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much as possible, or scaling down the speed of the simple computations and speeding up the computation of the more complex ones, such that you kind of emulate the outward external behavior of something that would actually be implemented with a physical system that isn't a classical Turing machine. But if you do that, in a sense, you are kind of like masking what is actually interesting about the system, which is what that it was allowing you to do those computational speedups. Now, in the context of consciousness and reality, and like why consciousness was recruited by natural selection to perform computations, it's something that I would define perhaps as not only embodied cognition, and I guess I should add a footnote here, which is that embodied cognition for a lot of people kind of like stands in for magic, essentially. It is very weird because you have kind of this quadrant of like, okay, people who think that embodied cognition truly matters, or is necessary, no, like truly matters and is useful. And then there's also how kind of whether you believe embodied cognition is actually necessary for consciousness. And at QRI, we would A, actually say that embodied cognition is really significant, but B, it is not necessary for consciousness. And that, you know, that quadrant is actually fairly rare. Usually you have people who will either not believe that embodied cognition matters at all. And they will say like, yeah, no, you know, a neural network embedded in a classical computer, even if it's not interacting with the world, it could still in principle generate an experience. Or you also have the crowd who essentially believes that embodied cognition is really significant, but also they will say that it's necessary. But we actually stand kind of like in the weird corner where we can say, for example, that no, actually, you don't need input from the environment and you don't even need to move around in order to be conscious. And actually just like dreaming is an example of that. You don't actually require external input. You don't require to be embodied in, you know, an action system that interacts in a feedback closed loop with the environment or something like that. That's actually not necessary. The nervous system is quite sufficient. However, embodied cognition does happen and it is really powerful. And the way in which we think it's actually very significant is through this concept of the resonant modes of the system, which is actually, yeah, embodied cognition and holistic properties of the entire system, except that for the most part, they're constrained to the nervous system and its harmonics. And, you know, in a way when you're hallucinating, kind of moving around in your dream, yes, that is holistic embodied computation, but it's going on in your somatosensory cortex, not actually in your arms and legs. You see what I mean? Okay. So that's kind of like as an aside, but in a

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text:
sense, a generalized or a enriched conception of computing would, for example, also include this notion of intrinsic information in, you know, intrinsic states, physical states. We could maybe talk about this in terms of monodologic or like monadological network or something like that, where actually you have like monads, which are like irreducible ontological objects that contain non-zero amounts of information and that they're like interacting with one another. Okay. So if you have something like that, some kind of like ability for reality to kind of like form these holes, these irreducible holes that contain non-zero amount of information, and they can interact with one another, that's a very powerful concept. If they can interact with one another, yes, you can use that in order to implement an input output algorithm, a particular function. Now it's going to be more interesting than just a Turing machine, because in the process, you know, in between the input and the output, you actually went through, in a sense, embodied, you know, actual unitary states with non-zero amount of information or like more than one beat. And from that point of view, actually, the thing that's important to keep in mind is that the input-output consciousness is not the input-output function that is being computed, is actually the quality of the intrinsic information of the bound states in between. Okay, so that's an enriched conception of what computation can even be like. Okay, so then the question becomes, well, what kind of computational speedups or benefits would you get from this kind of like monadological networking computing? Well, actually quite a lot. There's a lot of possibilities here. And I covered quite a bit of it on the video called Solving the Phenomenal Binding Problem, where essentially, I argued that the actual unity of experience gives you the possibility of, you know, extremely fast and parallel fashion, compute the harmonic resonant speedup of what is topologically segmented out. And that itself can be computationally relevant. So that is one example of how kind of this monadological networking computing can actually be beneficial. But there's really like a lot more. And kind of like taking a step back and like figuring out, okay, like what do the nervous system actually uses consciousness for? Well, I will have to, in a sense, ask you to redefine your very conception of intelligence. So like, what is intelligence? And like, what kind of problem is consciousness solving for which intelligence, genuine intelligence is required? And here is like an interesting kind of like perspective from David Pierce, in his essay called the Biointelligence Explosion, which I highly recommend reading. But I mean, basically, one of the main overarching claims that he makes is that intelligence is actually a function of one's whole phenomenal world simulation. And not only a property of kind of the sequential, logical, linguistic, serial processing that is emulated, like that is on running on top of these massively parallel world simulation

