RE: neural nets

From: H C (
Date: Thu Jan 12 2006 - 10:31:44 MST

Not to get into any actual math (too often grossly flawed by factors not
taken into consideration), projects like Blue Brain
( are probably the most important to take
into account when discussing neural network AI implementations.

"Scientists have been accummulating knowledge on the structure and function
of the brain for the past 100 years. It is now time to start gathering this
data together in a unified model and putting it to the test in simulations.
We still need to learn a lot about the brain before we understand it's inner
workings, but building this model should help organize and accelerate** this
quest." Henry Markram

This institute has BIG funding, and really 'effing big computers (which are
only going to get bigger). I'm not an expert, but in terms of the neural
modeling approach to AI, it appears they are at the top of the game, and
they are certainly raising the stakes immensely.


>From: CyTG <>
>Subject: neural nets
>Date: Thu, 12 Jan 2006 15:01:26 +0100
>Im trying to wrap my head around this AI thing, and entirely how far along
>we are in measures of computational power compared to whats going on in the
>human body.
>I know many believes that there's shortcuts to be made, even improvements
>that model nature has provided us with, the biological neural network.
>Still. Humor me.
>Here's my approximated assumptions, based on practical experience with
>and some wiki.
>Computational power of the human mind ;
>100*10^9 neurons, 1000 connections each gives about 100*10^12 operations
>the same time_ .. now on average a neuron fires about 80 times each second,
>that gives us a whopping ~10^14 operations/computations each second.
>On my machine, a 3GHz workstation, im able to run a feedforward network at
>about 150.000 operations /second WITH training(backprop) .. take training
>out of the equation and we may, lets shoot high, land on 1 million
>neurons/second .. now from 10^6 -> 10^14 .. that's one hell of a big
>Also .. thinking about training over several training sets (as is usual the
>case) wouldn't I be correct at making an analogy to linear algebra ?
>thinking of each training set as a vector, each set having their own
>direction. In essense, two identical training sets would be linear
>'depended' on each other and subject for elimination? (thinking there could
>be an mathematical sound approach here towards eliminating semi-redundant
>training data!)
>Hope its not too far off topic!
>Best regards

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