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It's possible that it's less effort for a top notch compiler developer to come in and fix Julia's compiler than it is to give Swift the data science ecosystem t

by cwyers 7y ago

It's possible that it's less effort for a top notch compiler developer to come in and fix Julia's compiler than it is to give Swift the data science ecosystem that Julia is building. I have little experience with it, but it's a question of tradeoffs.

orbifold 7y ago
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.

by cwyers 7y ago

orbifold 7y ago

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.