Sergey Levine: recommendation

Transcript context

…And then specifically make that the loss function, right? That's right. But here's the thing with this. There's a set of these ideas that are all going to be something like, “Train to make it better on the real thing by leveraging something else.” The key linchpin for all of that is the ability to train it to be better on the real thing. I suspect in reality we might not even need to do something quite so explicit. Meta learning is emergent, as you pointed out before. LLMs essentially do a kind of meta learning via in-context learning. We can debate how much that's learning or not, but the point is that large powerful models trained on the right objective and on real data, get much better at leveraging all the other stuff. I think that's actually the key. Coming back to your airplane pilot, the airplane pilot is trained on a real world objective. Their objective is to be a good airplane pilot, to be successful, to have a good career. All of that kind of propagates back into the actions they take and leveraging all these other data sources. So what I think is actually the key here to leveraging auxiliary data sources including simulation, is to build the right foundation model that is really good and has those emergent abilities. To your point, to get really good like that, it has to have the right objective. Now we know how to get the right objective out of real world data, maybe we can get it out of other things, but that's harder right now. Again, we can look to the examples of what happened in other fields. These days if someone trains an LLM for solving complex problems, they're using lots of synthetic data. The reason they're able to leverage that synthetic data effectively is because they have this starting point that is trained on lots of real data that gets it. Once it gets it, then it's more able to leverage all this other stuff. Perhaps ironically, the key to leveraging other data sources including simulation, is to get really good at using real data, understand what's up with the world, and then you can fruitfully use all this stuff. Once we have, in 2035 or 2030, basically this sci-fi world, are you optimistic about the ability of true AGIs to build simulations in which they are rehearsing skills that no human or AI has ever had a chance to practice before? They need to practice to be astronauts because we're building the Dyson sphere and they can just do that in simulation. Or will the issue with simulation continue to be one regardless of how smart the models get?…

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