Nathan Labenz: recommendation

Transcript context

…re in the development of AI, first by achieving the superhuman coder milestone and then the superhuman AI researcher milestone. And I am unhappy to report that I think that story is generally correct. I don't know if the timelines are exactly right, but I my forecast from that process of leading to something that looks like super intelligence around 2031 is roughly stable. I think the things that have happened in the years since AI 2027 come out very much indicate the theory that the most important thing going on is how useful is AI and improving the productivity of AI researchers within Frontier Labs. I've made public predictions that I thought Anthropic was going to run away with it because they had the best feedback loop of talent and actually using their AI internally. I think that has been, you know, n = 1. But I think it's been totally shown that that's been happening recently. So I think that will continue to happen. And Dan's closing confession about the whole project to prediction markets and a hope for what AI forecasting could still become. Maybe just in closing, sketch out a little bit more of the future as you hope it might unfold. Not necessarily the most likely scenario, because maybe the most likely thing is people act foolishly and don't take advantage of the benefits of forecasting. But like, if we really do a good job right, and we're and we're interested in truth seeking and we get the AIS working as well as you think they might, how do you think life feels different? Yeah, I have to leave with another example of me being a bad forecaster. I, I guess everyone who tries forecasting thinks they're a bad forecaster because they see things getting wrong. Here's a prediction that I made really strongly 5 or 10 years ago that has basically been totally falsified. I predicted that if we had highly visible, highly liquid prediction markets that were covering all of the like the major technological and political and economic things going on, that humanity would be wiser and people would make better decisions in government. So here we are. We have Pauline market and call sheet. ajor technological and political and economic things going on, that humanity would be wiser and people would make better decisions in government. So here we are. We have Pauline market and call sheet. I don't see any wisdom or better decisions coming out of all of that gambling going on on those platforms. So for me, part of the what is our AI future is trying to understand the present a little bit better. Why is having thriving prediction markets not transforming, say, the news or how people learn information or plan for their futures? Again, one simple answer is that it does. It just takes a while. We're only about a year into prediction markets having, you know, major headlines and being seen by everybody. Maybe it just takes a while for people to change their habits. A is, if that's the case, can move much faster as a is get better at forecasting. I guess ultimately you said this, Nathan, like we're after the epistemics. Like it's not necessarily just forecasting like predicted this outcome. We want models that are reasonable. And one of the beautiful things about forecasting as a human practice is it makes you more epistemically virtuous. The more that you try to forecast and actually write down what you get wrong and do these post mortems, the more it humbles you and it makes you more open minded. It makes you more of a fox instead of a hedgehog. It just makes you like a more like reasonable person. And so prediction markets with all these people doing this should be leading to people being more reasonable. Again, I think people aren't doing a whole lot of forecasting on prediction markets. They're doing a lot of trading and a lot of gambling, which are related to forecasting but not forecasting. If the AIS get more, if they get better at forecasting and they become better epistemically, then we could be in a world where just talking to a chat bot, you were getting something so much wiser and more grounded and more honest about it's uncertainty and more poking about you and your own uncertainties as the person talking to the chat bot. And that I think could make an absolutely enormous difference. I think again, putting my kind of cold blooded forecasting hat back on, I think that the technological outcomes of AGI will come before the cultural change happens. So I'm very much on the the AI safety camp. I really think we should slow things down, give us more time. We should fund more AI safety research and do more policy because if we have time for the wisdom of having these alien intelligences around helping us, if we can leverage them and actually make better decisions before the critical decisions get made, there's going to be a series of decisions in the 21st century that we're going to look back on, like this decisions made in the 20th century about communism and World War 2 and the atom bomb and all of those things. Those decisions are coming. Maybe some of them have already been made. Those decisions as of right now, I don't think are very well informed by like very rigorously epistemic accurate forecasting AIS. Those decisions are coming. Maybe some of them have already been made. Those decisions as of right now, I don't think are very well informed by like very rigorously epistemic accurate forecasting AIS. But if you just give it another couple of years, we might be in the world where everybody has the same grounding, like as smart as Kissinger, but actually like trying to help and like trying to give better outcomes that we can all have and that could usher us through this crazy phase before the crazy paper clip type of stuff starts to happen. So I feel like I'm racing, you know, from AI forecasting to make it useful and make it help. It's kind of a broader epistemics and safety process because otherwise it's just going to get away from all of us and then a lot of the work we're doing just doesn't matter. Wednesday's guest Zeve Farman, Co founder and CEO of Litrix, the company behind Facetune and now one of the only frontier scale open weights efforts in video and world models under the LTX brand. I've stayed up late making music videos with their audio condition model. In his honour. We started with what a world model even is. OK, wow, that's a big question. Because we released the LTX 2.3 like roughly 1/4 ago. And in the high years it feels like, I don't know, like a decade. OK, so a bunch of things. I think there's like a growing realisation that what started as video models is becoming a backbone of what we call now like world models. And I think like the best way to explain why this is so powerful is to use the analogy to LLMS, right? Like in the end of the day, at their core, LLMS are still predicting the next talk in the next word. And when we do the pre training at the scale of the Internet, it allows us to create models that do textual reasoning incredibly well. And the emerging world models, they're kind of doing the same, right? Like giving some kind of boundary condition, some kind of history, some kind of constraints. They predict the next moment, OK. And the moment includes how the world appears, how it like sounds, and what kind of action we can do. I think the action part is the most maybe surprising one. And like roughly, I would say like 1/4 ago, maybe a bit more in video showed in their Dream 0 paper that it's fairly easy to add to video tokens some kind of encoding of the the joints of the robot and then basically completely ditch the VLA paradigm that was the reigning supreme before it. So I think that's like one of the big surprises. And for us, realizing that was like this big moment that validated something that we always strive for is to create an extremely efficient models.…

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