Max Bennett: belief

Transcript context

…Yeah. I mean, couple of things on that. I mean, first of all, I would quite like to distinguish knowledge and intelligence. So collective intelligence and intelligence in general is is a process of discovering models. And when I just to sort of get the language down here, I will use models and skills and knowledge pretty much interchangeably. So I I think of, like, an intelligent process as epistemic foraging. So just finding interesting models, and then they can be discovered and and shared by by other people. So it's a little bit like when you distribute a GPU workload, you can do, like, model parallelism and you can do data parallelism. So you can either split up the the the CPU, like, you know, the processing, or you can split up the actual, representation. So I think the kind of collective intelligence that you've just been speaking about is, okay. We've got all of these independent agents and they are finding models and sharing models and, you know, the models get refined over time and and and it adapts and so on. But I also think a a big important element is sharing the the computation. So even though there's there's there's some, kind of redundant work going on, you know, epistemic areas over here are are being explored. But also in many cases, the same problems are being explored, but in slightly different variations. We're kind of sharing the workload with other humans. Yes. I think that totally makes sense. The there's some interesting ideas in AI here, actually, where there's this concept of knowledge distillation, in AI, where, 1 way in which you can have model a teach model b the things that model a knows. 1 way is you can wholesale copy the parameters of model a. Of course, that's totally biologically implausible. There are aspects of of parameter copying, which is the components of our brain that are genetically hard coded. That is a version of parameter copying. But for other applications, it's not feasible to just copy parameters or it's maybe not desirable. So knowledge distillation is saying, Okay, well, we can have a set of data that we give model A. And we either look at the outputs of model A or the layer before the outputs so we can see sort of more richness in its representation of of the input you give it. And then take those that data, that almost labeled data to model b and then train model b on it. So that's, distilling some of the knowledge through almost training model b to try and act similarly to model a. And so that type of information transfer, I think, does occur in nonhuman primates, and that's imitation learning. However, it is not nearly as, rich because what happens in non human primates is it's primarily grounded in just the actions that I'm taking, which is much less rich than I can share not only the the data of what you see me actually do, but I can also include in the data I'm transferring to you things that happen only in my mind. And that that opens the door for much more transference of of, and the word you use, the computations that I'm performing. Yeah. So I think it's absolutely absolutely true. Before, we were, learning in the physical world. So we were learning from physical things that we were directly observing, and now we are learning from imagined actions. But there's a bit of a latent component to language as well. So for example, someone might come up to me and say, blue swirly thing is over there. And I'll say, well, I don't know what you mean about the blue swirly thing because I've I've never seen 1 before. So there's this kind of, inference process. And this is where it starts to get really interesting because there's a diffusion. Right? There's a kind of, there's a message passing that happens between all of the different agents, and it's filling in missing information. So even though many of the agents wouldn't have seen anything like what we're talking about, sometimes it can be filled in with subsequent interactions with people, and sometimes it can just become a kind of latent category which can be filled in later. So there's this real diffusion process going on, which I think is quite difficult to articulate.…

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