Terence Tao: prediction

Transcript context

…To ask the question about the analogy more explicitly, does this analogy make sense if in the future we have smarter and smarter AIs? We’ll have millions of them, and they can go out and hunt for all these empirical irregularities. It sounds like you don’t think the bottleneck in science is finding more things that are the equivalent of the third law of planetary motion for each given field, so that later on somebody can say, “Oh, we need a way to explain this. Let’s work out the math. Here’s the inverse-square law of gravity.” I think AI has driven the cost of idea generation down to almost zero, in a very similar way to how the internet drove the cost of communication down to almost zero. It’s an amazing thing, but it doesn’t create abundance by itself. Now the bottleneck is different. We’re now in a situation where suddenly people can generate thousands of theories for a given scientific problem. Now we have to verify them, evaluate them. This is something which we have to change our structures of science to actually sort this out. Traditionally, we build walls. In the past, before we had AI slop, we had amateur scientists have their own theories of the universe, many of which were of very little value. We built these peer review publication systems to filter out and try to isolate the high signal ideas to test. But now that we can generate these possible explanations at massive scale, and some of them are good and a lot are terrible, human reviewers are already being overwhelmed. Many journals are reporting that AI-generated submissions are just flooding their submissions. It’s great that we can generate all kinds of things now with AI, but it means that the rest of the aspects of science have to catch up: verification, validation, and assessing what ideas actually move the subject forward and which ones are dead ends or red herrings. That’s not something we know how to do at scale. For each individual paper, we can have a debate among scientists and get to a consensus in a few years. But when we’re generating a thousand of these every day, this doesn’t work. There’s this incredibly interesting question. If you have billions of AI scientists, not only how do you gauge which ones are real progress, but how do you... This is actually a question that human science has had to face and we’ve solved somehow, and I’m actually not sure how we solved this. Let’s say in the 1940s, if you’re at Bell Labs and there are these new technologies coming out. Pulse-code modulation, how do you transfer signals? How do you digitize signals? How do you transfer them over analog wires? There are all these papers about the engineering constraints and the details, and then there’s one which comes up with the idea of the bit, which has implications across many different fields. You need some system which can then look at that and say, “Okay, we need to apply this to probability. We need to apply this to computer science,” et cetera. In the future, the AIs are coming up with the next version of this unifying concept. How would you identify it among millions of papers that might actually constitute progress, but which have much less in terms of general unifying ideas?…

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