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text:
that is being implemented in the nervous system. So it's very crazy to think about these, but like the actual way in which you render objects, the things around you, the shapes, the colors, the vibe, the mood in which you are, all of those things are contributing to the information processing that your nervous system is actually doing. Like being in a particular mood is actually very computationally significant. It's not an epiphenomenon, it's not trivial. I mean, there's a reason why it was selected by natural selection, why we are in particular moods that usually they're like adaptive to the particular situations in which we are, which is not always the case, but like, at least from an evolutionary standpoint, that's why it was recruited. So there's actually quite a bit of ways in which we can enrich our conception of intelligence to go beyond just kind of, you know, serial logical linguistic reasoning, which is, you know, the classic conception of intelligence that, you know, it's obviously really important, obviously correlated with a lot of really important things, but is by no means the whole account. And actually, if you just have that account of intelligence, you will be deeply misled about like, what is it that the nervous system can do? Okay, so there's several things like number one, intelligence requires the capacity for binding, both local binding and global binding. Essentially, it requires the ability to generate a world simulation where features are actually properly bound to each other, such that like, you know, a particular color belongs to the shape from which it's associated in the environment. And you don't perceive like disembodied colors, for example. So that is like binding. And it's really important. Local binding is features put together for a unified phenomenal object. Whereas global binding is that the features belong to the same experience, whether or not they're like, you know, immediately connected to a phenomenal object. And, but either way, I mean, like binding is both philosophically very puzzling, and computationally very significant. So non psychotic binding is a key ingredient of intelligence. But then you also have kind of like self, the self as a coordinating mechanism, something that allows you to, in a sense, model what is happening as happening to somebody over time with agency and the capacity to kind of organize its contractions and its, you know, expansions in such a way that it navigates the virtual environment. So that's, you know, another part and you could be really good at, you know, non psychotic binding, but actually lack of an adaptive sense of self, for example, with schizophrenia, or perhaps like some particular, yeah, I don't know, psychedelic psychosis or something, something like that. Then the third one is perspective taking capacity. I mean, like, that's a very intrinsically, you know, challenging problem of how do you in a sense, like simulate what others are experiencing, and what they are believing that you are

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experiencing, and so on, and so forth. And or, you know, in the case of somebody like Shakespeare, like six order intentionality, what does he think that she believes that he wants that she, you know, dislikes. So like, that kind of like ability to reason, okay, that's also like something up in which it is predicated upon which the properties of our world simulation, and which, you know, is integral to intelligence. Then also, we need a metric to distinguish the trivial from the significant. And, you know, you can, we can talk about like, okay, like, somebody like Kim Peek has tremendous computational power when it comes to, you know, calendrical questions, or arithmetic and things like that. But it's not the same as a, you know, a parallel man, somebody who can prove the Raymond conjecture and like, okay, like, that requires some kind of understanding to be able to distinguish the trivial from this from the significant. And that's not only, you know, just kind of like an IQ type of skill, it requires kind of like some deep heuristics, and ultimately requires some kind of like philosophical insight into what matters in reality. So that for sure is yet another key component of intelligence. Then you also need the ability to navigate and utilize different states of consciousness. And like, you know, you could have somebody who's really good at math, but in a particular bad mood, destroys his or her life, right. So in that case, like that particular person would not be very intelligent when it comes to navigating particular mood states of consciousness. I mean, let alone, I don't know, DMT or something like that, which is kind of yet these extreme, extreme changes in the state of consciousness and the computational properties that that entails. So being able to navigate that it's quite, you know, importantly, something that intelligence is to some extent related, to which is related. And for sure, I suspect that, yeah, I mean, the super intelligences in the future will start by looking more like, you know, a super Scholean Academy, as opposed to, I don't know, a super Watson or something like that, or a super GPT-3. And yeah, finally, number six, it is yeah, kind of like the serial logical linguistic reasoning, which yeah, it's essential, it's obviously key in order to create, you know, digital computers, in order to reason philosophically and for sure, in order to kind of like do altruism at scale, kind of like this systematizing type of intelligence. But it's important to recognize that that's just one element of a lot of different other kinds of intelligence that are really non-trivial. I mean, like one of the things that David Pierce points out is that AI researchers may confuse the agreeable, you know, may confuse empathy and the perspective taking capacity of people with just a personality factor of agreeableness. Whereas like, no, actually, high levels of agreeableness are, you

