My professor has talked about this. He thinks that the real gem of the deep learning revolution is the ability to take the derivative of arbitrary code and use that to optimize. Deep learning is just one application of that, but there are tons more.
Also, just curious, what are your studies in?
Unfortunately the CS field is only just rediscovering control theory while it has been a staple of EE for years. However, there haven't been many new innovations in the field until recently when ML became the new hottest thing.
For the ones interested there is a book that discusses both: 'Reinforcement Learning and Optimal Control', by Dimitri P. Bertsekas. It covers exact and approximate Dynamic Programming, finite and infinite horizon problems, deterministic and stochastic models, model-based and model-free optimization.
Aside from this book, Ben Recht has some interesting blog about Optimal Control and Reinforcement learning: http://www.argmin.net/2018/06/25/outsider-rl
It seems really weird that control theory is in EE departments considering it's sooo much more mathematical than most EE subdisciplines except signals processing. I remember a math professor of mine telling us about optimization techniques that control systems practitioners would know more about than applied mathematicians because they were developed specifically for the field, can't remember what the techniques were though ...
https://news.ycombinator.com/item?id=8417882
> It seems really weird that control theory is in EE departments considering it's sooo much more mathematical than most EE subdisciplines except signals processing.
I agree, apparently Bellman's reasoning for calling dynamic programming what it is was because he needed grant funding during the Cold War days and was advised to give his mathematical theories a more "interesting" name.
https://en.m.wikipedia.org/wiki/Dynamic_programming#History
The generalised form of the Bellman Equation (co-formulated by Kalman of the Kalman filters fame) to control theory and EE is in some ways what the Maximum Likelihood function is to ML.
https://en.m.wikipedia.org/wiki/Hamilton%E2%80%93Jacobi%E2%8...
That hilarious and sadly insightful. I remember thinking "what the hell is so 'dynamic' about this?" the first time I learned about dynamic programming. Although "memoitative programming" sounds pretty fancy too, lol
Sometimes there are other better ways to describe "how does changing x affect y". Derivatives are powerful but they are not the only possible description of such relationships.
I'm very excited for what other things future "compilers" will be able to do to programs besides differentiation. That's just the beginning.