Daniel Kokotajlo: belief

Transcript context

…And then a little bit more on scenarios. So I think that the human mind sucks in light of scenarios. It takes them very seriously. The vivid details I mean, there's lots of psychological literature on this overwhelm people's ability to see things. What I would like to see actually would be a distribution of scenarios. So when you put Scott Alexander, who's a brilliant writer, or at least a very compelling writer of a certain sort, into making 1 scenario vivid, everybody goes home and thinks that that scenario is real. But you and I know that that was 1 scenario of many. There's reasons to consider that scenario. It's sort of, you know, the darker 1 is is a very vivid version of the dark scenario. But really we want to understand the distribution of scenario. And that's a lot more work. And I'm sure it took you, what, a few person years or something like that to put together that report. There were multiple people involved. You probably worked on it for a while. And so it's a big ask. But what I would like to see is really a distribution of scenarios. We're working on it. I agree with the problem you're pointing out. We currently have a project to make a good ending, so to speak, at a similar level of detail to what we already have. And then also a mini project to make a more scrappy spread of possible scenarios illustrating different stuff at lower levels of detail, like just like a few pages each. Yeah. I I think that that would be helpful. My own personal scenario is, like, in 3 or 4 years, neurosymbolic AI starts to take off. Like, it's I already see signs of this. AlphaFold just won a Nobel Prize. That's a nice thing for neurosymbolic AI. The conferences for it are getting bigger and and and so forth. And I think eventually there will be a state change. I find it very hard to know when there will be a state change. But I think in 02/1935, we will look at LLMs and be like, nice try. We still use them for some things, but that wasn't really the answer. I think Yallin Lakoun would say the same thing. Again, despite our differences, I think we both think LLMs are not really the route to AGI and that when we get there, it's gonna look pretty different. It might use LLMs. They're great at kind of distributional learning. It might replace them because they're very inefficient in terms of energy and data. So somebody might find a better way to do the same kind of thing of of learning the models of distributions of things, which is a super helpful cognitive skill. It's not the only 1 that's super helpful. But we'll have much better ways of doing reasoning and planning. We'll have much more stable world models. I think it will take 5 or 10 years to develop that. I think the semantics that the current models have is very superficial. It's really about distributions of words. And then we need a deeper 1. Like, if you talk about 3 in a row, you should understand what a 3 in a row is. And I think we're missing something to get that. We will get it. Like, I don't think it's…

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