[6d65]

I think the main selling points of Swift to tensorflow are:

* Speed, ML learning pipelines are often bottlenecked by the data load and transformation. TF had new mechanisms (the last I've seen was TF.data). But a language compiled to native is much more flexible in that regard.

* Type safety. Sometimes issues with the models can pop long after they have been running. The hope is that typed API's will show simple errors at compile times

* Auto differentiation built into the language. If I'm not mistaken, this is more powerful than backpropagation in TF, wich also has autodiff. The idea is that this would allow for more custom models without a performance penalty. My knowledge here is limited, since it's been over 2 years since I've implemented back propagation. I've successfully forgotten most of the things I knew about ML/DL.

I don't have any experience with R, but from what I've heard, it was known to be slow. But that might have changed or I may have misunderstood the situation.

[socialdemocrat]

Does not need to be built into the language. Julia does it great without special built in support. But Julia is an extremely flexible language.

Number types are first class which is a big part of it and you can add extra custom passes to the JIT compiler in regular library code.