An agent must be physical — a computer simulation of an agent, no matter how accurate, is not itself an agent, only a model of one.

11:07I would have to say it's an agent. >> Yeah. >> Right. If it's doing exactly the same, I mean, this is like the standard. It's doing exactly the same calculations from from a purely like phenomenological perspective, it's like it's the same. It's indistinguishable. >> Okay. So agents need to be physical. >> So I do believe that an agent needs to be physical. That absolutely. I don't believe, you know, I I believe you can

11:26have a model of agency and not have an agent, right? I, you know, you can put that model in a computer and run it and make predictions as to what an agent would do. You and it might even be 100% correct, but I still wouldn't call it an agent. But again, this is like getting into philosophy and like philosophy frustrates the basian because philosophy is not probabilistic,

11:46right? [laughter] philosophy is really about drawing clear lines and distinctions and in my world those don't really exist right there's everything has an error bar you know all of there isn't a clear delineation between you know uh you know an object and an agent it's really you know in from this modeling perspective it's really just a question of degrees and philosophy is terrible at handling questions of degree

12:13>> my friend Keith he he's a big fan of um computability and and he thinks that an agent is basically you know like a type of computation and it has access to ambient state and it can take action and there's this kind of like cybernetic loop and for him the strength of the agency in the system is the compute type that the thing is doing right so if it's if it's

12:37a finite state automter then it's a weak agent if it's a touring machine it's a strong agent >> yeah it's the degree of sophistication of the compute right >> pretty much does That ring true to you? >> I mean that if if you were going to make if you forced me like, you know, at the point of a gun to put a measure on agency, it'd probably look a lot like

12:54that. >> Yes. Jeeoff, let's talk about energy based models. >> Sure. >> So, um, Yan Lun, he had a monograph out, I think, in 2006 talking about this. Been talking about this for a long time. >> Oh, yeah. When you fit your neural network to data, you know, via gradient descent, right? then you have written an energy function in weight space and you are follow and you're following it to

13:16its energetic minimum. You know the the advantage of using an energy based uh taking an energy based approach as opposed to taking say a straight up like function approximation approach is that an energy based model comes with something that's kind of like an inductive prior right it it basically you know an energy based model you know if you're just doing function approximation you're basically saying there's any mapping from x to y x is my