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Well from my point of view, the largest advantage that Swift has over Julia is that it has a pretty nice static type system. If you look at what they did with t

Well from my point of view, the largest advantage that Swift has over Julia is that it has a pretty nice static type system. If you look at what they did with the Differentiable protocol, then this is precisely what you want from a type system perspective without going all in on dependent types. Historically there are very few examples where a dynamically typed language was retrofitted with a static type system successfully (Typescript and Javascript being one example). Julia's multi methods are really nice but produce horrible errors when they fail at runtime, I don't see an obvious way to fix that. There is a bigger chance that another contender arises within the C#/F# ecosystem as an evolution of the ML.Net work, then that a company invests significant resources in Julia to make it suitable for large scale machine learning in a non scientific "blue collar" setting.

FridgeSeal 7y ago
> Julia's multi methods are really nice but produce horrible errors when they fail at runtime, I don't see an obvious way to fix that. What? I have had no trouble with the errors produced from multiple dispatch! More often than not they've helped me get straight to the fix, which is more than I can ever say for Python. > There is a bigger chance that another contender arises within the C#/F# ecosystem as an evolution of the ML.Net work I would rather claw my eyes out than do significant data work in .Net. C# is our language of choice at work, and it's pretty great for them, but my god is it the most verbose, confusing and convoluted language I've ever had to deal with. F# isn't too bad, but once again, lacks any serious data science community and package support. They're also not terribly fast languages, which is a pretty important factor in ML stuff.