Nathan Lambert: uncertainty

Transcript context

…It’s like, “Oh, and I made 10% higher price. Awesome.” And am I willing to say that for like, “Hey, book me a flight to [inaudible 04:28:18].” Right? And it’s like, yeah, whatever. I think computers and real world and the open world are really, really messy, but if you start defining the problem in narrow regions, people are going to be able to create very, very productive things and ratchet down cost massively, right? Now, crazy things like robotics in the home, those are going to be a lot harder to do just like self-driving because there’s just a billion different failure modes, but agents that can navigate a certain set of websites and do certain sets of tasks or take a photo of your fridge or upload your recipes and then it figures out what to order from Amazon/Whole Foods food delivery, and that’s going to be pretty quick and easy to do, I think. So it’s going to be a whole range of business outcomes and it’s going to be tons of optimism around people can just figure out ways to make money. To be clear, these sandboxes already exist in research. There are people who have built clones of all the most popular websites of Google, Amazon, blah, blah, blah, to make it so that there’s… And I mean open AI probably has them internally to train these things. It’s the same as DeepMind’s robotics team for years has had clusters for robotics where you interact with robots fully, remotely. They just have a lab in London and you send tasks to it, arrange the blocks, and you do this research. Obviously there’s techs there that fix stuff, but we’ve turned these cranks of automation before. You go from sandbox to progress and then you add one more domain at a time and generalize, I think. And the history of NLP and language processing instruction, tuning and tasks per language model used to be like one language model did one task, and then in the instruction tuning literature, there’s this point where you start adding more and more tasks together where it just starts to generalize to every task. And we don’t know where on this curve we are. I think for reasoning with this RL and verifiable domains, we’re early, but we don’t know where the point is where you just start training on enough domains and poof, more domains just start working. And you’ve crossed the generalization barrier. Well, what do you think about the programming context? So software engineering, that’s where I personally, and I know a lot of people interact with AI the most.…

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