In a nutshell, differentiable programming is a programming paradigm in which your program itself can be differentiated. This allows you to set a certain objective you want to optimize, have your program automatically calculate the gradient of itself with regards to this objective, and then fine-tune itself in the direction of this gradient. This is exactly what you do when you train a neural network."
Isn't this just declarative programming as we know it from e.g. Prolog, SQL or other places where the programmer declares what their objective is, and it's left up to the interpreter, compiler or scheduler to figure out the best way to achieve that? And now that's being applied to ML (which probably makes sense, since it involves a lot of manual tweaking). Sounds like a great use case for a library, but hardly worthy of being called a new programming paradigm.
In a nutshell, differentiable programming is a programming paradigm in which your program itself can be differentiated. This allows you to set a certain objective you want to optimize, have your program automatically calculate the gradient of itself with regards to this objective, and then fine-tune itself in the direction of this gradient. This is exactly what you do when you train a neural network."
Isn't this just declarative programming as we know it from e.g. Prolog, SQL or other places where the programmer declares what their objective is, and it's left up to the interpreter, compiler or scheduler to figure out the best way to achieve that? And now that's being applied to ML (which probably makes sense, since it involves a lot of manual tweaking). Sounds like a great use case for a library, but hardly worthy of being called a new programming paradigm.