Eugene Leitl wrote:
> paul@i2.to writes:
>
> > How many thousands is the real question. I suspect you're looking at 100's of
> > thousands. Sure, once you achieve the proper transistor density, the next
> > challenge is actually hooking them up in such a way as to accurately
> > emulate the entire spectrum of neurochemical activity! :-)
>
> Talk is cheap. Can you gives us numbers?
Well, I'm no neurophsyiologist, but lets give it a shot. First of all, how many 'and/or' gates would it take to model a seratonin molecule interacting with a phosphate molecule? This is outside of my expertise, but I suspect this would require several dozen 'and/or' gates to model even the simplest of interactions. The next challenge is coming up with a complex enough circuit design to model *all* of the possible interactions of seratonin along a single neuron connection. And of course this only describes seratonin! So the next challenge is moving up the scale of complexity to design a software/hardware system general enough to accommodate *all* neurotransmitter activity. You would think with today's supercomputers, somebody somewhere has designed a program that can emulate every conceivable molecular state of a single neuron. If not, then my point is well taken in how complex each neuron in fact is.
Assuming someone has designed a program that's capable of this; conceptually then, one must then have this program run simultaneously and in parallel with thousands of others to match the average neuronal connectivity. The program would have to do a complete run through an average of 10 times/sec. Since it would be a waste for a simultaneous program running for every neuron (10 billion?) it would be easier to have each neuron be stored as rapidly-accessed data until used. How much data would be required to store accurately the state of each neuron? I don't know, but I suspect it's easily on the order of a megabyte at least - as it would have to store the entire array of unique molecular states the neuron is in.
At this point it's all guess work, but my point is that even if when we achieve
atomic
scale transistor densities, the real challenge will be organizing those transistors
to
emulate the entire brain, which is itself a molecular switching computer.
**The challenge is not the speed or density, which will eventually give us the
_potential_
to run a human brain 1000's of times faster than our own. No, the real challenge
is creating something complex and coherent enough to emulate the brain itself. I
suspect
the hardware and software bottlenecks in actually doing so will be difficult enough
to close the gap between brain augmentation (IA) and human-level AI considerably.
> > Therefore, my primary position is that the gap between uploading
> > and human-level AI is much narrower than is often argued.
>
> Gap in which context? Brute-force uploading (aka neuroemulation)
> requires many orders of magnitude more power than straight bred
> AI. And the translation procedure to the more compact target encoding
> is nonobvious (i.e. I do have hints, but can't be sure it's going to
> work).
That is also completely non-obvious. I have yet to hear a single convincing argument
that sufficient *speed* and density automatically yields human-level AI. You will
need
complexity, and where by chance will you be getting it? Until this can be
convincingly
answered, all human-level AI claims should be kept in every Skeptics top 5.
**Since no one has actually built or designed a theoretical human-level AI, how can
anyone possibly claim what it takes to build one? This seems completely absurd
to the point of self-contradiction! As so many are fond of saying around here -
extraordinary claims require extraordinary proof. To re-iterate the obvious, until
someone can prove otherwise, a human-level AI will have to equal the complexity of
the
human brain.
Paul Hughes