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I'm worried the field will increasingly become a niche where ML takes over cases where accuracy guarantees are not very important, time-to-market is important or where complexity rules out mathematical modeling.

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I don't think that will happen. ML is overhyped and accuracy is going to be more important as everything gets smaller, faster, better. Infact I hope for the opposite: I think with the increase in software domination, "brute forcing" it (or with a ML black-box) won't do, and a calculated system is going to be needed.

Time to market is always important, I think calculating mathematical models might work out quicker than trying to shove ML on it? Our tools for making math models will improve.

(all of this is speculation, no one can predict the future)

Why do you say that? I think each field has its strengths and weaknesses. For example, mission critical applications still tend to avoid using DL algorithms since stability can only be proven in the sense of Lyapunov. There is also RL, which borrows many fundamental concepts from control theory.

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