The free energy principle made simpler but not too simple

Hello everyone. It is ActInfLab livestream number 45.0. It's May 27th, 2022, and we're discussing the paper, The Free Energy Principle Made Simpler But Not Too Simple. Welcome to the ActInfLab. We're a participatory online lab that is communicating, learning, and practicing applied active inference. You can find us at the links on this slide. This is a recorded and archived livestream, so please provide us with feedback so we can improve our work. All backgrounds and perspectives are welcome, and we'll be following video etiquette for livestreams. If you want to learn more about the livestreams or any of the other projects to get involved at ActInfLab, head over to activeinference.org. We're in stream number 45.0. Our goal is to learn and discuss this very interesting paper, The Free Energy Principle Made Simpler But Not Too Simple by Carl Friston, Lancelot da Costa, Noor Sejid, Connor Heinz, Kai Ultsofer, Gregorius Pavliotis, and Thomas Parr. And just like with all the .zero videos, and indeed all the videos, this is an introduction and an overview to a quite technical and lengthy-ish paper. It's not a review or a final word. This is like the opening context for some of the coming discussions we're going to have in the following weeks and beyond. And we're going to first just say hello, introduce the big question, the aims, claims, abstract, and roadmap. Then we're going to give an overview of some of the keywords that are in the paper. Then we're going to go through the sections of the paper with a focus on some of the key points, the formalisms, and the figures especially. So it should be a great discussion. And let's get into it. We'll start with just an introduction and saying hello. So I'm Daniel. I'm a researcher in California. And I'll pass to Brock. Brock, thanks for joining and for all the contributions in this .zero. Yeah. It's exciting to be here and participate in the ACDEMF lab. And yeah, I'm just really drawn to this topic. And this paper is a great starting point for that. It's really got a lot of detail to dig into and a lot to learn. So yeah. Okay. So, one of the big questions or one way to state the big question was what are the foundations of the free energy principle and what does it contribute? In the paper, they write that they start from a description of the world in terms of a random dynamical system, systems changing through time, and end up with a description of self-organization as sentient, sensing, active behavior, and that's active inference. So that's the question that we're wondering about. We're all wondering about what is the basis and the essence and the implications of the free energy principle. What would you say about that or what were some big questions that you had coming into and out of this paper? I think my biggest question around like the free energy principle is kind of it's general, how general is it? Where does it end? And because it seems so... They say in the paper also, it's like, it's pretty simple. It's kind of podological in some sense. So yeah, that's one of my questions. Where does it begin? Where does it end? And like, how general is it? The other question is, yeah, how does it emerge? Or what does it look like at different scales? Awesome. So we'll be returning to questions again and again. Let's check out the aims and the claims of the paper. So again, it's the free energy principle made simpler, but not too simple. And the authors are listed here. The paper describes that it's trying to present the free energy principle as simply as possible, but without sacrificing too much technical detail. And that's sort of a pun slash self-reference. That's what modeling in that Pareto optimal or Bayes optimal way is. And that's going to come back as a theme again and again, giving information, but not overfitting nor underfitting. And then several claims that they make are that they're going to step through the formal arguments that lead from the description of a world as…