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know, computationally very demanding, like actually, you know, doing perspective taking properly, it's very, very computationally demanding, it's not a just a personality trait. So, you know, that's kind of like something that it's easy to get really wrong. And yeah, it's a non-solved problem, as far as I'm concerned. Okay, so that is, okay, we enriched our conception of computation, we also enrich our conception of intelligence. Okay, so what's next? Well, what is next is, I will make the overall kind of overarching claim or observation that to a large extent, intelligence is all about choosing the right self-organizing principle that will solve the problem for you. And there's like a wonderful, wonderful recent paper, I'll add a link in the description about essentially how like if you tangle a knot, like with a cord or something, and you put it in a graphics engine, and you kind of like introduce these rule, where the surfaces are repelling each other as much as possible, you can put in something that is like very, in a very gnarly knot, and on its very own will unentangle, it will disentangle, and it will become kind of like just the cord. So, is that intelligent or not? Well, the intelligence there is to, in a sense, like select the right self-organizing principle, in this case, repulsion between surfaces, that will, in a sense, as an emergent effect of letting it run in a massively parallel fashion, solve the problem for you. So, I would claim that actually, this is like much more similar to what we do on a daily basis than, let's say, programming a Turing machine or something of the sort. So, the claim is that, okay, we have, yes, we do have like some specialized hardware to some extent, but also we have the capacity to, in a sense, like program self-organizing principles that then will solve the problem for us. And, you know, there's a lot of examples of these, and the thing that is actually tricky, the thing that is, in a sense, the meta intelligence behind this is the capacity to train new self-organizing principles to solve new problems. And that, I would say, is, yeah, completely fascinating property of our nervous system, that we're capable to, in a sense, like reprogram it so that it instantiates new self-organizing principles. Okay, so another, you know, another thing that I would like to mention, another, okay, yeah, let's say that. Now, one example of kind of like a self-organizing principle that is all over the place, and we've talked about it, it's annealing. And annealing, I mean, really is that because you're, in a sense, like the problem that you're solving is, for example, how do you get atoms to be aligned with one another in a lattice without having to individually go through each atom and kind of like orient it in a particular way so that, you know, everything becomes a checkerboard pattern or

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text:
everything is like properly aligned. Like, you don't want to do that manually. Is there a way in which you can kind of like do something to the system such that on its own, it will find that configuration? And the answer is yes. You can heat it up at a certain schedule and then let it cool in such a way that you get kind of a islands of constraint satisfaction, like bundles of atoms that in a sense do satisfy the constraint of being aligned with each other. And then they form nucleation points, then in a sense propagate, and then they negotiate between each other. And, you know, in some cases, kind of like an annealing cycle that heats up and cools down and heats up and cools down and heats up so that like it goes through kind of like several stages might actually work really well because you will have like the islands of coherence kind of congealing and crystallizing. And then you might need to kind of like reheat it a little bit so that they kind of jiggle and then you, they crystallize as an entirety, the entire, you know, scope of the crystal lattice actually becomes well organized. So in this case, like choosing the right annealing schedule would be kind of like the intelligent, the thing that requires kind of intelligence here, whereas like the actual organization is, you know, something that happens at a physical level, like you're kind of like recruiting the massively parallel nature of the universe to solve the computational problem for you. And, you know, there's some really exciting, you know, new kind of applications in computing that are kind of like doing these. So in particular, there's like some optics research in Caltech. I'll share a link in the description as well, where, I mean, essentially what they're doing is using optics to solve what's called the icing problem, which is, I mean, it's very similar to kind of like the annealing problem of like atoms wanting to be aligned with their environment, except that here is a much more general situation where essentially you have like nodes in a network. It doesn't have to be a lattice. It could be an arbitrary network where each of the atoms is trying to, in a sense, like be in the same alignment as the nodes is connected to, or the opposite. Like there's like several formulations, but the point is that in a sense, the more alignment you have, the lower the energy of the entire network is. And if you do this with optics, you can actually explore states of superposition. You can explore many possibilities at once, which drastically accelerates the, you know, the random complexity of the algorithm. And so you end up having something that is at a physical level, you know, recruiting the massively parallel nature of the universe in order to find the local minima of energy or the global

