From: Richard Loosemore (email@example.com)
Date: Thu Jan 12 2006 - 09:26:44 MST
Such comparisons have very little to teach us, because (among other
things) the kind of ANNs people have been playing with for the last
couple of decades are a really dumb way to go about building an
It might turn out, for example, that a functional unit (say a cortical
colummn if you want to talk neuroscience, or a concept-instantiation
unit if you want to go the cognitive systems route) can be implemented
by either a million neurons or by a hundred lines of code that can run
as one of a thousand parallel threads on a single blade in a roomful of
The crucial question is *what* is the best functional unit to emulate in
a human brain: somthing tiny and horrible numerous, like neurons, or
somethinh pretty huge, like a concept-instantiation module (I am making
that phrase up, but you get what I mean).
Me, I am firmly convinced that we would do best by getting inspiration
from what neurons do, but not even think about emulating them as if they
were the highest functional units.
> 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 of that model nature has provided us with, the biological
> neural network.
> Still. Humor me.
> Here's my approximated assumptions, based on practical experience with
> ann's and some wiki.
> Computational power of the human mind ;
> 100*10^9 neurons, 1000 connections each gives about 100*10^12 operations
> _at the same time_ .. now on average a neuron fires about 80 times each
> second, that gives us a whopping ~10^14 operations/computations each
> 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 'touched' neurons/second .. now from 10^6 -> 10^14 .. that's one
> hell of a big number!!
> 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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