Tim Scarfe: belief

Transcript context

…It will. Have confidence that that's not 1 of those things that I'm going to outright poo poo. Yes. I do have confidence that there's a lot of that, you know, that is a rich that is a new area. It's a, you know, a relatively new, and they haven't really, you know, there's a lot and there's, you know, there are a lot of there's a it has a lot of promise. That's what I'm gonna say. And to some extent, the approach that we're taking is compatible with program synthesis. Right? We're taking this object centered description of the world, and the reason we're doing that is because we wanna automate systems engineering. Well, what's systems engineering? Oh, that's like taking this object and attaching to this 1, to this 1, until you get something that does something really cool, right? Program synthesis, right, is an abstract way of doing that, right? Is that you start with 1 program, you attach it to another program, attach it to another program, and so on and so forth. There is this problem of just understanding the program. I mean, going back to DreamCoder and I'm sure Kevin and Josh have put other ones out more recently. Some of the programs which are learned are just really complicated. They had examples of like I think drawing towers and drawing graphs and stuff like that. And you just saw this you know huge confection of rules that are being composed together. And it's great it has many good properties that it's a program but it doesn't really make sense to us. Yeah. To a large extent, I suspect that there are ways around that that are related to how it is that like your AI coding agent actually works. So for example, right, when they when they're doing this program synthesis, what they don't currently have access to is the kind of dataset that GitHub has access to. They don't have access to a whole bunch of really well written programs that do exactly what they were intended to do. There was a paper in Nature. This was actually 1 of those situations where you know, neuroscience is making interesting statements about machine learning from Tony Zaidor. And what he had done is they'd taken a whole bunch of neural networks that did a variety of different things, and then they came up with a way of genetically encoding them for the purposes of seeing if like, okay, so what's this so it's like, oh, I had to have a layer that did this, and then a layer that did this, and then and then what I'm gonna do is I'm gonna like compactly represent the weights in each layer and come up with a representation of that. And I'm just gonna look at a whole bunch of different neural networks and solve a whole bunch of different problems and say, are there any, like, patterns that are present in these neural networks such that when I have a new problem I'm interested in, I can sort of just, you know, take something that understands this genetic code, maybe mutate it a little, as a way of sensibly you know traversing the space of possible neural networks until I find the best 1. Program synthesis could in principle exploit the same trick, they just need the dataset to do it. Yep. Yep. What are what are humans in a world where everything can be done by a robot? Yep.…

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