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before we're willing to to be bold about like precise visions, precise ideas. And instead, what we should be doing is laying out the groundwork so that we have all of the ingredients available to us to construct beautiful visions that we don't get wrapped up into. So I would maybe make an analogy with, yeah, kind of like Buddha lands or pure lands. Places where you go to learn. Ultimately you go to awaken. But in a sense, to play at that level, you have to have gone through many stages. And it's a very high level to play at because you need to, in a sense, not be prey to your previous aesthetics. And that is so difficult. I don't know the percentage of people who are there, but I would imagine it's very, very low. It's kind of a Keegan level five type of approach to reality, for those of you who are familiar with Keegan's levels of development. And there's so few people theorizing at that level. And I think we really need it. I mean, there's a lot of people kind of selling solutions or just raising up their hands and saying they don't know. But not kind of these nuanced analyses with all of these perspectives taken together as different ways of seeing. But we can make a lot of progress, I think, on that level. And I'll just share a couple things. I've made videos about this, but it's worth reminding you. Consciousness versus replicators is an important frame here. There's no kind of like the good and the bad guys. That's kind of like a bad low-dimensional framework that, again, from Camp Valio, you will have to recognize that as soon as you define the good and the bad, it activates some pretty primal emotions in you which distort your thinking and allow you to make coalitions based on that. But what is the point of making coalitions based on something that's not true or something that is just very partial, a very low-dimensional approximation underfeeding reality? That's not good. That's not good. And I think part of the aesthetic of the meta-aesthetic is recognizing the ways in which aesthetics give rise to various kinds of fanaticism, various kinds of closed-mindedness, being very aware of them and cognizant and feeling it in your body and really, really understanding it. Instead, something like consciousness versus replicators, where ultimately we're all on the same team, we're all on the same side. Whether we realize it or not, we are in team consciousness. There's kind of gradients to how much we realize it, but we can play as a one team, actually, to the extent that we realize that. And we can identify, in a sense, the dangers as coming from replicators out of control, from viruses all the way to ideologies, all the way to perhaps robots that we might create or algorithms that might be really good at making copies of themselves. And

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text:
from that point of view, there's no identification. There's no commitment to a particular view of your identity that ought to be preserved. And in fact, the whole feeling, the whole desire for something that ought to be preserved is then reinterpreted as kind of a replicator strategy. Perhaps an important ingredient in anything that will pragmatically able to replicate itself, but very, very dangerous when unchecked, when not understood as what it is. Perhaps when wrapped up within the shadow of one of the visions, not only this camps, but whatever memeplexes there are in society. So by disidentifying with any particular pattern and recognizing that this is a kind of impermanent flay of emptiness using buddhist metaphors, that clinging to it only makes it worse, it allows you to see reality more objectively and assess the pros and cons of the various states of consciousness that could be the building blocks, could be the inhabitants of the space-time of awareness of the utopia. So flexible sense of self. And flexible sense of self, yes, I mean there's this fascinating thing where like, you know, in some aesthetics, for example, a monotheistic aesthetic, there is this thing that you pray to which is kind of this external creator god, for example. But then there's also, you know, maybe kind of a transcendent, gnostic views of divinity where maybe the thing you pray to is kind of a unity consciousness or something like that. And then there's maybe like within buddhism and other religions like that, there's like, okay, maybe not even that, you know, this kind of emptiness or the state of neither being nor non-being or nirvana or something like that, that is like very, very transcendent but ultimately not agentic. And then there's like things like, you know, like samadhi or like states of super, super elevated energy that don't have a self, you know, they don't have a personality. So, I mean, I think we can make the case that they're important building blocks of paradise. And like, okay, maybe paradise is not like tiling the universe with, you know, little monks in samadhi or something like that. But maybe samadhi is like pointing towards, ultimately, like what is this value that physics can produce? The one electron of the universe and all of its tangles, you know, what states does it prefer? And it's not a personal thing, you know, this is the craziest thing because like once you're kind of at this level of analysis where you're able to disentangle yourself from standard identity, it's kind of like you recognize that, you know, even kind of like the typical way in which somebody might have a relationship with the divine has built up assumptions about the self, has built up assumptions about reality. But if reality is like just so much more expansive, so much deeper and wider than any of that, then that feels all of a sudden kind of like a strange

