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Certigrad: bug-free machine learning on stochastic computation graphs

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Re: Certigrad: bug-free machine learning on stochastic computation graphs

#5
I find the appeal of formally proven languages somewhat confusing. All you're doing is moving the bugs from the source code to the specification. (Alternately, you can think of source code as a 'specification' for a compiled program. You still have to transfer the same amount of information to the computer.)

Re: Certigrad: bug-free machine learning on stochastic computation graphs

#6
post #5

I find the appeal of formally proven languages somewhat confusing. All you're doing is moving the bugs from the source code to the specification. (Alternately, you can think of source code as a 'specification' for a compiled program. You still have to transfer the same amount of information to the computer.)

The specification is a lot smaller than the code, and so it's easier to read and manually verify that it's correct.

Re: Certigrad: bug-free machine learning on stochastic computation graphs

#7
post #6
post #5

I find the appeal of formally proven languages somewhat confusing. All you're doing is moving the bugs from the source code to the specification. (Alternately, you can think of source code as a 'specification' for a compiled program. You still have to transfer the same amount of information to the computer.)

The specification is a lot smaller than the code, and so it's easier to read and manually verify that it's correct.

How is that the case in this specific example? It looks a lot harder to check for correctness.

Re: Certigrad: bug-free machine learning on stochastic computation graphs

#9
post #5

I find the appeal of formally proven languages somewhat confusing. All you're doing is moving the bugs from the source code to the specification. (Alternately, you can think of source code as a 'specification' for a compiled program. You still have to transfer the same amount of information to the computer.)

You have to make sure your spec is right, but you also have to make sure your tests are right, and people are pretty happy with the idea of testing.

Re: Certigrad: bug-free machine learning on stochastic computation graphs

#10
This seems to be a newly hot topic, whether to fight adversarial inputs or looking for efficiencies. I bookmarked a few relevant blogs (but really need to read more deeply)

http://composition.al/blog/2017/05/31/proving-that-safety-cr...

http://www.cleverhans.io/security/privacy/ml/2017/06/14/veri...

https://blog.foretellix.com/

and another preprint: https://arxiv.org/abs/1706.10268

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