Trenton Bricken: belief

Transcript context

…This is really important. The ruthless prioritization is something which I think separates a lot of quality research from research that doesn't necessarily succeed as much. We're in this funny field where so much of our initial theoretical understanding is broken down basically. So you need to have this simplicity bias and ruthless prioritization over what's actually going wrong. I think that's one of the things that separates the most effective people. They don't necessarily get too attached to using a given sort of solution that they are familiar with, but rather they attack the problem directly. You see this a lot in people who come in with a specific academic background. They try to solve problems with that toolbox but the best people are people who expand the toolbox dramatically. They're running around and they're taking ideas from reinforcement learning, but also from optimization theory. And also they have a great understanding of systems. So they know what the sort of constraints that bound the problem are and they're good engineers. They can iterate and try ideas fast. By far the best researchers I've seen, they all have the ability to try experiments really, really, really, really, really fast. That’s cycle time at smaller scales. Cycle time separates people. Machine learning research is just so empirical. This is honestly one reason why I think our solutions might end up looking more brain-like than otherwise. Even though we wouldn't want to admit it, the whole community is kind of doing greedy evolutionary optimization over the landscape of possible AI architectures and everything else. It’s no better than evolution. And that’s not even a slight against evolution. That's such an interesting idea. I'm still confused on what will be the bottleneck. What would have to be true of an agent such that it sped up your research? So in the Alec Radford example where he apparently already has the equivalent of Copilot for his Jupyter notebook experiments, is it just that if he had enough of those he would be a dramatically faster researcher? So you're not automating the humans, you're just making the most effective researchers who have great taste, more effective and running the experiments for them? You're still working at the point at which the intelligence explosion is happening? Is that what you're saying?…

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