Dwarkesh Patel: belief

Transcript context

…2018. So if you’re back in 2015, there’s not an automated procedure one can easily imagine for knowing which paper is the scaling laws paper versus which is just another random plot. Even in the Go case, it’s a hard-to-verify outer loop, and the whole idea of an outer loop is to have some backstop on improvement, let alone for general AGI, where of course we have a bunch of these benchmarks. But there’s a problem. We know the things we can measure, and we improve on the things we can measure. We care about this broader ability to do economically useful work, which is not super easy to measure, at least until you automate everything. There’s a question of how good the outer verification loop is for AI self-improvement, and does that matter? I’m going to give a non-rigorous argument, but one that I intuitively believe. DeepMind started with a focus on games. They used games as their outer loop, and then their researchers learned from the experience of solving games, and now they’re working on LLMs. Presumably, there was some positive transfer from their time working on games and Atari and Go and StarCraft that now helps them make good LLMs. I assume that there’s positive transfer in some regard, whether it’s coding or general research ability or project management. All these things probably help them do well. If that’s the case, why wouldn’t it also be true for automated AI researchers? They should be able to positively transfer experience tackling quick-to-verify, quick-to-iterate environments to something more ambitious and economically useful, like automating drug discovery.…

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