Source: Am an AI research scientist.
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).
I'm guessing you work in image recognition, or mostly hear from people who work in image recognition.
There is more to AI, and not all of it is instantly improved by a convolutional neural net.
What are the most valuable, unsolved problems in the field?
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.
Personally, I'm bearish about AGI because I believe we will eventually realize that the brain is a glorified linear regression too, with a custom wiring to help learn language and vision.
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.
Probabilistic programming isn't going to help general AI much. Things like dropout seem to work well enough, and for the most part AI is severely underfitting rather than overfitting. Our models are far to simple and small to really learn language and do complicated reasoning. Making them bayesian doesn't fix that.
Excuse me while I laugh.[1,2,3,4]
>Things like dropout seem to work well enough, and for the most part AI is severely underfitting rather than overfitting.
For the most part, neural networks can't reason at all. They just induce deterministic functions over high-dimensional Euclidean spaces.
>Our models are far to simple and small to really learn language and do complicated reasoning.
They're also not compositional (new concepts as functions of old concepts), productive (able to draw an unbounded number of inferences from each representation), or unbounded in size of representation (unboundedly many concepts). Neural networks don't even represent causal structure, let alone model how an intervention will affect outcomes!
It is, however, really nice to hear an AI booster admit just how incredibly limited connectionist models actually are.
>Making them bayesian doesn't fix that.
No, changing to a causal, compositional representation that allows for productive and nonparametric (unboundedly large) learning does that. The Bayesian part just makes it extra nice by letting us "put information in" anywhere in the model (at any variable) by conditioning.
[1] -- http://forestdb.org/models/learning-physics.html [2] -- http://forestdb.org/models/word-learning.html [3] -- http://forestdb.org/models/arithmetic.html [4] -- http://forestdb.org/models/politeness.html
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.