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text:
minima of energy for you. And it's doing that in a way that it's faster than a classical computing. So when you have something like that, then actually the problem becomes not how do I solve this constraint satisfaction problem? Actually, the problem becomes how do I take the problem that I have and then cast it as an icing problem? Because then you have the specialized hardware and the physical, you know, the self-organizing principles that will solve the icing problem for you. So actually, yeah, the non-trivial problem here becomes how do you map your problem into an icing problem? And I think like these will, you know, show up all over the place when it comes to, you know, human intelligence. That in a sense, we have kind of this range of possible self-organizing principles that we're really good at instantiating. And then actually the thing that is learned, the thing that is really tricky is figuring out how to translate the problem that you have into a format that one of these self-organizing principles can solve for you. And I think like we will, I mean, ultimately be hashing out one after another, kind of like one, you know, how we solve this particular social cognition problem, how we solve this perceptual problem, how we solve these, you know, homeostasis problem. And I suspect in all of those scenarios, we will find some self-organizing principle that is actually doing the computational legwork for you. And I mean, isn't it fascinating, like what it actually feels like to solve a very difficult problem? I mean, there's definitely kind of an embodied component, but it feels kind of like this massively parallel thing that you're in some implicit way, exploring a massive space in a very efficient way without explicitly kind of like laying out all the possibilities. You do it in this, yeah, very crazy, massively parallel way. Okay. So now let's go into some things that actually suggest that, you know, our consciousness is doing something like this. And well, you can actually just pick up a book on visual illusions, and you will notice in a sense, tons and tons of artifacts of your visual system. And what they demonstrate is that you have this massively parallel process that is trying to find an equilibrium where essentially, yeah, the fixed points of the system are in a sense, like adaptive representations of the environment. So let me see one example. I highly recommend, like, if you're going to take a substance, you know, a crazy substance like DMT or ketamine or something, not that you should, or I'm not recommending that you do, but if you are going to do it, I do recommend that you have a visual illusions book right next to you so that you can see kind of like extreme examples of these kind of holistic field behavior and self-organizing principles happen in your visual field as you explore the visual

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illusion. And, you know, this is also noted by quite a few neuroscientists, a really excellent kind of new source of insights. It's in the space of like Stephen Lehar and Selen Atasoy, I would say, I highly recommend this, Stephen Grossberg. And essentially, he also kind of has this notion of resonance, except this, you know, it's not linear resonance, it's a more generalized notion of resonance. And I'll definitely make a video specifically about it. But one of the many things that he points out is that there's a lot of like visual illusions, for example, the Kanye psi illusion, that strongly suggests that we are doing some kind of what might be described as a feeling in process, that in a sense, all of the edges, this is also Stephen Lehar realized these like all of the edges in the Kanye psi illusion, you can think of them as in a sense, kind of like sending a wave of energy, kind of like radar. And where all of the waves of energy collide, that's actually a symmetry point. It's a point where it's very close to a lot of reflections in multiple dimensions. So essentially, when you have something like that, the waves of energy propagate back, and they quote unquote, ray-phi an object. So the Kanye's illusion, that you see these like square, you hallucinate the square with even if it's boundaries. Yes, that is kind of an equilibrium point of all of these waves of energy trying to expand as much as possible and ray-phi-ing those symmetries. Apologies if this sounds like nonsense is something that we actually probably would have to go through much more slowly to really absorb. But here's another one, the water color illusion. I don't know if you guys can see it very well. But I mean, essentially, you have this fascinating effect where by the gradient of the boundary, like in what direction you have a gradient of color, you will determine whether the entire column looks blue or not, or more blue, you know, kind of like what is the bluish region versus the less blue region. And there's a lot of other kind of illusions like that. And that actually suggests quite strongly. And I mean, this is crazy, but I think it's the case. I think it's the case. It's really, really puzzling, really crazy, but I think it's the case. And that is that we actually undergo a filling in process where every region in your visual field is painted with something that is kind of like water color. And the paint is trying to expand and expand and expand. And the reason it doesn't, you know, expand over the entirety of your visual field is actually because there is other colors that are also trying to expand that essentially make those waves of colored energy, as it were, collide with one another and form boundaries. So in this worldview, actually, all of the boundaries

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are sort of illusory, because all of the boundaries are actually this equilibrium between forces. I talked about in the video on cognitive sovereignty, about how the ocean, beach is actually very beautiful, because it's a equilibrium of a lot of different forces simultaneously. And the visual field too, your visual field in a sober state is kind of like a beach. I mean, it essentially it's a lot of forces are in equilibrium. And that equilibrium is in this massively parallel fashion, finding an adaptive representation. And you know, on psychedelics, it's kind of like you have this hurricane or something that yeah, can blow out all of the sand, you know, like it will mess with the equilibrium significantly. And then you can have like a lot of crazy things happen. But you will still see in every point in time, essentially, that whatever you do happen to experience is whatever equilibrium of forces is currently realized. Here's another example of this, like the gradient, like this gives you the illusion that the interior of this shape is yellow. And the answer is that it's not, it's actually not yellow at all. It's a visual illusion. But this suggests, essentially, that you have like waves of, you know, something like yellow energy, that sounds so bad, but it's more or less the case, at least perceptually, that are propagating and bouncing off of each other. And in the equilibrium, they essentially paint the objects around us. Okay, so that's a very concrete case. But I mean, there's like many, many other things going on in the visual field. I mean, this is just colors and edges that demonstrates this. There's also this illusion called neon spread, you can look it up online. You know, very efficient symmetry detection. I mean, the fact that on psychedelics, essentially things that are quasi symmetrical, they click into symmetry. Okay, like when you have a wallpaper symmetry group in your visual field, it will be in this crazy state of resonance and holistic behavior, like the whole thing acts as a unit. Okay, like that is the signature as well of kind of these like massively parallel holistic behavior, because it is a resonant mode. And a superposition of resonant modes is such that it gives rise to a particular wallpaper symmetry group. And the same happens also with space groups in 3D. If you take enough DMT to go to what we call the crystal worlds, your space will be tessellated with, you know, 3D symmetrical structures. And those actually will behave very much like how Stephen LeCharre describes it, which is with these shock wave propagation algorithm, where essentially, the boundaries of the symmetrical shape will be sending what feels like these waves of energy that collide against each other, and they form stable attractors. And those stable attractors, by design, actually tend to be extremely symmetrical along several dimensions simultaneously. Yeah, so that's also like, yeah, an example of

