Source: Am an AI research scientist.
What we do know is that current techniques won't get us close to AGI, so something new is needed (or perhaps like backprop, something old will work once we have enough compute power). Personally I'm bullish on AGI because I have strikingly low faith in the ability of evolution to operate very effectively as a tool for algorithm discovery, so I suspect that once we've hit the compute threshold we'll find that many different algorithms can do the trick, and 40 years is probably not out of the question for us to hit that point (or 10, or 100), depending who you talk to about what the compute threshold might be.
I'd caution against putting too much weight in what experts say, though, since with a tiny few set of exceptions anyone working on "AI" today is actually just working on narrow AI, which is, as someone put it, just glorified linear regression. Those tools will almost certainly be part of the solution, but only in the sense that the classical theory of Diophantine equations was part of Weil's proof of Fermat's Last Theorem - they are not the core of the theoretical approach.
Evolution is a slow algorithm, but it had access to an absurd amount of compute (all neuronal organic matter on Earth) and environment simulation (all of physical reality on Earth) when discovering us; so the discovery of the algorithms/architectures/principles in our heads shouldn't be viewed as trivial.
With backprop we didn't just need bigger machines, we needed better algorithms, palliatives for the exploding-gradient problem that made values exceed our numerical representations, and then hardware specifically designed for doing the matrix-ops involved.
If I saw something capable of speeding up probabilistic program inference the way GPUs sped up backprop, I'd start saying we should expect to see powerful AI applications quite soon.
My advisor shared the following wisdom with me: "When the experts in your field say that saying can be done, they are probably right. When the experts in your field say that something cannot be done, they are not necessarily right."
Generally yes, but they may be significantly off on the timeframe. One famous example is that once alpha-beta search was invented (in the late 1950s), Herb Simon predicted that "within ten years a digital computer will be the world's chess champion". That did eventually happen, using techniques not even all that different from alpha-beta search, but it took 40 years rather than 10. Many of the 1980s neural nets claims turned out to be eventually vindicated too, but it took 30 years, which was quiet a bit longer than the optimistic portion of 1980s "connectionists" expected.
That's the type of skepticism I usually have with claims today too. When people say "there will be fully autonomous self-driving cars on the road by 2020", I don't doubt it'll happen, but whether it'll happen in less than 3 years I have more doubts about. You could argue AI researchers have gotten better at accurately predicting the timeframes of advances than they were in the early days of AI, but I'm not sure there is solid evidence of that (would be interesting if someone has studied it).
What are the most valuable, unsolved problems in the field?
The idea that OpenAI could talk him down is pretty impressive, and if true I would significantly positively update my impression of OpenAI. (I thought OpenAI was funded by people on this hype train.)
Edit: And seems like you are wrong anyway, see top comment.
