That's a terrible argument. AlphaGo represents a general approach to AI, but its instantiation on the specific problem of Go tightly constrains the problem domain and solution space. Real life is far more combinatorial still, and an AGI requires much more expensive meta-level repeated cognition as well. You don't just solve one problem, you also look at all past solved problems and think about his you could have solved those better. That's quadratic blowup.
Tl;Dr speed of narrow AI != speed of general AI.
AlphaGo represents a general approach to AI, but its instantiation on the specific problem of Go tightly constrains the problem domain and solution space ..
Sure, but that wasn't my point. I was addressing key questions of training data size, sample efficiency, and learning speed. At least for Go, vision, and related domains, the sample efficiency of DL based systems appears to be approaching that of humans. The net learning efficiency of the brain is far beyond current DL systems in terms of learning per joule, but the gap in terms of learning per do...
I've been going through the AIFoom debate, and both sides makes sense to me. I intend to continue, but I'm wondering if there're already insights in LW culture I can get if I just ask for them.
My understanding is as follows:
The difference between a chimp and a human is only 5 million years of evolution. That's not time enough for many changes.
Eliezer takes this as proof that the difference between the two in the brain architecture can't be much. Thus, you can have a chimp-intelligent AI that doesn't do much, and then with some very small changes, suddenly get a human-intelligent AI and FOOM!
Robin takes the 5-million year gap as proof that the significant difference between chimps and humans is only partly in the brain architecture. Evolution simply can't be responsible for most of the relevant difference; the difference must be elsewhere.
So he concludes that when our ancestors got smart enough for language, culture became a thing. Our species stumbled across various little insights into life, and these got passed on. An increasingly massive base of cultural content, made of very many small improvements is largely responsible for the difference between chimps and humans.
Culture assimilated new information into humans much faster than evolution could.
So he concludes that you can get a chimp-level AI, and to get up to human-level will take, not a very few insights, but a very great many, each one slowly improving the computer's intelligence. So no Foom, it'll be a gradual thing.
So I think I've figured out the question. Is there a commonly known answer, or are there insights towards the same?