For a dissenting view, there's e.g. jacob_cannell's recent comment about the implications of AlphaGo.
Critics like to point out that DL requires tons of data, but so does the human brain. A more accurate comparison requires quantifying the dataset human pro go players train on.
A 30 year old asian pro will have perhaps 40,000 hours of playing experience (20 years 50 40 hrs/week). The average game duration is perhaps an hour and consists of 200 moves. In addition, pros (and even fans) study published games. Reading a game takes less time, perhaps as little as 5 minutes or so.
So we can estimate very roughly that a top pro will have absorbed between 100,000 games to 1 million games, and between 20 to 200 million individual positions (around 200 moves per game) .
AlphaGo was trained on the KGS dataset: 160,00 games and 29 million positions. So it did not train on significantly more data than a human pro. The data quantities are actually very similar.
Furthermore, the human's dataset is perhaps of better quality for a pro, as they will be familiar with mainly pro level games, whereas the AlphaGo dataset is mostly amateur level.
The main difference is speed. The human brain's 'clockrate' or equivalent is about 100 hz, whereas AlphaGo's various CNNs can run at roughly 1000hz during training on a single machine, and perhaps 10,000 hz equivalent distributed across hundreds of machines. 40,000 hours - a lifetime of experience - can be compressed 100x or more into just a couple of weeks for a machine. This is the key lesson here.
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.
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?