Textbook Group meeting for Parr, Pezzulo, Friston 2022 .

Welcome. It's Cohort 1, Meeting 17 already, and it's September 23rd, 2022. We're having our first discussion of Chapter 7 in the textbook. Well, we have many ways to go. We have questions on Chapter 7 that people have been adding. We also have some summaries and overviews and can walk through the text directly. But first, does anyone want to just raise their hand or unmute and give any thought that they had on Chapter 7, their experience reading it, their understanding of where it's situated in the textbook overall, what was in it or what was not in it, etc. Okay. Well... I'll open... Again, please raise your hand or write in the chat if you want to address anything at any point. I want to open by acknowledging a lot of the contributions Ali made and in some conversations that he and I had earlier this week that I believe all of you Cohort 1ers will find interesting. But first, let's just start with this quotation. We're going to start with the opening quotation of the chapter. Then I'm going to surface some discussions with Ali. And then we're going to go into some details of the chapter. So the quote is, What I cannot create, I do not understand by RF. So what does anyone think about that quotation or what does it mean in this context? I think... I mean, it's more applicable... Applicable, the more complex the thing is that you're trying to build. But how could your generative... What priors or afford... What way could your generative model create an accurate prediction of the operation of something or the underlying dynamics of something if you do not kind of step by step generate the affordance... Take the actions that generate those affordances? I don't see any causal path to doing that. So without... Like you would have to step through it necessarily if it's computationally complex. So... That's most things in the world. But... Yeah. Awesome. It's like this is like the low road answer. Like how can the generative model have anything like understanding without generating? You can't just have it on the shelf. And then someone has added a mild answer. We can't have understanding. Just through mental envisioning. That the algorithms need to be implemented for a learner's journey. And then this is also an even deeper or stronger point. Which... Fristin and others have been working on for a long time. Which is like it's sentient artifacts in the world. That will be the realization of active inference. It's not just like some nice derivations. Brock? And then anyone else? I was just going to add something about hidden states there. I mean it's again coming back to computational complexity. But there's things now that we're starting to build that we don't really 100% understand all of the dynamics of what is... Or just in general. Like we invent things that we don't completely understand first. And then based on the observational... Based on the evidence that we observe. That it is consistently exhibiting some behavior that directs our attention. That directs where our generative model pursues more observations. But we're definitely getting towards a point where that's like... Kind of no longer going to be possible. In the way that we usually try to use math to just shortcut stuff. Where the operation or the dynamics of the things that we're going to build are necessarily going to be... They will be the proof of... Their existence will be their own proof. Awesome. Okay. So just fun. You know. Starting quotation. I'll vote that one for sure. There's some detailed things about... The examples. And I also... And some of these I've been working on. Everyone is welcome for every chapter to be contributing on these pages. Like kind of just trying to overview what these examples are. Because a chapter... It states it up front. Yet I missed it the first several times reading it. Like these illustrated models of... Every Color in the Rainbow. Are the section titles. And those are the functionalities that are getting layered in.…