I'm really excited about the prospects of this project. I particularly like the ideas differentiability unlocks once it's a language feature.
For instance, it's possible to write a differentiable rigid body simulator like MuJoCo or Bullet. This has a potential to unlock a new class of reinforcement learning algorithms that can take advantage of the gradients passing directly through in place of very inefficient sampling.
Differentiable simulations in general are the holy grail of optimization and system identification. These ideas have been around since the 70s or so. The bottle neck up until now has been the implementation.
For instance, it's possible to write a differentiable rigid body simulator like MuJoCo or Bullet. This has a potential to unlock a new class of reinforcement learning algorithms that can take advantage of the gradients passing directly through in place of very inefficient sampling.
Differentiable simulations in general are the holy grail of optimization and system identification. These ideas have been around since the 70s or so. The bottle neck up until now has been the implementation.