@headinthebox You verify it separately, in layers or stages. Even with pure stuff, there are many ways to write one thing, a spectrum between "most verifiable" and "most performant". The first gives you the math of your algorithm, and you expand it when doing the more performant version.
Applying Functional Programming to make AI more reliable? HELL YEAH!
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@ahh_soka There's a fairly new branch of algerbraic geometry called singular learning theory which aims to explain the theory behind deep learning, see
https://t.co/ReQDqPtRYC
https://t.co/VqGG0Nkkhi
https://t.co/35wGdWqGDB
@dr_c0d3 it might be interesting to point out that ssreflect was introduced in its day with exactly this goal in mind, i.e., to allow easier proof maintenance by making the tactic scripts less brittle (e.g., by handling names explicitly and avoiding dependent type classes)
@dr_c0d3 If there's some sort of interesting repeating pattern or higher abstraction, then ideally it should be factored out into a separate theory instead of fiddling with lists directly; like in other kinds of programming.
@dr_c0d3 Thanks! My point is that you typically don't really need to read the proof, it's essentially just shuffling around chunks of lists according to some rules; what you need to see is the definition of the theorem.
@dr_c0d3 looks like a pretty standard line of reasoning about some property holding on numerical lists under some blockchain-induced ordering, I've written dozens of proofs like that :)
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@ariil@pandabanda у местных речевые центры очень развиты, это правда; мне тут как-то даже объясняли, что невежливо НЕ перебивать собеседника - это дескать показывает, что ты его не слушаешь
@kreatific @ataiiam@yoheinakajima It's essentially a typelevel tree transducer automaton, only it's constructed interactively by a very big and expensive heuristic, instead of a compiler operating on high-level code.