Yann LeCun makes a very clear and honest statement of why Deep Learning is not enough. It's not enough because there are functions that don't emerge intrinsically from the network.https://twitter.com/ylecun/status/1215286749477384192 …
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Indeed; I was simply building on what you said: “Every stepwise constructive or evolutionary process is in a sense an optimization via a gradient based method.”
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It does not follow that each of these processes is also DL, but that DL (understood as a methodology) is not obviously unable to reproduce them
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Yes, DL is basically compositional function approximation. Before DL, ML was mostly limited to shallow models. For instance, end-to-end training for speech recognition or game playing was considered to be outside of the reach of ML.
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