Dwarkesh Patel: belief

Transcript context

…I think if you instead got someone who is really good at quickly picking up a bunch of different domains and you gave them some time to train and talk to people and shore up their expertise and do some practice, they would actually do a pretty good job. I think most domains are fundamentally pretty shallow, where a very smart generalist who’s good at a limited subset of core skills can get going pretty quickly. That’s not true for literally every domain. My sense is that the AIs will develop increasingly good mechanisms for quickly acquiring understanding and expertise in a given domain. Consider, for example, how fast AIs can understand a new code base. AIs can understand a new code base much faster than humans can, but to a degree that’s shallower than humans could currently understand. But it’s getting better over time. Let me spell that argument out a bit more. Let’s say you take Fable 5 or Mythos 5 or whatever, and you wanted to make some kind of complicated change to a really massive code base. The model will get some understanding of the code base very fast, in the course of maybe significantly less than an hour, potentially much less than an hour. Then its understanding of the code base will plateau a little bit, where it won’t get as deep of an understanding as a human would have gotten over a much longer period. So it’s like an AI in an hour can match a human with a few weeks maybe, depending on the details of exactly how complicated the code base is. But it won’t match a human who’s been working on that code base for two years or whatever. But over time, the amount of understanding AIs can match has gone up. If we look at 3.7 Sonnet or 3.5 Sonnet, maybe it could only match the equivalent of understanding a code base for a day or something. But now AIs are much better at building context about a task. So you can be like, “Mythos, I want you to really understand this code base, and then implement this feature.” It will spawn a bajillion sub-agents. Those sub-agents will pore over a bunch of things. It will deliver a bunch of context back. It will then investigate a few things. It’s not amazing at doing this, but it can happen really fast, and it can work pretty well. And it’s not very hard for me to imagine how you could train AIs to be increasingly good at this task. The task of implementing some very complicated feature in some reasonable way in a very big code base is extremely verifiable, and that can be a thing the AIs improve on. Similarly, there’s a broader skill of quickly understanding context and being able to have a bunch of different AIs learn in parallel and then merging that together. I think there seems to be a crux here, which I think is just an empirical question we’ll see. How good is the transfer between getting really, really good at understanding the situation, getting up to speed, making progress over long periods in verifiable domains — which the AIs are obviously getting way, way better at really fast — to, “Okay, go talk to the president and convince him to do X thing.” Or, “You’re now in charge of Google. You must make Google a much more profitable company this quarter.” 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.…

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