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Back when I took signal processing, I remember our professor making a very insightful comment. I sadly don't remember his exact words, but it went something lik

Back when I took signal processing, I remember our professor making a very insightful comment. I sadly don't remember his exact words, but it went something like this. "20 years back, everyone wanted CD's for music, today it is flash drives, the upcoming thing is streaming music from the internet. Technologies come and go. What I can say is that the Fourier transform will still be around 200 years from now as a valuable way of understanding the world." I am of course in no real position to assert that this is a good idea (as I am still in graduate school), but this is the reason why I gravitate towards math and physics classes. Conditional on the assumption that we have a meaningful civilization decades from now (and none of the dangers outlined for instance in the excellent book "Here Be Dragons: Science, Technology and the Future of Humanity" by Olle Haggstrom) cause its utter collapse, I think it is a safe bet to assume "fundamental" topics like Maxwell's equations, Fourier transforms, etc will still be around, alive, and fruitfully studied and applied across a variety of domains. Part of the reason is simply historical: the Fourier transform has had over 200 years to prove its worth time and again across a variety of disciplines. Maybe deep learning is here to stay, maybe it isn't. I don't know, and I am not willing or interested in making bets either way. I am willing to bet on a time proven framework of understanding, such as the examples above.

kefka 9y ago
Indeed. ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter them against the wall, and hope for the best. The best, are things like "Tell what type of bird it is"... But in the end, we have no clue HOW it got those results. Just that the Mystery Magic Function + data = results. I can certainly see a middle path of being able to train a learning function, and then interrogating said function for the underlying things that make it true. Once we understand the primitives, then we can make ideal functions that do X, and do it well. Because right now, learning functions do it rather well but with exceptional overhead. And they're hard to tune without recrunching the whole dataset.

kefka 9y ago

Indeed. ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter them against the wall, and hope for the best. The best, are things like "Tell what type of bird it is"... But in the end, we have no clue HOW it got those results. Just that the Mystery Magic Function + data = results. I can certainly see a middle path of being able to train a learning function, and then interrogating said function for the underlying things that make it true. Once we understand the primitives, then we can make ideal functions that do X, and do it well. Because right now, learning functions do it rather well but with exceptional overhead. And they're hard to tune without recrunching the whole dataset.