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play, kind of an aesthetic that is very childish in a sense, almost, almost childish, I would say. Because, you know, if the sense of self is a possible variable, you have to ask yourself, you know, if we are doing this for the universe, but then in the future there's no sense of self in the future, for example, that everything is like a post-samadhi society, there's no sense of self in the future, who are we doing this for? There's no recipient, there's no imagined sense of self getting this as a benefit. So all of our programs, implicit assumptions, of how, for example, favors are transferred, or goodness, or karma is transferred, and so on, a lot of those are fabrications that rely on very deep in-built background assumptions about reality. And if you start questioning them, you realize that the visions of paradise you could have could be free of that. You could recognize them as fabrications and go deeper, explore a wider space. And it goes so deep, it gets so crazy once you expand, and vivify, and energize these high-level visions that integrate, you know, all of the different camps that I've been talking about, while also remaining open to the mystery of reality. And I think that is a good way forward. I think that that's a good space, that's a great place to inhabit, and it's very subtle. And right now it's a seed that we are protecting, and we are trying to give life to, and nurture, and hopefully, you know, it can be something that ultimately, yeah, sprouts something very beautiful and blossoms. But for the time being, yeah, I wouldn't want to say it's any kind of complete vision, you know. We assume basics, like, you know, part of the negative view, the negative vision of reality, of the future would be, you know, utopia clearly doesn't have intense suffering, that, you know, that's a constraint. I can't imagine, really, a utopia with intense suffering, or factory farming, or things like that. They're kind of negative constraints, but that's not a vision of the positive side. Whereas, I think, playing with a very open mind, with all of these concepts, you kind of a high entropy alloy of consciousness, where you're open to, for example, how samadhi comes into the picture. That, there's so much juicy qualia, so much beauty of a very high level philosophical kind is so difficult to explain. Yes, so that's what excites me, what gets me up in the morning, and I definitely, yeah, want to share share that with all of you. So, thank you very much for listening, and yeah, join me in imagining possible advanced visions of paradise. Right, infinitely please everybody, and may you have a wonderful, wonderful day, evening, whatever time it is. Take care. Hello everybody. So today we're going to talk about the free energy principle and how it relates to psychedelics, which is a very, very

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text:
exciting topic. There's a lot of excitement, I think, in the academic community about this topic. I've got to say, I'm going to present three completely new ideas. Well, two of them have been kind of like talked about in various QRI articles as well as videos. There's one completely new one, but in either case, these ideas, you know, like they're not being talked about in the academic literature. We intend to publish them and run simulations. But in the meantime, it's just like very exciting, kind of like these new perspectives on how psychedelics and the free energy principle relate to each other. And as, you know, kind of the North Star of this conversation, we will have the basically the goal of explaining Indra's net in terms of the free energy principle, which I think it's actually possible. So so let's get to it. So first, the quality of the day is Indra's net. So, you know, it is said in various Eastern philosophies that, you know, Indra, this very powerful god, basically has this net hanging on kind of like the ceiling and, you know, one of the palaces in the realms of the gods. And, you know, each of the nodes of the net is a multidimensional gem, this beautiful, beautiful gem. And the net is infinite. It's completely infinite. And what happens is that each of those gems will contain a reflection, a tiny reflection of every other gem. And then within the reflection of every other gem, you will find an even tinier reflection of every other gem and so on at infinitum. You know, I mean, of course, if this was physically realized, it would be a limit, you know, because, you know, photons are quantized ultimately. Right. So there's not going to be like infinite resolution when you observe it. And you try to find kind of the world in a grain of sand, so to speak. But but, yeah, it's a very interesting and powerful metaphor. And, you know, it's used methodologically to kind of like talk about the interdependence of everything on everything else, kind of in Buddhism and even Hinduism. And that's a powerful kind of like vision. And like like imagine, you know, if you assume that that's the case, that, you know, like everything in the world actually stands in kind of an intra, internet relationship to everything else. Then everything implies each other, like even like a piece of trash or like a piece of, I don't know, this foam. I don't know. It was a random thing that came in in a package of essential oils like this is interdependent with absolutely everything else. And more so, you know, this is the final product of its backwards light cone, which, you know, contains such a huge amount of, you know, everything like the cosmic background radiation. And well, everything is interpret interdependent with everything else. Now, that's kind of a mind blowing idea. But

