Modeling and Predicting Self-Organization in Dynamic Systems out of Thermodynamic Equilibrium

Hello, welcome. It is June 11th, 2025. It is Active Inference Guest Stream, lucky number 111.1. Very nice. And we are with Georgi Georgiev, who will be presenting and then we'll have a discussion on modeling and predicting self-organization in dynamic systems out of thermodynamic equilibrium, part one, attractor, mechanism, and power loss scaling. We will look forward to a presentation and some Q&A. So if you're watching live, feel free to ask a question. Thank you for joining, Georgi. All right. Thank you, Daniel, for inviting me. It's my great pleasure to talk to Active Inference Institute audience. And hopefully we have a lot of interests in common so we could have a good discussion or continue to explore those questions. I personally know some of the people in your group, so I hope we'll find a lot of commonalities. Yes, as you said, I'll be talking about some of our recent work on computer simulations with modeling and predicting self-organization what is the basic mechanism that leads to increase of order in those systems and how the different characteristics relate during the process of the self-organization, how they grow together. And I'll show a lot of examples from other areas to connect to, like, why is this important? Why is it interesting? And on this title slide, I put the big picture, the arrow of time as presented. This was a poster made by Eric Chaison when he was at Tufts University at the Wright Center of Science Education. And we were, like, next, his office was next door to mine. So we had a lot of great conversations. And we found that we're interested in the same questions. And actually, I had the exact same, like, chronical view of the expanding future. And the arrow of time as leading to more interesting things, more organized systems, more intelligent systems. And needless to say, I was very pleasantly surprised when he made this poster. So this represents the arrow of time from the Big Bang pretty much capturing everything from the pure energy to formation of structure in particles, in formation of atoms, of galaxies, of stars, planets, and planets, chemistry, biology, cultural evolution, and whatever comes in the future. And the big... In many people's minds, it has been ingrained that the second law of thermodynamics is predicting that everything is going to a state of thermodynamic equilibrium to entropy. And that's why a lot of people are very pessimistic about the heat death of the universe. In many people's minds, it's going to be the same. And that's why a lot of people are very pessimistic about the heat of the universe. And that's why this is the same. And that's why the systems are becoming less and less of equilibrium. They're moving further away from equilibrium. And the interesting question is, why does this happen? Why they don't just disintegrate? Why they don't fall apart? Why does this process of increasing complexity and structure and everything that we see around us has not stopped, let's say, a random number of billion years ago, if it ever started? And why do we observe everything around us right now? How did it come to be? Well, I guess that's a question that a lot of people are thinking from different angles and have different answers. answers. And, um, um, I want to point also to another aspect, not only that the process of self-organization in the universe in all kinds of different systems is moving in an hour of time of increasing structure, increasing complexity further away from equilibrium, but it happens at an accelerated rate. It has been happening since the big bank and it doesn't seem to be slowing down. If anything, uh, I know that some of your audience have told me from machine learning and AI, boy, that's an acceleration every day, there are new things. So, uh, and that's a great example of self-organization when you put tons of information in a neural network and it finds a pattern and you don't know what happened. It happened by itself, I guess, as far as I…