There are certain fields that have been around long enough (example: Physics) and principles are well established that a Ph.D. has an extreme advantage. There are fields that are so new and do *not* have decades worth of principles that a PH.D. has less of an advantage.
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The first principle that every deep learning researcher should come to grips with is that deep learning is more biological than it is mathematical. To formalize this one should study the complexity science literature.
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One hint of why this is true is that Deep Learning architectures are "grown" and not "programmed" (in the classical sense).
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Furthermore, if you take the myopic viewpoint of mathematical programming then one could argue that Deep Learning is nothing but credit assignment using gradient descent. That's a very impoverished viewpoint!
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The only existing general framework that stitches together ideas from biology and computer science can be found in the complexity sciences. So it is indeed astounding that many DL researcher are ignorant of this field.
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DL research speaking from the viewpoint of either Bayesian statistics or mathematical programming simply do not speak in the same conceptual framework as those in the complexity sciences. Their ignorance is revealed by the language that they use to describe DL.
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“A PhD is definitely not required. All that matters is a deep understanding of AI and ability to implement NNs in a way that is actually useful (latter point is what’s truly hard). Don’t care if you finished high school.”