[thedataangel]

This is actually huge. I saw a proof of concept of something like this in Haskell a few years back, but it's amazing it see it (probably) making it into the core of a mainstream language.

This may let them capture a large chunk of the ML market from Python - and hopefully greatly improve ML apis while they're at it.

[krapht]

Huh? Nobody is writing numerically intensive libraries in Python. Clearly this language proposal is taking aim at C++ and Fortran. Even if this caused TensorFlow & others to rewrite everything in Swift, people would write Python bindings to it and keep using Python.

I'll get excited if Apple actually merges this into Swift. It's a niche feature that their compiler team will need to maintain forever. I actually have been working on algorithmic differentiation in C++, so it's not even that I wouldn't want to try Swift out if it actually made it in. However, because this sort of thing is of such narrow interest I believe the future will stay with embedded DSLs / libraries / ugly macro/template hackery.

[mlevental]

lol this is literally by the group that's rewriting tensorflow in Swift https://www.tensorflow.org/swift so you're off on the intention here in that it is exactly taking aim at python as the main data ecosystem language.

[seanmcdirmid]

> I saw a proof of concept of something like this in Haskell a few years back, but it's amazing it see it (probably) making it into the core of a mainstream language.

Probably Conal Elliott’s work, eg in Vertigo (http://conal.net/Vertigo/, circa 2005)? There he was using it for normal computations used in pixel shading, pretty cool stuff. He is still active in this field, and has a lot of new papers that are more ML focused. I do wonder if “general” AD support will be useful for computer graphics as well as ML?

[thedataangel]

It was Conal Elliott's stuff, though it was a more recent paper - Compiling to Categories, if memory serves me.