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  2. 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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  3. 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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  4. 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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  5. 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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  7. 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 understand the complexity science literature.

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  8. 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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  9. I have both a physics and a computer science background. But when I began to study deep learning, I became aware of my knowledge deficiency. This required me to hit the books hard studying evolution, biology, neuroscience, and psychology.

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  10. It's also very important to realize that Deep Learning requires a ton of plumbing (i.e. technology). Many times, a data scientist doesn't have the requisite understanding of the technology underneath their tools. One should thus never trust a data scientist to build software!

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  11. To conclude, a deep learning PhD who cannot speak coherently about other fields like biology or physics has questionable foundations. Unfortunately, most CS curriculums don't fit in the time to understand adjacent fields.

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  12. The ideas of connectionism are very different from the abstractions that come from GOFAI. To understand these, you have to go elsewhere: physics, biology, ecology, economics, psychology, neuroscience etc. It's not found in mathermatical programming.

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  13. If your goal is to be an expert in theoretical computer science (CS), then a PhD is important. However, an expert in deep learning requires a different set (but overlapping) of knowledge from that found in CS. An analogy is the difference between GOFAI and connectionism.

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  14. This is perhaps why a person with an experimental science PhD may be more capable than a computer science PhD in data science type jobs. The conventional computer science curriculum does not teach what needs to be taught about how models are created about the physical world.

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  15. This said, PhDs with experimental science backgrounds have relevant data science experience that someone in a data science boot camp cannot acquire in 3 months' time. There are certain tacit knowledge that cannot be learned quickly enough.

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  16. 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.

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  17. Also, remember that the director of AI at Tesla also does not have a Ph.D. So one cannot fault Tesla for a recruitment strategy that seeks to maximize finding the best AI talent.

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  18. Elon Musk who did not complete his PhD in Physics should recognize the minimum educational requirement to seek first principles.

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  19. 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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    2) Flu shot is important. If the coronavirus 🦠 arrives, you might be less confused if you have the flu. And therefore seek testing/treatment faster. You block out one possibility, which improves your preparedness. And you’d be fighting fewer viruses. Chance favors the prepared.

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    prije 20 sati

    FACE RECOGNITION My angry article in Mail on Sunday "It’s as if we are standing in a perpetual identity parade without ever being told, let alone being asked Creepy Orwellian UnBritish Racist cites (pic in online version)

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