@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.
That’s all I am saying, but some people don’t like me saying it, and try to undermine my credentials to keep me from saying it. Go back to 2015 and you will see a lot of people more optimistic about dl alone than I was. Maybe times have changed; that was how this thread started.
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True, there was quite a bit of silly hype around DL, especially for those with less expertise (maybe mostly - even as an undergrad I knew fancy big neural nets won't get us to AGI)... My impression is it's getting better now, seems AI is on down slope of Gartner's hype cycle.
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This whole thread got started when I pointed out that the narrative was changing.
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To most people in the field, any particular network architecture trained with gradient-based learning is qualified as DL (even if it's designed to manipulate symbols) In that sense "DL alone" will probably do a lot more things than it does today.
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