Kanjun Qiu: preference

Transcript context

…are a good idea. On the theoretical side, we like really believe in like deeply understanding, like when we actually fine tune on individual examples, like what's going on, when we're pre-training, what's going on, like debugging tools for these agents to understand like what's going on. And then on the regulation side, I think there's actually a lot of regulation that already covers many of the dangers like that people are talking about. And there are areas where there's not much regulation. And so we focus on those areas where there's not much regulation. So some of our work is actually, we built an agent that helped us analyze the 20,000 pages of policy proposals submitted to the Department of Commerce request for AI policy proposals. We looked at what were the problems people brought up and what were the solutions they presented and then like did a summary analysis and kind of like, you know, build agents to do that. And now the Department of Commerce is like interested in using that as a tool to like analyze proposals. And so a lot of what we're trying to do on the regulation side is like actually figure out where is there regulation missing and how do we actually in a very targeted way try to solve those missing areas. So I guess if I were to say like, what are the takeaways? It's like the takeaway is like the future could be really exciting if we can actually get agents that are able to do these bigger things. Reasoning is the biggest blocker plus like these sets of abstractions to make things more robust and reliable. And there are, you know, things where we have to be quite careful and thoughtful about how do we deploy these and what kind of regulation should go along with it so that this is actually a technology that where we, when we deploy it, it is protective to people and not harmful. Awesome, wonderful.…

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