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what's the what's the use of it? And, you know, recently, there's kind of this fascinating Dharma teacher, Robert Bourbier. I've been consuming a lot of his lectures. He has this book called Seeing That Frees. And one of the things that he talks about is how, in a sense, you can interpret insight practice as opposed to, for example, like concentration practice or morality practice, you know, just insight practicing in Buddhist meditation as basically ways of seeing the world that reduce suffering, that diminish the dukkha, the unpleasant qualities of experience. So, for example, like being hyper, hyper aware or like being in the moment or perceiving things as like empty of inherent existence. When you apply those ways of seeing to even like, you know, unpleasant low level body sensations for a number of people in a number of circumstances, it can have a great reduction in their suffering. Now, it doesn't always work. And it's really interesting to see like why does it work and under what circumstances does it work? But that's kind of like as a general frame. I really like it. You know, it's very, very pragmatic. It's kind of like, oh, these deep, quote unquote, esoteric views of the world, you know, interdependence and, you know, dependent origination and emptiness and all of that stuff. Ultimately, you know, they're not necessarily useful because they're fundamentally true. Maybe, you know, as Buddha might say, like it's unknowable ultimately, but internalizing those ways of seeing the world and your experience lessens the suffering, allows you to kind of like drop a lot of baggage that carries around and causes dissonances internally and kind of like cleans your experience. And I think a powerful experience of Indra's web, well, it actually depends on how you interpret them, because there's also a kind of a spiritual materialism risk of it. Like if you experience like a mind blowing like meditation or LSD state of, you know, everything reflecting everything else. And you think like, oh, my gosh, I'm enlightened. And like, I'm one with everything. And like, kind of like now maybe you have even more baggage because you kind of have like this self conception that arose out of that. But there's also, you know, kind of the flip side, which is like maybe actually understanding the interdependence of everything with everything else allows you to lessen the amount of fabrications that you essentially engage with, especially mindless fabrications. Because in a sense, you recognize their empty essence and their co-arising with everything else. And in a sense, you can treat them by modifying the conditions that give rise to them. Anyway, so Indra's web, not only is it a mind blowing experience, it's also potentially like very pragmatically useful in that it might lessen your suffering to some extent. Now, importantly, too, I think it's very valuable to study as an experience itself. And, you know, like different experiences have like different degrees of

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text:
like how much they are like Indra's net. Now, because, you know, at QRI, we essentially assume indirect realism about perception. Basically, yeah, when you're having like an experience of Indra's web, I don't think you're literally, you know, contacting the rest of the cosmos and seeing how everything reflects everything else. I think what's going on is you're instantiating a state of consciousness that essentially has this fascinating property that every part of that state of consciousness in some tricky but non-trivial and very real sense is actually reflecting every other part of the experience. And that is what I will explain with a free energy principle. So exciting, isn't it? I should also remark that because of the symmetry theory of valence, I mean, most of the Indra's net kind of experiences tend to be pretty fractal and like highly detailed, but above all, very consonant and very symmetrical. So they tend to be extremely blissful. So, again, a lot of people may experience Indra's net and kind of like be fascinated by it, but not recognizing where the valence comes from. You know, they may think that the valence comes from literally becoming embedded into the fabric of reality. But, you know, that's kind of a tyranny of the intentional object. It's kind of like, in a sense, it's like reading too much into the intentional content of the experience, I would say. Rather, what I would kind of advise you to do is to pay attention to the way in which those patterns are either consonant or dissonant with one another. And the claim is that, you know, blissful Indra's net experiences, for the most part, will basically be hyper harmonious. There's going to be hyper harmony. They're going to be minimizing dissonance in a very, very deep way. So that's the quality of the day. Highly recommended. You can get it with meditation. It's very common on DMT, as we'll talk about. And it may be freeing in some deep sense that I think is very valuable. OK, so now let's get on to the topic of the day, which is the free energy principle more concretely. And there's a couple of things to say, you know, I mean, first of all, like Carl Friston's free energy principle and predictive coding as a paradigm, which are slightly different things. And the free energy principle can derive the predictive coding, maybe not the other way around, but they're very interrelated. And they're part of the lineages of QRI. So basically, we have now 10 lineages that basically we draw a lot of inspiration and theoretical frameworks from lenses that we use to understand the world. And we also have our own lenses, for example, neural annealing and the symmetry of valence and a bunch of other stuff like the tyranny of the intentional object and so on, which are ours. I mean, we've been developing them for the last, I don't know, seven years or so.

