TEST VINTAGE MODELS

“And then the the second sort of, I'd say, normal doctor, use case is for decision support. So there's this one company called Open Evidence that has created a free tool that has gone from, again, zero to crazy numbers of adoption. I will tell you younger doctors like my residents use it all the time. I don't know the actual numbers, but it's probably close to half of US doctors are using this right now.”

“So we have a classifier that tries to look for things that are anachronistic. And especially if you wanna use this for forecasting or to evaluate forecasting, it's really important that we really nail this issue. So we have all sorts of ideas for, like, canaries and things that we think, the model should just never assign any likelihood to. Like, think of, I don't know, Nagasaki and Hiroshima. Like, before World War two, like, those those two towns would just never show up in the same sentence ever almost, except for, like, some weird weird coincidence. So you can just tell whether there's been leakage about important events if the model just thinks that there's any chance that you'll see those particular names together.”

“I think this was a case where, like, reality has just finally intruded on the Stargate project. Like, when all of these deals were getting announced initially, this is how they sounded. Well, we're gonna spend 1 batrillion dollars that we don't have to build 40 data centers. And at the time, people said that kind of seems like a lot. Can you guys actually live up to that? And they said, yeah. Just watch us. Well, guess what? They could in another changing course.”

“Not yet, but I think it could be at some point. I mean, so ChatGPT for health pulls in your data from the medical record and lets you chat with your medical records. Now reason number one for concern is privacy. That's obviously gonna have your entire medical history going to a AI company. It's also going to not be redacted by you in a way to remove identifiable things. Reason number two, I I think if we're talking about health record data, it's really messy. They include tabular data. They include copy forwarded data, pay that's been copied and pasted. And they also, if you've ever read your health records, they include things that are wrong. There's a lot of errors or misdocumented things in your health data. And it turns out that just copying a bunch of information, like, they're LOMs aren't magical. You can't just copy your entire medical record in and think that you're gonna get good performance. And I would never bet against the technology. I think that we will get to the point that we have ways to build representations of of humans and understand their health. But right now, there's, like, no advantage to just dumping everything in an LLM, which is what Chachipi do for health theoretically would allow you to do in a way that would allow you to better understand your health.”

“I think the lawsuit and this ongoing litigation between Elon Musk and OpenAI has been very distracting for OpenAI. But, like, as a journalist and as a person who wants to know more about the inner workings of how these companies run, I think it's been actually very valuable for a lot of these emails and early communications between OpenAI leaders to be released as part of this litigation. I have found it very useful in understanding some of the early dynamics, at OpenAI. And it also just illustrates the degree to which these projects are all just sort of fueled by grudges.”

“Yes. So that is the biggest worry that I actually have about sort of the short to medium term is deskilling of the workforce. We have some evidence. There was a sort of scary study last year from Poland on a trial where they gave doctors not a language model, but a polyp detecting technology. And they they looked at their ability to detect polyps, so potentially cancerous lesions in the colon before using it and then after using it for three months. And when not using it, their ability to detect polyps dropped by six percentage points. So these are skilled doctors using, technology, and they lose six absolute percentage points of their ability to detect potentially cancer in three months. And then imagine that you're learning to do it for the first time.”

“We don't think people should take our word for it. And we also don't think that people should trust machine forecast unless they have a track record going back, like, decades and decades. So the idea here is that if we could build a model who really only knew about the world up to a certain date, we could ask it to forecast, like, five or ten years ahead of time. Like, ask it, what's the New York Times headline gonna be five years from now? Or is there gonna be another great war or something? And, we can iterate and see, like, what kinds of things are predictable. What does it take? Like, how far out can things be foreseen? And then, hopefully, eventually, we'll have machines that have, like, a hundred year track record of forecasting. And then we can ask them, you know, in 2026, what do you think is gonna happen, like, you know, two or four, eight years from now? And we'll have an idea of how much to trust those forecasts.”