[dual_basis]

Reading the link, it seems that Jeremy Howard only tried out Swift because Swift for Tensorflow existed. I got the impression from your comment that using Swift for this purpose was somehow "in-the-air" at the time, and many people independently considered it, but I don't think this is true. My impression is quite the contrary - when they announced Tensorflow support for Swift, I think many people were surprised.

[taharvey]

In his lecture, he specifically said he interviewed both the Swift and Julia teams and got a view inside what happening at Google... and the the Swift teams work, development, and vision was well beyond what the Julia team had even envisioned.

[ChrisRackauckas]

Interesting since the manifesto hasn't mentioned something that's not already completed in Julia. Is their main project or motivation something that is not being shared? I find it quite odd that this manifesto doesn't mention anything that actually requires differentiable programming (old adjoint equations for their applications have existed since the 90's), which makes it a very odd justification for this work.

[taharvey]

I suspect his comments are about the vision for the whole solution and environment, not just auto-diff, which is just a piece.

Auto-diff extends back 40 years. But the relative obscurity of its usage is that it is a "add-on", meta-programming step, or/and a limited sub-set of a language.

The Swift approach of 1st class compiler support is fairly unique. But also supports a future view of computing. If many problems can be solved by differentiable programming, not just the typical but somewhat niche NN/ML problems, then it needs to built into the whole language, not a sub-set.

But more importantly the future requirements, and death of Moore's Law means a fast, deeply statically analyzable language will be required to make the most of a heterogeneous computing environment.