Also important to recognize that many PhDs are extreme specialists and tend to have an underdeveloped sense of general ideas. They are unaware of many ideas in adjacent fields. This is can be a detrimental blind spot in any emerging new field.
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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