Dwarkesh Patel: uncertainty

Transcript context

…Astronomy was one of the first sciences to really embrace data analysis and squeezing every last possible drop of information out of the information they had because data was the bottleneck. It still is the bottleneck. It’s really hard to collect astronomical data. Astronomers are world-class in extracting all kinds of conclusions from little traces of data, almost like Sherlock. I hear that for a lot of quant hedge funds, their preferred hire is an astronomy PhD, actually. They are also very interested for other reasons in extracting signals from various random bits of data. We do under-explore how to extract extra information from various signals. Just to pick one random study, I remember reading once that people were trying to measure how often scientists actually read the papers that they cite. How do you measure this? You could try to survey different scientists, but they had a clever trick. Many citations have little typos, like a number is wrong or punctuation is almost wrong. They measured how often a typo got copied from one reference to the next, and they could infer whether an author was just copying and pasting a reference without actually checking it. From that, they were able to infer some measure of how much attention people were paying. So there are some clever tricks to extract… These questions you posed earlier of how we can assess whether a scientific development is fruitful, interesting, or represents real progress… Maybe there are really useful metrics or footprints of this phenomenon in data. We can examine citations and how often something is mentioned in a conference. Maybe there’s a lot of sociology of science research to be done that could actually detect these things. Maybe we should get some astronomers on the case, actually. That brings us nicely to the progress that, from the outside, it seems like AI for math is making. You had a post recently where you pointed out that over the last few months, AI programs have solved fifty out of the eleven hundred odd Erdős problems. I don’t know if it’s still correct, but as of a month ago you said that there had been a pause because the low-hanging fruit had been picked. First of all, I’m curious if that is still the case, that we have picked the low-hanging fruit and now we’re at this plateau currently. It does seem so. Fifty-odd problems have been solved with AI assistance, which is great, but there’s like six hundred to go. People are still chipping away at one or two of these right now. We’re seeing a lot fewer pure AI solutions now where the AI just one-shots the problem. There was a month where that happened and that has stopped, not for lack of trying. I know of three separate attempts to get frontier model AIs to just attack every single one of the problems simultaneously. They pick out some minor observations, or maybe they find that some problem was already solved in the literature, but there hasn’t been any further purely AI-powered solution yet. People are using AI a lot currently. Someone might use AI to generate a possible proof strategy, and then another person will use a separate AI tool to critique it, rewrite it, generate some numerical data for it, or do a literature survey. Some problems have been solved by an ongoing conversation between lots of humans and lots of AI tools. But it does seem like it was this one-off thing. Maybe one analogy for these problems is that you’re in some sort of mountain range with all kinds of cliffs and walls. Maybe there’s a little wall which is three feet high, and one that’s six feet high, and then there’s fifteen feet high, and then there are some mile-high cliffs. You’re trying to climb as many of these cliffs as possible, but it’s in the dark. We don’t know which ones are tall, which ones are short. So we try to light some candles and make some maps, and slowly we figure out some of them are climbable. Some of them we can identify a partial track in the wall that you can reach first. These AI tools, they’re like jumping machines that can jump two meters in the air, higher than any human. Sometimes they jump in the wrong direction, and sometimes they crash, but sometimes they can reach the tops of the lowest walls that we couldn’t reach before. We’ve just set them loose in this mountain range, hopping around. There was this exciting period where they could actually find all the low ones and reach them. Maybe the next time there’s a big advance in the models, they will try it again, and a few more will be breached. But it’s a different style of doing mathematics. Normally we would hill climb, make little markers, and try to identify partial things. These tools either succeed or they fail. They’ve been really bad at creating partial progress or identifying intermediate stages that you should focus on first. Going back to this previous discussion, we don’t have a way of evaluating partial progress the same way we can evaluate a one-shot success or failure of solving a problem.…

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