Swift: Google’s Bet on Differentiable Programming

Re:Swift: Google’s Bet on Differentiable Programming by abra1010 on Thursday April 09, 2020 @09:38AM (#22822398)

> the need for a statically typed "differentiable" language is extremely high

This is not what Google has found, actually. Teams who wanted to use this for research found that a static language is not flexible enough when they want to generate graphs at runtime. This is apparently pretty common these days, and obviously Python allows it. Especially with JAX that traces code for autodiff

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Re:Swift: Google’s Bet on Differentiable Programming by MiroF on Thursday April 09, 2020 @09:53AM (#22822537)

> This is not what Google has found, actually.

Or has at least found that existing solutions for statically typed "differentiable" programming are ineffective, and I'd agree.

But having some way to check types/properties of tensors that you are doing operations to would really help to make sure you don't get your one hidden dimension accidentally switched with the other or something. Some of these problems are silent and need something other than dynamic runtime checking to find them, even if it's just a bolt-on type checker to python.

There are a lot of issues with our current approach of just using memory and indexed dimensions. [0]

[0]: http://nlp.seas.harvard.edu/NamedTensor

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Re:Swift: Google’s Bet on Differentiable Programming by danielscrubs on Thursday April 09, 2020 @11:29AM (#22823473)

Flexible enough or just... former statisticians have enough on their plate than learning programming so lets use the simplest popular language in existence?

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