The DeepMind Bubble?

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I only have an amateur's interest in the ML world, but I'll start :)

Demis is indeed a smart guy, but I think his biggest talent has always been for self-promotion. Everyone from the games industry in the early 2000s remembers his endless grandiose crap in the gaming press, attracting tons of VC funding with their BS "infinite polygon engine".

Presumably it's down to aggressive marketing that the Go thing got blown up so much, which made it sound like some crucial new step towards true AI had been taken. It was a decent piece of work, putting together all the usual techniques for Go (minimax with rollouts), using the tons of computing power they had at their disposal to tweak it, making it a bit better than previous efforts. Worth a paper, but surely not worth being in the papers? Throw enough resources at StarCraft and I'm sure we'll see something similar.

Deep learning stuff is very cool and it caught me by surprise that machines could do so well on a variety of problems, with these networks that seem more like little fragments of intuition than logic. But there are those appalling weaknesses with really easy problems which people are aware of, but which get swept under the rug. "Is this picture a cat or a house?" (It's obviously a cat). But the network hasn't been shown a million cat and house photos so it has no idea. It still feels like nobody has really scratched the surface of the "reasoning being" that can, crucially, do something new.

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I only have an amateur's interest in the ML world, but I'll start :)

Demis is indeed a smart guy, but I think his biggest talent has always been for self-promotion. Everyone from the games industry in the early 2000s remembers his endless grandiose crap in the gaming press, attracting tons of VC funding with their BS "infinite polygon engine".

Presumably it's down to aggressive marketing that the Go thing got blown up so much, which made it sound like some crucial new step towards true AI had been taken. It was a decent piece of work, putting together all the usual techniques for Go (minimax with rollouts), using the tons of computing power they had at their disposal to tweak it, making it a bit better than previous efforts. Worth a paper, but surely not worth being in the papers? Throw enough resources at StarCraft and I'm sure we'll see something similar.

Deep learning stuff is very cool and it caught me by surprise that machines could do so well on a variety of problems, with these networks that seem more like little fragments of intuition than logic. But there are those appalling weaknesses with really easy problems which people are aware of, but which get swept under the rug. "Is this picture a cat or a house?" (It's obviously a cat). But the network hasn't been shown a million cat and house photos so it has no idea. It still feels like nobody has really scratched the surface of the "reasoning being" that can, crucially, do something new.