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text:
It's been a big effort. And hopefully we will actually be able to present all of that in a very formal fashion, all of those developments with their predictions and testing those predictions and so on. But yeah, I mean, QRI doesn't exist in a vacuum. It very much exists in the context that these other lineages generate. And a very important lineage, they're all very important. So one of those lineages is a free energy principle. So it's very important for QRI. I would also mention that, you know, Quintin Frerichs, the lead engineer at QRI, actually presented about neural annealing to Carl Friston and his lab. And, you know, we got a lot of really interesting feedback, mostly extremely positive feedback, very encouraging. And basically they were suggesting that we publish a particular version of what we were working on. And so that is, you know, in the works. And I'm very excited about that. OK, so let's get into the actual content of it. OK, so the free energy principle is one of those kind of like really high level, cool ideas that in a sense, as you munch on it, you start to see it everywhere. And I would compare it with like a couple other kind of things in that kind of category, or like very high level interpretations of reality that make you reinterpret everything, kind of like gives you a completely new lens on reality. One of them, for example, is evolution. You know, this idea that whenever you have reproduction with selection and variation, you will have basically the ingredients for evolution. Now, that is a fascinating, you know, clever idea, kind of like synthesizing evolution to just those three principles. And when you have those three principles, you will start seeing evolution everywhere, you know, not only biological evolution, but like all over the place, you know, in Conway's Game of Life, in fundamental particles. It will show up everywhere. But you can maybe even distill it further. You know, you can say like, well, that's maybe a special case of something more deep. So what could be that thing that is kind of like even deeper? Well, maybe it is the survival of the stable. And the survival of the stable is kind of this meta principle, right? That like from it, you can essentially derive, you know, the three principles of evolution. And it's kind of like a deeper, deeper truth, you know, that like, yes, sure, there's like biological organisms and all that. But, you know, if a rock is really stable, you know, it will be here, it will be around, like if it has like mechanisms to continue to exist. You know, that's what exists. What exists is what is good at being stable in some way. You know, something could be very variable, it could be a dynamic system, but maybe construed as the state space of the entire system. That is a very stable

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text:
construct, you know, and I would say that, yeah, biological organisms are like that. They're very dynamic, but in some sense, they do carry some stable kind of a metadata. And that is what is actually, you know, is immortal. You know, our genes are immortal over the generations, even if the individual isn't. You know, and in that sense, survival of the stable is kind of a meta principle. I would also say, you know, you know, Bayesian epistemology is like maybe one of these like pretty, pretty high level concepts that allows you to reinterpret so many things. That like, for example, like once you understand that, hey, the brain is receiving information from its environment in a time series fashion. I mean, there's massive parallelism, but there's also time, you know, we are embedded in time. So the information that we get is, that happens over time. It's time series. And whenever you have kind of like that data structure and you're trying to minimize, you know, prediction errors, it follows from math that you're going to be doing Bayesian inference. And there's no way around it. I mean, that's just a hard constraint. And it doesn't matter if the brain is made of like neurons or, you know, it's made of like honey and bees, or, you know, if it's made of the population of China or whatnot, like if it's making accurate predictions about time series data structures, it is going to be, you know, making Bayesian inference. You know, that's just a super hard constraint. Now, Bayesian inference is something that lives at the computational level. You still need to dig deeper into the algorithmic and that implementation level, as I've talked about in other videos, go to the digital sentience video for a deep discussion about this. But yeah, in a sense, computational level doesn't really tell you the full story, but it is a constraint. You know, it's kind of a boundary condition upon which everything else has to be fit. And for sure, you know, nervous systems do Bayesian inference, period. You know, that's a hard constraint. They have to be. Otherwise, they wouldn't actually be able to make accurate predictions. Now, the free energy principle, some could even think kind of brings together all of these high level paradigms, maybe into like an even bigger, higher level paradigm, which is which is crazy. And, you know, at QRI, we have a lot of respect in general for thinkers who are what Mike calls one true ontologists, you know, who kind of like take one principle or one set of ideas and try to basically apply it to all of reality and see how far that goes. And, OK, maybe it doesn't apply to all of reality, but in the process of trying to apply it everywhere, you discover a lot of things and you may be able to massively compress a lot of, you know, crazy, highly detailed information reality

