It seems like AD is a solved problem, which is not. I can personally think two instances in which I have hand rolled my own gradients: in the context of recommender systems (dense matrices are evil) and right now in the context of real time collision detection (memory allocations are evil).
One of the domains where I hope Julia will excel is precisely as you comment on physics + ml (I check frequently if MuJoCo source code is available at last).
Anyway, I encourage everyone with any background in scientific computing or DS to have a look at Julia. The ecosystem is nowhere near Python yet, but the language is very good and the tooling is getting better. The performance Julia provides without interfacing C/C++ or Fortran is not just a convenience, it has architectural consequences. It's not about of coding faster, it's about coding "further".
It seems like AD is a solved problem, which is not. I can personally think two instances in which I have hand rolled my own gradients: in the context of recommender systems (dense matrices are evil) and right now in the context of real time collision detection (memory allocations are evil).
One of the domains where I hope Julia will excel is precisely as you comment on physics + ml (I check frequently if MuJoCo source code is available at last).
Anyway, I encourage everyone with any background in scientific computing or DS to have a look at Julia. The ecosystem is nowhere near Python yet, but the language is very good and the tooling is getting better. The performance Julia provides without interfacing C/C++ or Fortran is not just a convenience, it has architectural consequences. It's not about of coding faster, it's about coding "further".