A thought-provoking article by @GaryMarcus . Accordingly, I had some thoughts! Longish thread ahead. /1https://www.wired.com/story/deepminds-losses-future-artificial-intelligence …
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First thought: Why frame this as "DeepMind lost $572 million" rather than "Google made a very large, long-term investment in one of its research arms, DeepMind"? /2
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DeepMind's mission is to "solve intelligence" -- a statement I find nonsensical BTW -- but whatever you want to call it, AI is a very hard, long-term research problem and it's great for companies to fund basic, long-term research that doesn't have immediate payoff. /3
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Second thought: Marcus claims that deep reinforcement learning is "a kind of turbocharged memorization". That's an interesting but still too vague hypothesis that I think needs to be made more formal and testable. /4
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Third thought: Marcus: "DeepMind has yet to find any large-scale commercial application of deep reinforcement learning." Why the focus on this kind of short-term commercial application? Was that kind of short-term payoff what Google had in mind when it acquired Deep Mind? /5
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Fourth thought: Marcus compares DeepMind with IBM Watson. But IBM made huge promises & put out a lot of hype on how their system would very soon revolutionize healthcare, law, etc. Has DeepMind ever made promises about how it would commercialize Deep RL in the short-term? /6
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Fifth thought: Marcus correctly warns of the dangers of "overpromising". But is this fair, with respect to Deep Mind? Have they actually overpromised with respect to their technology? (I did see a quote from Shane Legg a while ago personally predicting AGI by 2020s
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sample: “While it is still early days, AlphaGo Zero constitutes a critical step towards this goal. If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials,
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