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with just one principle. And the free energy principle may do that. So briefly stated, and I'll go into more detail, essentially, it says that any dynamic system that is actually capable of surviving is actually able to sustain itself, must be able to, in a sense, minimize the entropy is receiving from its environment. Right. So you have like a little cat. And if it's kind of just always going towards, you know, the absolutely most chaotic environments possible, it's going to die. You know, like it's going to go to the oven or it's going to go. I don't know. But the point is that, like, if you're like maximizing chaos, that chaos is going to eat you. Yeah. So basically it's going to shake you apart. So systems that actually do survive, that actually are stable, as I was talking about, it's like this generator for the theory of evolution, are basically those that are able to minimize the amount of entropy, unexpected entropy that the environment basically imposes on them. And from that, through a chain of inferences, you can arrive at the implication that dynamic systems that survive in the universe have to, on some level, have an internal model or representation of their environment, such that they can anticipate what is going to happen to them, given the particular input that they are receiving, and basically avoid states of very high entropy as a consequence. So it's kind of crazy, right? Like the thing is, like, this may apply to a lot of systems that are, you know, counterintuitive. Like, you know, one thing is the brain where like, OK, we kind of intuitively understand that, yes, we do have kind of an inner model of the environment. But this may also apply to other things like, you know, like a whirl on water or like a tornado or like other dynamic systems like that, you know, convection currents and things like that, that like implicitly, you know, maybe not explicitly, I'm not saying that convention currents actually contain kind of like a picture of their environment inside them, but implicitly, they must be doing some level of computational processing that is, in a sense, making them maximally adapted to their environment. And now there's like various schools within, you know, the free energy principle literature of like, OK, are the representations explicit or implicit? And like, maybe that depends on the particular system that we're talking about. And, you know, I think like most people would generally agree that like, at least the brain is making explicit representations. So it's not, you know, we're not just convection currents. Like there's something more explicitly computational going on. But at the same time, it must be satisfying kind of this principle that if it's going to survive, it has to actually be able to predict this environment. Now, the actual term free energy is an information theoretic construct. So it's not a physical construct. It's

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an information theoretical construct that bounds evidence for a model of data. So here's the thing. Like if you had complete information about the world and the inter latent states, basically the latent parameters that give rise to what's happening in the world, you could, in a sense, like compute how surprising a particular set of input is. And in that sense, like we would actually be talking about the surprise minimization principle, as opposed to the free energy principle. So why are we talking about the free energy principle instead? And what is this free energy? Well, it's an information theoretical construct. It's not free. It's not energy in the physical sense, although, as I'll mention in a moment, there might be an isomorphism in some circumstances. But free energy appears basically in machine learning and statistics. You know, I have like this book, for example, of deep learning and, you know, in page 625, you have this definition of variational free energy, which is essentially the point is that. Yeah, I think like here is the variational free energy. And essentially the point is that this provides an upper bound to how surprising the stimuli is, given your generative model of the world. I know these are a lot of words. And, you know, to do justice to this topic, it would take many hours and actually a detailed presentation with definitions and all the math in there. But what you have to know right now is that, you know, the variational free energy is an upper bound on the true surprise that a particular stimuli is generating. And surprise here means that basically the model says that these are very unlikely events. So this information theoretic construct, like the negative of the log surprise, basically provides kind of this total surprisal of the stimuli. And that is what you should be minimizing. You don't have access to that. It's intractable. So instead, what you can do is try to minimize free energy, which is an upper bound that is tractable and is computable. And it's essentially something that, yeah, in machine learning is used routinely to basically optimize inferential learning in probabilistic graphical models. Again, apologies for all of the jargon. What you have to get is that this is a clever and tractable way of minimizing how surprising the stimuli are from the environment. There's a couple of things to mention here. It's like, OK, if somebody might claim that if you're minimizing surprise, why don't you just go to a dark room? That's kind of the quote unquote dark room interpretation. Well, yeah, because if you're in a dark room, everything is infinitely predictable. You know, like you know exactly what you're going to be getting. And that in principle should make you feel great because you're minimizing the prediction errors that you're getting. Well, not quite, because actually the model that we have of the world is dynamic. And the things that are expected

