Unless I am much mistaken, this is what Eugen is trying to say:
If we were aliens, trying to understand a bunch of chess-playing IBM
supercomputers that we had just discovered on an expedition to Earth, we
might start by noticing that they all had very similar gross wiring
patterns, where "gross wiring" just means the power cables, bundles of
wires inside each rack, and wires laid down as tracks on circuit boards.
But nothing inside the chips themselves, and none of the "soft" wiring
that exists in code or memory.
Having mapped this stuff, we might be impressed by how very similar the
gross wiring pattern was between the different supercomputers that we
discovered, and so we might conclude that our discovery represented a
significant advance in our understanding of how the machines worked. We
might even conclude that the chess-playing ability of these machines was
all encoded "genetically" by the folks in the factory who did all the
soldering and hooked up the wiring.
But how much of our understanding of the chess playing ability of these
machines would we *really* have understood, once we got the gross wiring?
Virtually none of it.
We would not yet have understood the hardware functionality inside the
chips, nor the software functionality in the code.
Same goes for the understanding that we have of the brain. There could
be as many as 14,000 brain-specific genes in the human genome:
(http://www.nature.com/neuro/journal/v4/n3/full/nn0301_217.html)
That would be enough to encode the gross wiring of, what?, a single Blue
Gene supercomputer? Maybe a little more?
We might understand everything about all the wiring diagrams in the
human brain -- all the stuff that Shane Legg was beginning to list --
and yet still barely have scratched the surface.
*****************************
The 14,000 genes figure makes me think that we could encode the
equivalent of the gross wiring of a Blue Gene sized machine, plus enough
further detail to get some powerful algorithms that *then* interact with
the environment to yield the real intelligence.
That last bit -- the [powerful algorithms that interact with the
environment] bit -- is what makes the difference between a baby that
sits there drooling and probing for its mother's nipple, and an adult
human being who can understand the complexities of the human cognitive
system.
Anyone who thinks that that last bit is also encoded in the human genome
has got a heck of a lot of work to do ..... and only 14,000 genes to
write it down on.
Richard Loosemore
Eugen Leitl wrote:
On Thu, Apr 05, 2007 at 02:03:32PM +0200, Shane Legg wrote:
I didn't mean to imply that all this was for wiring, just that there
is a sizable
about of information used to construct the brain that comes from the
genes.
No disagreement. Apart from sizable. A few gigabases doesn't give you 10^10
bits. And that's about the last time I'm about to mention it.
We're totally on the same page, but for some reason you're extremely
literally-mindedly focusing on some isolated phrases instead of what
I mean. It is rather obvious what I mean, if you don't start looking
at isolated phrases and look at word meaning in an absolute way
(yes, Blue Brain is about as complicated and large scale as simulations
come. No, it is completely trivial on the code size as far as the
classic AI school is concerned. The classic AI school doesn't
think that cable theory, calcium dynamics or the Nernst-Planck
equations are to be considered nontrivial.
If you want to model the brain then this is the kind of information
that you
are going to have to put into your model.
Not if you're looking at short-range processes. The genome has zero
activity on second scale, and only very little activity on minute
scale. Here you can look at the anatomy, and completely ignore how
it came into being, and what it does on hour to day scale (which
is where the genome comes in).
You have to make the model a lot more complex if you just start with
a fertilized egg in machina.
Why does the optic tract project to the lateral geniculate nucleus,
the pretectum
and the superior colliculus and not other places in the brain? Why
does the
lateral genicultate body project to striate and not other parts of
cortex? Why does
the magnocellular pathway project to layer 4Calpha, while the
parvocullular
pathway projects to 4A and 4Cbeta? Why does the cerebral cortex
project to
the putamen and caudate nucleus, but not the subthalamic nucleus? I
could
list pages and pages of examples of brain wiring that you were born
with and
that came from your genetics, it's basic neuro science.
We're still in vigorous agreement. In fact, in C. elegans each
neuron *does* have an address, and the neural network *is* completely
deterministic and genetically wired. But that's a 300 cell network
in a 1 kCell animal.
I don't clam that all wiring in the brain is genetic, or even a
sizable proportion of it.
You sound about as frustrated about this exchange as I am.
What I am claiming is that the brain wiring that is genetic is
non-trivial and cannot
be ignored if somebody wants to build a working brain simulation.
On a time scale of seconds to minute you can absolutely ignore the
genetic component. You absolutely need the genetic contribution (including
full cell dynamics and migration, and molecular target recognition) if
you start from nowhere.
You remember the thread: complexity in the code versus complexity
in the
data? The Blue Brain complexity is all in the data. This is very
different
from the classical AI, which tends to obsessionate about lots of
clever
algorithms, but typically does sweep the data (state) under the
carpet.
Yes, I agree, it's in the "data" rather than the "code". But I don't
accept
that you can say that their model is simple.
As neural emulations go, in terms of lines of code, it's probably
the largest and most complex there is. In terms of software project
complexity, as measured in MLoCs, especially if you exclude the
numerics libraries, no. If compared with a classical AI approach
to human cognition, it's effectively zero complexity. Yet Blue Brain
(with quite a few extensions) is in touching distance of creating
the full cognition of an adult, if loaded with the full data set
on appropriate hardware.
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