[tc]

They want to build up the language compiler itself to better support machine learning. That requires a language with enough type information to support sophisticated analysis. It should be built on LLVM. It needs an existing ecosystem of tools and libraries. And having a better performing language than Python is likely a win.

Swift fits the requirements. As Chris Lattner is driving this, no one could ask that he choose something else.

But Rust also would have been a plausible choice. As the Rust team is very interested in applying the language to exciting new use-cases, it's a bit of a loss for it to miss out on collaboration here. Perhaps this could inspire similar work on the Rust side; many of the concepts would likely transfer straightforwardly.

[jpz]

The principle reason Python flourishes is because it's highly expressive and readable. (There are things it misses, such as sensible lambda, any reasonable multi-threading model, etc.)

I can't see Rust competing on similar merits. Swift however (like Kotlin to which it is extremely similar) seem to be in the right sweet spot in terms of languages designed for usability, with a lot of useful f.p. constructs.

[swsieber]

It depends on if all your writing cutom functionality. If all you have is one big main function, rust is pretty readable. Function definitions and structs are where things get a little messier.

[jpz]

I can't agree - there's too much going on in Rust - most people using Python want something approaching Matlab or R for ease of use.

I'm not saying Rust doesn't have it's sweet spot - but I can't see that as a general purpose language. (Likewise Haskell and Scala - that's just my opinion though :))

[tormeh]

For better and worse, I wouldn't say anything so absolute about Scala. Scala contains multitudes ;)

You can write Scala as Java or Haskell or anything in between.

[swebs]

>And having a better performing language than Python is likely a win

I was under the impression that most Python libraries for machine learning (numpy, scipy, tensorflow, etc) are running C code under the hood. How much is Python's performance really holding it back?

[ChrisRackauckas]

>They want to build up the language compiler itself to better support machine learning. That requires a language with enough type information to support sophisticated analysis. It should be built on LLVM. It needs an existing ecosystem of tools and libraries. And having a better performing language than Python is likely a win.

You literally just described Julia. The Julia core developers are all part of Julia Computing (which contracts for ML work) or are somehow associated with data science and machine learning. For this reason there's a lot of on tooling for building computational graphs / performing AD, compiling for TPUs, etc. It's built on LLVM. It has loads of packages for optimization, machine learning (like 4 NN libraries, a few different ways to work with GPUs), data science, etc. And type-stable Julia code is as fast as C. So it's interesting that this gets some kind of noteworthy announcement when it's already been done and already been done well.