David Rein: belief

Transcript context

…they are, Will McCaskill on the Sam Harris podcast last night. It was a great conversation. But he was kind of talking about AI risk as maybe in a year, maybe in 2 years, we'll have AI models doing things that are like a month or 2 months for a human. And at the moment, I don't think there are any tasks over 30 hours that have been evaluated by humans. And then we get into this question of, if the public discourse is talking about the least constrained region of the graph, Are we getting into extrapolation here? Like how legitimate is it for us to talk about AI might be able to do things that take a month or 2 months? Predicting things is hard, especially about the future. I I and and, yeah, I think, you know, are a lot of different, kind of perspectives or or kind of prior beliefs people people can have that, you know, I think there's a wide range of kind of reasonable, judgments about where we're going to be. But of course, doing that kind of prediction is a different activity than talking about data that has been collected with a concrete methodology. We have the results already. 1 thing I can say, I have been surprised, I think, to some extent, by kind of how well trend line, the kind of original trend line has held up. And I do think that is like some evidence. I'd maybe say for me at least, it's kind of decent evidence about where things will go. A colleague of mine recently a kind of short blog post talking about this kind of intuition of straight lines on graphs. Lots of people have different models of how progress is happening and what's going on. But a very if you have observed a really kind of robust trend over a decent period of time, I think especially in AI where progress is to a decent extent kind of systematic, I definitely do kind of put weight on that trend continuing. But I mean, yeah, there are a bunch of reasons why it might not. I think software engineering is a specification acquisition problem. So it's very difficult. We don't know ahead of time what we're building. I'm sure you folks can attest to this, right? So you build some software, and the first version is buggy, and your users use it, and you find lots of edge cases, and then you revise it. And then you have this kind of, thing in your mind, you know, after the tenth revision, and you've kind of created these lovely representations and abstractions and coarse grainings. And you kind of say to yourself, you know what? If I could throw all the code away, could build it 10 times quicker because I know exactly what to do now because I've actually enacted the intelligence. I've actually built the you know, I've found the contours of the domain. It's now an automation problem, basically. And in a sense, contamination thing is a concern for me because when people use Claude code, they're taking your data. So there are people out there that are writing kernel compilers and there are people out there doing all of these different things. And you know Anthropic is just sucking that up and then at some point it becomes an automation problem. So if you're putting a task in there which is essentially a head query so I'm using like information retrieval language here so you know like a head query is it's something that's you know in the mode of the distribution it's used all the time. It's a common task. Claude code will give you the specification because it's already been stolen from other people. Not stolen but you know taken from other people. And then if you give it like something on the long tail then you as the developer have to give it the specification in the prompt. And then again, it's an automation problem. So automation is really easy. So is that what's happening? Like do you think that the increase in in the timelines could just be explained by the acquisition of all of this kind of knowledge from other people doing similar tasks?…

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