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Analyzed from 415 words in the discussion.
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#code#model#pick#better#read#higher#design#class#author#more
Discussion Sentiment
Analyzed from 415 words in the discussion.
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Discussion (5 Comments)Read Original on HackerNews
But they should! The code is the best source of truth on what the software is doing after all.
Instead of giving up on that, we should make it easier to read generated code, e.g. by generating less code in a higher level language.
On the flip side, forcing myself to read all the code also resulted in a smaller, higher quality code base.
Zooming out (but only a little) from the impetus to formalize a commitment to a particular class of result candidate (what the author here is calling "spec elucidation"), we can also imagine this same evolution of concerns being applied in order to cause what we currently term "AI safety" into something more like "AI ethics".
For example, if we can elucidate the specifications for things like peace and justice to ensure that the class of results is formally verified as non-participation in war (or perhaps, further in the future, non-participation in state activities whatsoever), we may be able to throw cold water on all the vitriolic arguments about model capabilities and which need to be banned or delayed lest we accelerate the apocalypse (or whatever is actually on the mind of the ban-this-model constituency).
I like how the author ends tersely with:
> If you have a formal language with the closure properties above — we suspect you would be surprised how many do — we would very much like to hear from you.
That's certainly not me, but I bet it's true that it's somebody.
There are very few things that cannot be stated as dual use, with one totally benign and one totally screwed up. It's like wanting a hammer to distinguish if it's striking a nail for a roof vs. a nail for an illegal animal pen. That's the wrong application of constraints. The hammer shouldn't care.
> This is also why we do not believe PICK becomes less useful as models improve. Better models do not make user intent more articulate — asked for “a regex matching countries of North America”, a more capable model still cannot tell you whether you want the Caribbean included, or where you want to stop heading south. Better models produce better candidates, faster — which shifts user effort precisely toward the work PICK is built to support.