The thing this article somewhat "misses" is that the designers of these systems are typically aware of—or anticipate—these limitations in some form. A large research theme throughout NLU is coming up with tasks+data that require the sort of extrapolation NNs cannot currently do.
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What designers are aware of is one thing. But what's communicated to the public is another. Microsoft press release, 2018: "Microsoft creates AI that can read a document and answer questions about it as well as a person."
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My two favorite complexity explainers.
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@david__moser My favorite complexity explanee!
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Dear prof Mitchell would you have a draft of this somewhere? I do not have access. Btw 20 years ago as a cognitive science undergrad I *almost* attended to a Santa Fe summerschool. The math is too tough for me but I really enjoyed (trying to understand) your work back then.
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I like this quote from the quoted book: “People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.”
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I read your stuff with Doug what seems like a long time ago. Yes, there are so many of the same challenges as... has it been only !?! 25 years?
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@swisscognitive - The "awarded" Global AI Hub#ArtificialIntelligence#CognitiveComputing#MachineLearning#DeepLearning AI our passion Founders@DalithSteiger@Andy_Fitze at the@WEF@KirkDBorne@EricTopolHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Brilliant exactly the same issue comes up btw in the much older distinction between clinical (intuitive) vs statistical (aka actuarial) prediction. 2nd does better in long run but can have disastrous failures in individual cases. Suspect same is true of AI.
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