Tim Scarfe: belief

Transcript context

…I don't think we need to. See, I think this anthropomorphizing of intelligence understanding all that is not necessary, not appropriate, and is is a distraction for many many problems. Why say it understands? You know, some of my heritage comes from seeing in in real life in in industrial settings, machine learning algorithms being rolled out 20, 30 years ago. So I when I first went to the West Coast, I visited Amazon and around 2,000, they were using huge amounts of data to do supply chain modeling using the neural networks of the day. It was random forests. And it was really working. They could make really fantastic predictions of whether certain ships would be delayed in the Indian Ocean or whatever and so certain parts wouldn't arrive in time. And the overall supply chain takes billions of products and sends it to 100,000,000 people per day. And so there's no way that any human can understand what's happening in that big box. But it's not necessary. And in fact, you can ask, does that overall system understand transport and logistics? And the answer is, who cares? It does a very important optimization and prediction process that allows an engineering system to be built around it. It brings down uncertainty. It makes you possible to do kind of stockpiling and planning and that's what you ask for. You don't care whether it has to have a word like understanding or intelligence applied to it. That's for the media. That's kind of my problem with a lot of these people rolling out AGI and AI terminology. The media laps it up and they know that Even though we don't have a clue what understanding intelligence means, we and our researchers realize we don't care or need it. We want to build good systems. 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?…

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