As someone who enjoyed your paper a lot, 3 things stood out:
1. The citation to @dawnsongtweets Berkely systems paper seemed ill informed. They do not cast doubt on Deep Learning at all.
2. The omission of Markov Logic Nets in your symbolic section was prominent
fun to see all the discussion re the future of #deeplearning. if you or someone you love has a substantive critique of my recent critique of deep learning, reply with a link here, please. I would love to hear it and plan respond soon. RTs appreciated. #ai #ml
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I liked the paper very much. One thing stood out to me as missing from your section on "insight from cognitive and developmental psychology": the ability to make analogies. IMO this ability is key to learning without big data (and "analogy" is not simply "transfer learning").
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The operative word is "substantive". Not ad hominem attacks. So far, the only
@tdietterich made some valid pointsThanks. Twitter will use this to make your timeline better. UndoUndo
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I posted an even more detailed rebuttal to each of your 10 criticisms of Deep Learning. https://medium.com/intuitionmachine/intuition-machines-versus-algebraic-minds-fad052b46ad5 … Enjoy!
#deeplearning#ai#mlThanks. Twitter will use this to make your timeline better. UndoUndo
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Given that "deep learning" can (apparently) mean any big model that contains something differentiable, how, when history is written, will we be able to say definitively say whether you were right or wrong? In other words, what are specific bounds on your def. of deep learning?
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Good observation. Actually, I suspect that there isn't enough funds to go around with AI research and that's why you have this kind of criticism against DL. I Ben Goertzel had to go do an ICO to get funding, that tells you that traditional funding has all dried up.
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Thanks for the great paper! I wrote up some thoughts in the hope that they add to the discussion.https://medium.com/@christiankaiser_57972/thoughts-on-gary-marcus-critique-of-deep-learning-89146e63c3b3 …
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