Jeff Beck: belief

Transcript context

…That is a so so I I feel like that's a trick question. I was I actually had this conversation with with with 1 of my friends Max and coconspirators the other day. In some sense, every model is grounded. It's grounded in the data that it was given. Now okay, so that's like a true statement. It's like, yeah, okay, yeah, yeah. But that's not what we want and when we often use the word like a grounded world model, it's we say that it's grounded in something. And that something is not just the data that it saw. So example, vision language models. A vision language model is like is a way of grounding the visual model in the linguistic space. And this is the approach that we're taking. This is what LangChain does. Right? It's all about taking models and everything becomes a blank language model. Right? Vision, whatever. Everything becomes when you do that what you're doing is you're saying that you're grounding all of your models in like a common linguistic space so that they can communicate with 1 another, right, via language. Now why did we choose language? Well we chose language because like honestly I think it's because we wanted models that we could talk to. Right? We wanted a model that like you know it was really all about the making the interface convenient for us, which is great. That's totally something you want. But it begs the question, what's the right domain in which to ground your models? Now, I like grounding models. So we also use the phrase like, you know, 1 of those, ground truth. And of course, ground truth is the thing you made up and said was ground truth. What's ground truth? What the right domain in which to ground models in order to get them to think like we do? That's the relevant question. And so my view is that if you know, again, you want AI that thinks like we do, you need to have it grounded in the same domain in which we are ground. And we are grounded in this domain, right? This is why the embodied bit is such an important thing. We want models that that are are grounded in the physical world in which we evolved. And the reason for this is because that is the world that provides us with these atomic elements of thought. A single cell like lives in a in a soup, right, and it has, you know, and it it it it's, you know, whatever model it has of the world to the extent that it has 1 or it behaves as if it has 1, that model is the model of its environment, right? If it didn't understand the environment in which it lived to some extent, right, then it wouldn't be able to continue to exist and function in that environment. So you can sort of say that a cell has a model that's grounded in chemistry, right, of the chemistry of the soup in which it lives. You know, when we talk about like that is a prerequisite for its survival. Now we talk about like mammals and bigger animals and things that live in the macroscopic world that includes other animals, right? And you know, and all that stuff. So what's that model? ts survival. Now we talk about like mammals and bigger animals and things that live in the macroscopic world that includes other animals, right? And you know, and all that stuff. So what's that model? What's the world the, you know, well at the very least we can say that whatever models we have a significant subset of them are grounded in that world. Right? And that world we know has properties that that we can understand it is object centered, it's relational, it's all this you know, all this stuff. And so the ground the the the the the the grounded bit is more about like properly grounded. Grounded in the domain in which in which we are grounded as a route to to creating, you know, AI, you know, an AI models that in fact think like we think. Right? That's the grounding that we that that that we're particularly focused on. If you had to choose the domain in which to ground your models, what would you choose? Right? I don't think language is the right 1. Language is an incredibly poor description of both our thought processes and reality. I tell this story all the time, right? So you ever you ask any cognitive scientist or psychologist who's done some experimental work with humans, right? You put them in a chair, you make them do some tasks, you carefully monitor their behavior, you look at what they did, right? And then you have a nice way of, and then you, you know, that informs your theory of that behavior or however that works. Then you, and you know, and if you do the experiment well, you have a very good model of how they made whatever decisions they made throughout the course experiment. And then you go back and you ask them, what did why did you do what you did? And they give you an explanation. It sounds totally reasonable. It also is completely inconsistent with an accurate model of their behavior. Self report is the least reliable form of data, right, that 1 gets out of a cognitive or psychological experiment. And so we don't wanna rely on that. We don't wanna ground our models in what we know is an unreliable representation both of the world and of our thought processes. Right? We wanna ground it in something that's a good model of our world. And that's why we we've we've chosen to focus on like mac you know, models that are grounded in the domain of macroscopic physics as opposed to language. Can you speak a little bit more to the the limitations with current active inference? A nearly uniformly applicable information theoretic for describing objects and agents. Right? It's it it really is inspired by statistical physics and its links to information theory. And when you take those 2 mathematical structures, throw in a little, like, Markov blankety things so you can talk about macroscopic objects, you kind of have a very generic, widely applicable mathematical framework that you can throw up many problems. And a lot of what has gone on in the active members community over much of the last 20 years has been demonstrating that it's it's it's like uniformly applicable. So there's been a lot of breadth breadth and not a lot of depth. Right? he active members community over much of the last 20 years has been demonstrating that it's it's it's like uniformly applicable. So there's been a lot of breadth breadth and not a lot of depth. Right? And part of, you know, and I and I think that, you know, of course like that's, you know, that that's appropriate. Like given, you know, if you really wanna make the the argument that everyone should be using this, you shall see in this in this domain it works on your like toy examples. But the people doing that, right, it's kind of, you know, the active awareness community has had it has this habit of showing like like, see like, this basically, like, I I can handle this, like, psychological phenomenon. I can model this cognitive phenomenon. Oh, and look, like, it's a good post hoc description of this neural network's behavior and things like that. Right? They're they've been showing that but they've they've never really sat down and and and and, like, tried to tackle any really big, really hard problem because the emphasis has been on evangelism. You couple that with the fact that there is this strong bias within the active members community towards being as Bayesian as possible. And so of course, they also like shun the really hard problems because Bayesian inference, you know, is has been historically challenging to scale. There have been a lot of developments over the last few years that have come out of the machine learning community as well, but mostly out the Bayesian machine learning community that have really made it possible to start scaling Bayesian inference in ways that we really weren't able to do it before. And you couple that with a desire to sort of stop the evangelizing and start solving really hard problems with these methods, and you've got a way to prove that active inference really can live up to its promises.…

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