Jeff Beck: evaluation

Transcript context

…be without becoming too brittle. You're talking to this interaction dynamic. So there's a graph of interactions which might possibly represent affordances in the macroscopic domain. And by doing analysis on the interaction graph and sort of simplifying the analysis as much as possible, you get a principled way to partition the world up. That's right. And so so there's there there it's it's it's it's all about having interactions and interaction classes. So it's not like there's not just 1 adjacency matrix. Right? There's an adjacency matrix that also specify there's there's 1 for every type of interaction that that that's possible. That's what gives you the additional flexibility. The other thing that gives you the additional flexibility is being is being a little bit Bayesian about things. Right? It may very well have been that all of your observations of this object, you know, when it was in a house, were like really simple. It was all just, it sits on a shelf, right? And so what do you know? Well what you know is that that object sits on a shelf. But you have to be, you know, which is 1 kind of interaction, right? That's just the, you know, it has a force pushing down, there's a force pushing up. You don't know anything about like the weight, but if you keep error bars about that, if you keep error bars about the other kinds of interactions that you have seen, but are agnostic, right, about the specific deals for this particular object, it gives you the flexibility to say, I'm gonna put it this environment. I can make some predictions about how it's gonna behave, right? But if I throw a bowling ball at it, I'm gonna be making some, you know, assumptions about how it might behave. But I but once the bowling ball hits it, right, I might have to revise those assumptions. This is the other critical element of the approach we're taking, is continue which is you have to have some kind of continual learning element. This is something that really doesn't exist in contemporary AI. Right? And and, know, you know, when you build your big model, you've spent millions of dollars training it, and then you're done. Right? Yes. Someone else can come along and fine tune it a bit for a particular task. Right, which is great. But at the end of the day, when you're at the deployment phase, you turn learning off. Whereas in this approach, we're saying, no. No. No. You've 1 of the things that's critical a critical aspect of the way we think about the world and the way we learn about the world is is that it's continual and it's interactivist. Right? So there's you know, and that needs to be true of the objects that we're discovering as well. We've learned classes of interactions, but just because we haven't seen a particular class of interactions previously doesn't mean we say the others never happen. Right? We still allow for that possibility. Right? And then do continual learning quick with with rapid updates when we see something happen. We we see, you know, a new interaction. Now the what makes that work, right, is the fact that you specify that there are certain set of kinds of interactions, some of which you previous or some of which you still don't know about and might observe soon, and then you could update your posterior beliefs about whether or not that object interacts in that way. What would the architecture of such a system look like? I'm imagining it'll be distributed, right? You'd have all these different agents. And then we have the consistency problem because maybe this agent has empirically learned that these 2 things are a book, but the agent over there just thinks this 1 thing is a book. And then there's how many objects are there? Would it become intractable? Realistically, I don't know what you're…

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