Michael I. Jordan: uncertainty

Transcript context

…Yeah, it's interesting because I agree that we live in this complex, adaptive, irreducible system. We can't essentialize it. And, folks like Francois Schollet or even David Krakauer, they talk about intelligence as the, you know, adaptation synthesis of coarse grained representations. But what if there is a bit of a step? So let's not anthropomorphize it. Let's say that understanding is about, like, not the endpoint, it's about the path which led us there. And we know that in the real world, we're a collective intelligence, and there's the blind men and the elephant, and we all take our own paths and lives, we we have different perspectives on the same hole. What if like a better form of understanding is just being able to reconstruct the thing from your perspective using building blocks rather than trying to essentialize it? You know, that that all sounds great. It's just not the language that those of us who do research would use. I mean, we would think in those terms a little bit, of course, but we would try to turn it into some kind of an equilibrium or optimization problem and here's the information that's available and here's the data and here's the power and the, you know, the the error rates. And we try to put a little bit of structure around it of that form. And and, you know, there's always this creative moment. Like, remember when in high jumping, I used to be high jump interested in high jumping when I was a kid. And, you know, you you would go up to the bar and you'd jump over it in various ways and there were the the barrel roll rolling across, you know, was the technique the Olympians were using. And then there's this guy, Dick Fosbury came along and he says, no, if I go backwards, I can do better. And no 1 had thought about doing that. As soon as he did it, everybody did it and that's and and, you know, it went up like by half a meter or something. I don't know. And so what process led to that? Was it an understanding process? It was just a little bit of let's try something different mixed in with the ability to try it out and to do tests. So a huge amount of industrial planning is try it out and see what works. Those are called AB tests. And those are done all of the time, and I've got nothing against that. It's not based on understanding, but it's led to optimized systems that can do things that, you know, people hadn't thought about before. So a blend of that with understanding. But just understanding, you know, I'm I I was a cognitive scientist and I, you know, I'm interested in neuroscience. I'm 1 should be interested in those things. They're fascinating. They aren't they aren't the leading edge of thinking how to build systems that, you know, work in the world. And they're not the leading edge of trying to believe in the next generation systems. If you put a lot of people keep saying, well, we got to put logic back in or symbols because that came from the that came from our previous kind of view of what humans are doing. And probably humans are capable of doing some logical reasoning and probably have some symbols, whether they're kind of built in some complicated network or they're reified somehow, I don't know. My intuition is as good as yours. But really, the goal is to I tend to be an engineer at heart, a mathematically inclined engineer. I wanna say, what are you trying to achieve? Are you trying to displace teachers? Are you trying to make doctors better? What are you trying to do? And what would be the abstractions and the points of entry into that problem? And then how can you pull back from that and do it in some general way that's elegant and will inspire others? It's so interesting seeing different scientists, you know, from a multidisciplinary perspective attack this problem. Physicists, for example, they they work very, low level and they talk about, you know, the the the dynamics of particle systems and and whatnot. And what I'm really fascinated in, I mean, you come at it from an economics perspective, which is traditionally dominated by this agential lens, and you talk about equilibria and incentives and and so on. How how does that come into it? How how would you take a very complex system and almost kind of decompose it into this new frame of thinking?…

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