Terence Tao: prediction

Transcript context

…There’s two different ways to think through what you’ve just said. One of them is more bearish on AI progress, and one of them is more bullish. The bearish one being, “Oh, they’re only getting to a certain height of wall, which is not as high as humans are reaching.” The second is that they have this powerful property that once they achieve a certain waterline, they can fill every single problem that is available at that waterline, which we simply can’t do with humans. We can’t make a million copies of you and give each of them a million dollars of inference compute and have you do a hundred years of subjective time research on a million different problems at the same time. But once AIs reach Terence Tao-level, they could do that. Once they reach intermediate levels, they could do the intermediate version of that. The same reason that we should be bearish now is the reason we should be especially bullish. Not even when they achieve superhuman intelligence, but just when they achieve human-level intelligence, because their human-level intelligence is qualitatively wider and more powerful than our human-level intelligence. I agree. They excel at breadth, and humans excel at depth, human experts at least. I think they’re very complementary. But our current way of doing math and science is focused on depth because that’s where human expertise is, because humans can’t do breadth. We have to redesign the way we do science to take full advantage of this breadth capability that we now have. We should have a lot more effort in creating very broad classes of problems to work on rather than one or two really deep, important problems. We should still have the deep, important problems, and humans should still be working on them. But now we have this other way of doing science. We can explore entirely new fields of science by first getting these broad, moderately competent AIs to map it out and make all the easy observations. And then identify certain islands of difficulty, which human experts can then come and work on. I see very much a future of very complementary science. Eventually, you would hope to get both breadth and depth and somehow get the best of both worlds. But we need practice with the breadth side. It’s too new. We don’t even have the paradigms to really take full advantage of it. But we will, and then science will be unrecognizable after that, I think. To this point about complementarity, programmers have noticed that they’re way more productive as a result of these AI tools. I don’t know if you as a mathematician feel the same way, but it does seem like one big difference between vibe coding and vibe researching is that with software, the whole point is to have some effect on the world through your work. If it leads to you better understanding a problem or coming up with some clean abstraction to embody in your code, that is instrumental to the end goal. Whereas with research, the reason we care about solving the Millennium Prize Problems is that presumably that in the process of solving them, we discover new mathematical objects or new techniques that advance our civilization’s understanding of mathematics. So the proof is instrumental to the intermediate work. I don’t know if you agree with that dichotomy or if that in any way will explain the relative uplift we’ll see in software versus research.…

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