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naraburns  nihil supernum  1yr ago (text post)   32952 thread views

Culture War Roundup for the week of February 27, 2023

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The problem we are looking here isn't doing selfplay for optimal code. The problem is to write something into a random adversarial environment. AI dominates Chess and Go with clear rules and perfect information that has trained through self play, but for Poker the results aren't as clear cut. All of that because of randomness and hidden information. So putting code into a distributed system within an organization full of internal corporate politics where a manager somewhere wants to sabotage and also there are external advesaries that want to mess with your system. Sure it can write optimal code for your computer through selfplay but actually delivering something to an enterprise setting that is a different ballgame, it is Chess vs Poker.

And even with perfect formal rules AI can still be tricked https://arstechnica.com/information-technology/2022/11/new-go-playing-trick-defeats-world-class-go-ai-but-loses-to-human-amateurs/

"Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the Universe trying to produce bigger and better idiots. So far, the Universe is winning."

--Rich Cook

I don't think that has changed...

I'm just saying that inasmuch as LLMs are weak specifically at targeting objective metrics like performance, self-play should improve it. I'm not saying self-play is the panacea that'll give us AI, just that it will fill a hole in the existing methods.

I don't think that we are disagreeing at all here I'm just pointing out that having a target for self-play is going to be difficult. Because there are multiple dimensions to the problem of "not writing trash code" as it depends on whether or not it needs a theory of mind of actual people. Needing a theory of mind precludes self-play, that is always going to require input data.

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