Easy take: calling linear algebra “quantum physics” and “artificial intelligence” is hype. Deeper: the huge influx of physicists into AI has produced an intellectual monoculture that isn’t capable of addressing key problems in the field.https://twitter.com/WIRED/status/1181437300414275584 …
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Yes, and "let's look at the energy surface" leads to insights that vaguely-motivated tweaking can't. (Such tweaking being an approach CS-educated folks are liable to fall into.) But it doesn't lead to the insights that understanding mechanism-domain interactions can.
This seems very different from the original assertion, now more like "Are ideas adapted from physics sufficient to solve the main problems of AI?" Seems the answer is "obviously not", though of course exploring ideas from many different domains seems likely a good thing.
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Not sure I follow; what is the relevant difference between this formulation and what I said? I was suggesting that physicists mostly only draw on methods and analogies from physics, and (useful as those are) they aren't able to address many of the key problems in AI.
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Oh, that's not how I interpreted your tweet at all. It read (to me) as you asserting that the monoculture in AI is due to physicists. I guess not!
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Hopfield networks, Boltzmann machines, renormalization group explanations of machine learning models - I'm glad all these things are being explored, although I have low confidence any will turn out to be on the critical path to AI.
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Is the point here that these are diverse intellectual traditions, although all based in physical analogies? (I suspect I'm not seeing where you are pointing)
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We have inadequate frameworks for understanding how knowledge is created. It should be no surprise that we can't figure out how to create a generalized AI. I write about knowledge structures and their creation. Here's an intro.https://empathy.guru/2019/04/06/what-is-structural-memetics-and-why-does-it-matter/ …
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