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

--- 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]
> 
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