> 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 toaccommodate *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.
**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