Nathan Labenz: belief

Transcript context

…But yeah, I'd like to think we didn't buy our way onto the podcast. Yeah, no, the enthusiasm is definitely is real because I've heard so many great things over time. So excited to get into this. I thought we would maybe just start with kind of big picture from your perspective. And I think, you know, having watched some of your previous talks, I know that you play sort of a portfolio strategy where you're not like, I have a very specific, narrow prediction, and I'm trying to maximize the value of this organization, this program for that very hyper-specific prediction. It seems like you're more saying, well, there's a lot of uncertainty out there in the space, and we're going to try to be valuable across a range of those scenarios as much as we can be. With that said, you can speak on behalf of yourself or on behalf of mentors or the community as a whole. Like, where are you guys right now? Where are we in terms of timelines, so to speak? And how has your strategy evolved over the last year or so as we've gained more information on where we are relative to the singularity? Yeah. Okay. So I don't like to have opinions here or I don't like to have opinions very loudly. And the reason for that, I think, is because as you say, we are somewhat like a hedge fund or something or maybe an index fund, right? more likely, which is to say we have a broad portfolio, we adopt a bunch of different theories of change as valid, and we try and have our thumb in 100 pies. So I would say in terms of Mass's institutional opinion on this, definitely we tend to go with things like metaculous and prediction markets and the Forecasting Research Institute, FRI, their predictions and so on. So the current metaculous prediction for strong AGI, I think it's called, which is I think you can ignore most of the requirements of the test and just look at one of them, which is the two hour adversarial Turing test. That's predicted somewhere around mid 2033. Okay. So I think that is probably the best button we have for when AGI of that nature occurs. Now, we recently just had two days ago or three days ago, perhaps, dropped this new AI futures project, dropped this new report. which two Matt's fellows were, one was the lead author, one was a contributing author on, so very excited about that. And that just was updating their model. And I think they predicted something between 2031, well, 2030 to 2032, depending upon how you define AGI. They broke it down into all these automated coders, like they can do all the coding stuff, these top expert dominating AI across all these fields and so on. So I think, I don't know, somewhere around 2033 seems like a decent bet. But also we had, you know, Nathan Young recently compiled all these different like forecasting platforms. So Metaculous, Manifold, another Metaculous poll that was like for weak AGI, it was like a little bit less good at Turing tests and all these other like, I don't know, some kaushy thing or whether they're opening, I will achieve it. And he came out with an average of 2030. Now, I don't know, I still like the Metaculous 2033, but like I wouldn't bet against 2030 in terms of nearest of AGI. As for superintelligence, complicated, right? Could be six months or less, could be a very hard takeoff after this AGI thing. If it's like a very software only singularity scenario where you don't need a big bunch of hardware scale up, you aren't limited by compute, it's just recursive self-improvement or something, algorithmic improvement, AIs are improving the algorithms of training AIs and it's like, wow, that's a fast feedback loop, right? Or you might need a lot more experimentation, right? You might need massive hardware scale ups. You might need like just staggeringly more compute than exists in the world, in which case that could take you a decade, right, to get your singularity. So I currently think that 2033 is a decent central estimate in terms of the median for what we're preparing for, but obviously 20% chance by 2028. I think that's the metaculous prediction. That's a lot, right?…

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