From: Vladimir Nesov (robotact@mail.ru)
Date: Sat Sep 22 2007 - 11:18:09 MDT
This list is ironic, but misleading. I don't think Turing was way off: if we
had the algorithm (absence of which is methodological problem, not
technical), we'd probably have an AGI by now.
Google is much less an AI than many other systems.
Processing power required for AGI is probably several orders of magnitude
less than required for brain simulation.
Simulating one brain might be not enough, but running one AGI might be
enough: it can be trained to be an expert in all of relevant human
knowledge, after that depending of architecture it can either do all at once
or be replicated required amount of times, each acting as superproductive
genius in assigned area of expertise, worth in innovation maybe 10^7 random
humans, if you take required time and probability of successfully training a
human expert into account.
On 9/22/07, Matt Mahoney <matmahoney@yahoo.com> wrote:
>
> --- CyTG <cytg.net@gmail.com> wrote:
>
> > Off topic from a ghost :) .. but you guys are frequently talking about
> > beeing somewhat far away from the takeoff scenario both in terms of
> software
> > and hardware.. are there any lists/sites that deals with a more precise
> > micro detailed evaluation of these specifics? Just how much hardware do
> we
> > think we need in regards to wich framework and what does curret
> frameworks
> > lack? What bottlenecks, hurdles, are we looking at and what sort of R&D
> are
> > going into solving them ?
> > If anyone knows!
> > Thanks.
>
> We have historically underestimated the difficulty of AI. Take your pick:
>
>
> - Turing predicted in 1950 that in 50 years a machine with 10^9 bits of
> memory, but no faster than current hardware at the time could pass the
> Turing
> test [1].
>
> - Landauer estimated that human long term memory capacity is 10^9 bits
> [2].
>
> - The Blue Brain project simulated a mouse cortex (1/1000 the size of a
> human
> cortex) at 1 ms resolution in 1/10 real time on the 4096 processor Blue
> Gene/L
> with 1 TB memory [3].
>
> - Google is about as close as we have to AI right now, and they use a few
> hundred thousand processors.
>
> - The human brain has about 10^15 synapses. Neurons have an information
> rate
> of about 10 bits per second. Therefore the equivalent computation is
> 10^16
> OPS and 10^15 bits of memory.
>
> Of course simulating one brain is not enough. For computers to surpass
> the
> human race as a whole, you need to simulate about 10^10 brains.
>
> References
>
> 1. Turing, A. M., (1950) Computing Machinery and Intelligence, Mind,
> 59:433-460.
>
> 2. Landauer, Tom (1986), How much do people remember? Some estimates of
> the
> quantity of learned information in long term memory, Cognitive Science
> (10)
> pp. 477-493.
>
> 3. Frye, James, R. Ananthanarayanan, D. S. Modha (2007), Toward
> Real-Time,
> Mouse-Scale Cortical Simulations, IBM Research Report RJ10404 (A0702-001)
> Feb.
> 5, 2007. http://www.modha.org/papers/rj10404.pdf
>
>
>
>
> -- Matt Mahoney, matmahoney@yahoo.com
>
>
-- Vladimir Nesov mailto:robotact@gmail.com
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