Dwarkesh Patel: belief

Transcript context

…Let me try to spell out a few more arguments that are maybe relevant. One thing is, when looking at how the AIs have improved at essay writing… Let’s talk about that a little bit. You can get some data even on these domains. AIs will be able to get some data even on these domains when on a very fast progress trajectory. Maybe it’s hard to build a verifiable environment for “was your essay really good according to humans?” But you can do a bit of that. You can do some training. You can do some online training. The AIs will be able to do some online training based on real-world stuff. They’ll be able to have evals. They’ll be able to sample that. You can scale up the cadence at which you do this. The second thing is that in practice, when I just look at the transfer, it seems okay. I think the AIs have in fact improved a bunch at non-verifiable domains, and it’s hard to point to domains that are really hard to verify on which the amount of improvement between GPT-4 and Mythos hasn’t been pretty high in practice. Now, that doesn’t mean that Mythos is better than the best humans or something. It can still be significantly worse than typical human professionals at some aspect of their job while still being way better than GPT-4, which was not even close. So we’re talking about how much progress has come from data versus algorithmic progress over the last few years. That reminds me, I’m actually running an experiment with this with Jerry Han, who’s still a college student. What we’re basically doing to evaluate how much progress is coming from data versus algorithms is training the best algorithmic recipe from 2019 till now with the best data from the 2026 data file, and then also training the different data files going back from 2019 to 2026 with the current best algorithmic recipe. I think that will be interesting. I’m curious if you want to pre-register what amount of compute multipliers are coming from one versus the other. We need to be pretty careful with what we mean when we say the word data. I was trying to be pretty careful to distinguish between scaling up spending on getting human experts to label data, or scaling up the amount of human expert-labeled data. The reason why we have a better pre-training data set now versus in 2019 is not because people are spending way more money getting human experts to type up data that the AIs are then trained on.…

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