@Ylecun Now that you have joined the symbol-manipulating club, I challenge you to read my arxiv article Deep Learning: Critical Appraisal carefully and tell me what I actually say there that you disagree with.
It might be a lot less than you think.https://twitter.com/ylecun/status/1066418378070507520 …
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Granted, there are exceptions & various levels of hype around Deep Learning (*cough*
@deeplearningai_ *cough*), but I think by far the majority of researchers & practitioners see it as a tool for powerful pattern recognition, but don't predict that it alone will get us to AGI.
Love to hear other people weigh in: do they think deep learning alone can get us to AGI, and if not why not?
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Since we have DL but don't yet have AGI, it's quite obvious to everyone that we are missing something. I repeat: the interesting question is precisely what. I repeat my claim: DL (defined as gradient-based learning of non-linear functions) will be part of the solution.
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Still waiting for you
@ylecun to show me the courtesy as a professional colleague at NYU and review Deep Learning: Critical Appraisal and clarify what it is that you are saying now that I didn’t say previously.
End of conversation
New conversation -
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FWIW, no, I definitely don’t think we will get to AGI via DL alone. That said, I doubt anyone working on DL right now is looking to build an AGI with it. It’s a great tool to experiment and solve narrow problems with, but it won’t make AI-complete problems much easier to solve.
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Why do we think deep learning would get us to AGI in the first place? I think that's reasonable to ask. The brain exhibits no similar architecture so why should DL form part of the solution. Not an DL hater, far from it, just advocate of a completely different approach to AGI.
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