>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.
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.