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Engineering undergrads that I talk to often express a desire to go learn about machine learning, AI, or data science.

Even students in specialized fields, e.g. computer architecture or pure math, often relate their field to machine learning.

Examples:

  • In differential geometry, there's "manifold learning" and "topological data analysis".

  • In FPGA pipeline design, there's "FPGA for deep neural network".

  • Machine learning courses are so popular now a lot of courses offered at my university changes its name to "X + machine learning applications".

    • A traditional course titled "Nonlinear Optimization" is now "Nonlinear optimization for machine learning application".

    • A course on basic Neurobiology is now "Neuroscience and Artificial Intelligence" (or maybe "Human Intelligence and Artificial Intelligence"?).

Questions:

  1. Can anyone who is working in math, science or engineering comment on the phenomenon of the prevalence of machine learning in their own department?

  2. Is the popularity of machine learning taking away students from doing work and advancing traditional research disciplines?

  3. Do you see this as ultimately a good thing for academia as a whole?

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    There have been fads before. At some points, well over 50% of incoming graduate students in CS departments wanted to work in AI. But way fewer than 50% of graduating students ended up doing their theses in AI. – Peter Shor Feb 3 '17 at 1:21
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    (1) More, please. (2) No, of course not. (3) What is "academia as a whole"? – JeffE Feb 3 '17 at 3:58
  • Question (3) seems very opinion-based. It may help to improve this question if it's either removed or reworked. – Nat Jan 31 '18 at 6:00
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    Why has this been closed? I am sure someone has fact-based answers. And I can’t see any answer, let alone opinions here. – famargar Jan 31 '18 at 22:16
  • It has become somewhat popular in physics too. Anywhere with lots of data might find machine learning a useful tool, somewhat like statistical methods. – Anyon Sep 18 '18 at 17:33