1) Growth rates in nature are never exponential. They are sigmoidal. Sigmoids look very exponential when you're in the middle of them, but we are starting to see the level of moore's law (yeah yeah, it's technology not ICs, still sigmoidal)
2) Even if we had a computer that was 1,000 times faster than the ones we have today and used 1/1,000 of the computing power we STILL don't have the algorithms to produce a human intelligence and that is one hell of an algorithm.
3) The focus for a long time has been moore's law and the associated increase in FLOPS. I think what is more important and more limiting is the bandwidth and bandwidth is a couple orders of magnitude lower than FLOPS where FLOP=Byte and an extra order of magnitude lower when FLOP=Word.
Even if we had a computer that was 1,000 times faster than the ones we have today and used 1/1,000 of the computing power we STILL don't have the algorithms to produce a human intelligence and that is one hell of an algorithm.
- One has to put such an algorithm as in the realm of "unknown unknowns". Anyone who says they know for sure that a human-capability-equivalent algorithm is complex would have to know a least a lot about such an algorithm. No one can yet make that claim. So the non-existence of such an algorithm isn't a certainty but just a trend.
And the "unknown unknown" things might a long time out, might be just around the corner or might be utterly impossible.
So, yeah, we're not too far off from the end of the road for silicon lithography. We've had quite a good run, and we've advanced so, so far from the humble beginnings 60 years ago.
With each generation of chips, with ever shrinking process size, the designs get harder and harder. At some point soon, we're going to decide that we just can't improve this technology any further. That silicon-lithography based computers just aren't going to get any better.
So what happens then? What happens to Intel, and all the other semiconductor vendors when this year's newly released chips aren't any better than last year's chips?
Is the market going to accept that? Will it be OK for Apple to say to everyone that the iPhone 20 (or whatever) is as good as it is going to get, and no new whizzy features are going to be implemented?
I think the investors will file lawsuits, and all the heads of the technology companies will be replaced with people who are going to try harder, and use some other, better, technology instead to keep the profits rolling in.
And what is that going to be? Molecular nanotechnology. Precise placement of individual atoms to create materials and structures with superior properties to what we have now.
And that will enable a whole bunch of things, including AGI.
2. I didn’t say that we are close to human level intelligence, just that if generalised Moore’s law holds then we will not get much warning - we will move the 1% level to human level in 10 years. How long it will take to get to the 1% level is currently unknown.
3. I was talking about generalised Moore’s law (a doubling of processing power ever 18 month), not the mechanism of how we do this. Just increasing transistors counts does not have much future (it appears), but there are many other ways of increasing computational power that I am sure will be used over the coming decades.
It's quite likely that human intelligence is actually highly optimized to solve specific classes of problems, such that increasing performance in one respect decreases performance in another.
If you want to see this in play, look at human variability. There are plenty of examples of humans with extreme intelligence, but those people don't dominate other humans in every regard. They are brilliant on certain subjects while other subjects don't even register. The most brilliant physicist in the world might say the wrong thing to the wrong person and get shot. And software has no intrinsic way to integrate the two systems that might be able to perform well at those two tasks respectively. The very computational structures which lead to physics breakthroughs might be involved in the social mistake.
The integration of conflicting models is solved by human beings through society. We empathize, coordinate, fight, and kill until some consensus emerges. People seem to assume that AIs will just be able to automatically integrate their knowledge with one another, but that doesn't make sense. If that's true you haven't gotten to the "conflicting models" scenario yet.
The whole notion of "general" intelligence is highly suspect. Intelligence is really a certain kind of adaptability to your environment. But there's no such thing as general adaptability. Features that let you adapt to the sea will be liabilities if you find yourself on land. Some combinations of adaptation may be compatible, but many will be are antagonistic.
The realistic scenario is that AIs will just join our society, occupying their own place on the spectrum of specialization.
We have a rough idea of how much processing the human brain is capable of by looking at the eye and optic nerve. Our most powerful computers are many orders of magnitude below human level processing.
How can this be true? Human brain consumes just so much energy, our chips are already running close to single-electron level switching, and consume comparable amounts of energy (not even talking about computer clusters/supercomputers). May be layouts/programming are not good enough, but bare computing power is there.
1. http://chrisfwestbury.blogspot.co.il/2014/06/on-processing-s...
Modern deep neutral network demonstrate that they do comparable decisions with relatively little power. (i.e. modern speech and image recognition running on laptops, for example).
So I say for modern computers it's all about correct programming.
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