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by arensc 1955 days ago
Every time I see a post, about TensorFlow for Swift. There seems to be so many misconceptions about why this it was created. The top one being, oh it's Chris L's baby.

As a Software Engineer, who's been working on teams for the past 15 years and seeing the craft devolve, and the market saturate with people whom don't understand that fundamentals but memorize the frameworks, you need a language like Swift to bridge that gap.

It's hard enough to get people to program to an interface, let alone communicate what they are gonna return from a function or how a function behaves. 70% of people don't understand software engineering is legitimately just plumbing, they think its pretty much an artistic endeavour with no rhyme or reason. This makes it pretty difficult to stay on the same page, when building a product to scale up.

Having a strong type system solves that problem. When working with ML people my question 100% of the time is what does that function, return what type?, so I can build off what you are doing, or even debugging requires understanding of the type and the values.

It seems like team orientated programming is foreign to most people.

Swift has a low cognitive load pushing you to solve problems, with Software Engineering and reducing communication lead time between engineers.

All languages eventually converge Swift, I think Chris L has solved the language UI problem.

2 comments

There is nothing special about Swift that ML languages, or .NET eco-system didn't offered already, Chris L might be a very good technical person, with several achievements, however Swift without iOS is just yet another ML/Modula-3 language with "modern" syntax.
Most people regret that Google did not invest in Julia but, to my mind, it misses the point. Swift for TensorFlow's unique selling point was bringing a strong type system and ML+autodiferentiation together. That, for me, would be a huge boost to push ML code to production quality.