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text:
in that model are sequences of events. So actually, there's no kind of like, you know, local maxima that is kind of like, oh, you know, this homogeneous perfectly symmetrical state is like, you know, maximally expected. You know, because your model actually goes against it. It actually says, no, like that would be very unexpected. You know, you don't expect to wake up in the morning and just see nothing and have no sensor input. That's an incredibly surprising state. Right. So actually going into dark room and becoming super thirsty and not eating and so on, that's a drastically increases your surprise and your free energy. So actually, in a sense, like minimizing free energy does entail kind of like doing a lot of very, very simple quotidian, like normal everyday things that in a sense, restrict the range of states that you find in. Because and that's an important implication to you're trying to minimize the entropy of the density of states that you can inhabit internally, because that in a sense, will maximize your chances of continuing to exist, existing definitely. And and yeah, I mean, as a consequence, yes, if you all of a sudden started to find yourself in a dark room, maybe on a low level, you can think that's, you know, very, very, there's no prediction errors because it's constant. At the same time, you will be making the inference that you're probably in huge danger. So in that sense, yes, actually, it doesn't minimize free energy. The other fascinating thing about this is active inference, that in a sense, there's two ways in which you can minimize the free energy. A, you can change your model, you know, if you're getting something very, very surprising, in order to minimize the free energy that that is generating, you can change your model, such that what you're experiencing now becomes less surprising. And that's one way. But we also require basically, a balance between the accuracy and the complexity of the model, because you shouldn't overfit. So basically, there's only so far, you basically a free energy minimizing system will be willing to, in a sense, adjust its model to the immediate, immediate information, because it's also taking into account all of the previous information. And like, you know, that shouldn't be discarded, if it's actually an embodied entity is going to survive, you know, new information could be garbage. So you really can't update too heavily on it. And also that gives, you know, brings us to the topic of the precision on the high level priors of the model, which is that, yes, I mean, like, very surprising things may not actually be warrant deeply updating the model precisely because you have way too much evidence. In the past, that like, yeah, most likely, maybe the input that you're getting is like spurious or something. And you can maybe, as a constant, you know, spin up an additional model that is

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text:
about like, okay, what is the probability that data is noisy or faulty? And like, then you start to model under what circumstances data becomes faulty. And that becomes the way in which you minimize free energy, not just instantly updating your model. And the other thing, though, is that it will be the other way of minimizing free energy is to actually act on the world such that you actually make the inputs later on more expected. So that's kind of like, yeah, basically, you're like close to a hot stub or like, you know, I don't know, like a very, very chaotic bar where there's like about to, you know, a bar fight is about to be to break and like, there's a bunch of drunk people. It's very chaotic. You know, one that's going to generate a ton of, you know, free energy for you, for you to minimize it to something you can do is kind of like start walking and getting out of the bar and you will be outside is going to be a way more predictable environment. So there you go, you know, like action onwards, you know, that can also be a method to reduce the free energy. And so in the end, what you get with this theory is kind of this beautiful interpretation of, you know, dynamic systems that survive, including humans, that they will be kind of like in a balanced way, modifying their internal models and also acting on the world so that there's future prediction errors. And that as a consequence, you know, what we're doing right now is kind of doing that implicitly on some level, you know. Another thing to mention is this concept of the Markov blanket, which is basically in probabilistic graphical models. You should look that up if you're curious. Basically, you can define this notion of a barrier between an inside and an outside, which is basically for a given set of nodes, you can look at all of the nodes that in a sense, if you were to kind of like fix them, they would in a sense, like lock in the information that their internal nodes can get from their environment. In a way, it's kind of like the Markov blanket is the outer shell of a particular set of probabilistic graphical models, nodes. And in that sense, they generate an inside and an outside. And what the free energy principle will entail is that whenever you have a Markov blanket, and you know, those are, and it's modeling a dynamic system, it will be the case that the free energy principle will apply for that Markov blanket. And, you know, in actual something like, you know, Bayesian predictive coding models, where you have a hierarchy of kind of like models that go from very low kind of like sensory data to like very high level kind of like notions of the self and so on. That in a

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