Eugen Leitl <[EMAIL PROTECTED]> wrote:
> > > > > Google already have enough computing problem
> to
> > > do a crude simulation of a
> > > [human brain]
> > > > 
> > > > Um, no. It takes 64 kNodes of Blue Gene/L to
> do
> > > about 8 1/10th speed
> > > > crudely-simulated
> > > > mice, or about one realtime cartoon mouse
> > > (assuming, the code would scale,
> > > > which it
> > > > wouldn't).
> > > 
> > > The Blue Gene/L simulation is at a lower level
> than
> > > is needed to do useful AI.
> > >  You don't need millisecond resolution.  In most
> > > neural models, the important
> > > signal is the rate of firing, not the individual
> > > pulses.  I realize there are
> > > exceptions, such as the transmission of phase
> > > information for stereoscopic
> > > sound perception up to 1500 Hz.  But for most
> > > purposes, neurons have an
> > > information rate of about 10 bits per second. 
> This
> > > was measured in tactile
> > > sensation in the finger.  (Sorry I don't have
> the
> > > references).  In any case,
> > > 0.1 seconds is about the smallest perceptable
> time
> > > unit in humans.
> > > 
> > > The human brain has about 10^11 neurons with
> 10^4
> > > synapses each.  Each synapse
> > > represents about 1 bit of memory (Hopfield
> model). 
> > > Therefore you need 10^15
> > > bits of memory and 10^16 operations per second.
> > > 
> > > Google has about 10^5 PCs with 2-4 GB memory
> each,
> > > connected by a high speed
> > > Ethernet.  Therefore they have enough memory. 
> They
> > > have a mix of different
> > > processors, but a modern processor can execute
> 10^10
> > > to 10^11 16-bit
> > > multiply-add instructions per second using SIMD
> > > (SSE2) instructions (more if
> > > you add a GPU).  Therefore they have enough
> > > computing power, or at least close
> > > enough to do useful experiments.
> > > 
> > > 
> > > 
> > > -- Matt Mahoney, [EMAIL PROTECTED]
> 
> 
> -- Matt Mahoney, [EMAIL PROTECTED]
> 
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