Q get like remarkable results. They've been able to You know, finally figure out what was, you know, what, what was wrong with them, and they've been able to take that to a doctor. You have doctors using it, too. So medical, I think, is a really interesting area, but there's a whole bunch of these, um, examples of different, different industries are now being impacted. Do you have anything else?
A The one area I think a lot about is, is AI for science, and, and back to, to, to David's initial point about the progress we've seen in these frontier models. I think the very early ones sort of started with just general knowledge, and you have to go back and understand, like, why? And the question was, What was the data available for those model builders to start training their models? And for the early ones, you could just scrape the internet and just kind of cram everything to a model and train it. And, and that's where you kind of had this, this first phase of, of large language models. And the second one was coding. And if you think about how do you get a really good coding model, you get, you have to trade it, you have to train it on existing code. And that was again, something that is, you know, relatively easier to, to acquire than other types of data. And you saw great progress and jumps in, in the coding models. I think that the third big sort of shift that hasn't really been touched on yet, which the government itself is trying to do a push on is the AI for science question and why it's so challenging for scientific discovery to like tie in with the way that LMs are traditionally trained is that the science data is extraordinarily fragmented and it's not done in a way or formatted in a way that can easily be applied to a large language model sort of like training run…
AI assessment note: “The one area I think a lot about is, is AI for science”