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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Replying to @IntuitMachine
This is not what he said here. Every stepwise constructive or evolutionary process is in a sense an optimization via a gradient based method, so DL is a sufficiently broad methodology, but you'll still need to set up the system performing the optimization or meta optimization.
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Replying to @Plinz @IntuitMachine
So, DL is an architecture AND a methodology AND the mechanism behind all of evolution? Next, you’ll tell me that is also serves to unify the theory of gravity with the standard model of physics!
@GaryMarcus1 reply 0 retweets 4 likes -
No, my understanding is that Yann LeCun thinks of DL as automating the optimization of a program for computing a complex function. Depending on the optimization method, the program or its generator function will often be differentiable.
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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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Indeed. Which is why I asked the question earlier, what is DL not?
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Handcrafted solutions
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Replying to @Plinz @Grady_Booch and
Would you classify biological inspirationalists as part of Deep Learning? Even if they long ago strayed from perceptron in favor of a more temporal model?
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Arguably, most of the ideas that enabled present day DL were not novel. DL practitioners sometimes reinvented, sometimes repurposed many solutions from earlier AI research, from econometrics, cybernetics, signal processing, physics, psychology and neuroscience.
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