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text:
field behavior in visual experience and kind of this massively parallel process. There's also other things that suggest kind of that we use self-organizing principles for computation. I mean, another very stark example is annealing effects. I mean, very generically, on psychedelics, they are kind of like broad spectrum energy enhancers. You will experience periods during a psychedelic state where in a sense, you will feel that your energy body is resonating in a particular way, where the resonant modes will start to interlock with one another. And if you kind of like follow that gradient, we could talk about, for example, some sort of like oscillatory entropy. I mean, this is kind of like inspired by the concept of, you know, harmonic entropy. It's a concept in music theory. I highly recommend looking into it as well. But yeah, I mean, essentially how pleasant a particular chord sounds like can be computed and approximated essentially by how complex would a superposition of harmonic resonant modes be in order to generate the spectrum that you're hearing. And essentially, like more simple chords tend to sound better. And it's for this reason, because you can kind of generate that sound with few harmonic resonant modes. Okay, so likewise on a psychedelic, when you're getting to kind of like these very, very mystical states, they tend to be very low in information content. For example, like empty space, or the feeling of everything converging into one point and things like that. So those would be low in oscillatory entropy. And the fact that they happen, when they happen, and in the way that they happen, strongly suggests a crazy annealing effect, where like maybe in the second hour of your LSD trip or something like that, things are kind of like still very chaotic. But then if you meditate on it, and you try to kind of like find the peace within, as it were, yeah, maybe by the third or fourth hour, you can kind of enter into these like spaces that are like resonating in synchrony. And those would be like much simpler. And they tend to be also much higher in valence, much more blissful, more mystical, more loving, and so on. And generally speaking, they tend to be very simple. And also that actually somehow it's healing for reasons that I'm not going to go into this time. If you're curious about it, you can look up healing trauma with neural annealing, which is a presentation I gave at the Oxford Science of Psychedelics Club. It's online, you can find it. Okay, so like crazy annealing effects also suggests that, you know, a lot of what's going on in our experience is actually the result of these self-organizing principles, you know, running in a massively parallel hardware, and how they interact with one another. Now, I'll also mention that there is these crazy things that can happen on meditation, for example, nimittas. And nimittas are essentially when there's

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text:
a threshold of concentration. And you can look up Buddhist annealing, a video I made about like, okay, how do we quantify concentration? What does concentration even mean in the context of meditation? I go much deeper into it. But you know, a very, very broad picture is that concentration is sort of like a coefficient of how many of your resonant modes are either in a state of harmony, could be, for example, different scales, but they're like interlocking with each other, resonance, and synchrony. So when you have those, the more of your harmonic resonant modes are either in resonance, harmony, or synchrony. There's also this generalized concept called coherence in neuroscience, which actually captures these in a much more general way. So whenever you have that, whenever a lot of your resonant modes are in a state of coherence, that tends to be a highly concentrated state. So when all of your resonant modes are in a sense, interlocking, such that your attention gets concentrated in the center, and it doesn't move from the center, okay, like that would be a highly concentrated state of consciousness. But there's also, you know, highly concentrated states of consciousness where you're actually have broad attention. So it is not the case that to be highly concentrated, you need to in a sense, like focus in a narrow spatial space, a spatial scope, because the spatial scope could actually be very large. Could it be, you know, the sphere of infinite space, for example, a boundless space, and still be in a highly concentrated state. So when you cross a certain, you know, level of concentration, you start experiencing this thing that is called nimitas, where many of them are described as like luminous. So essentially, you can feel like this very, very bright light, like at the top of your visual field, you don't necessarily look at it directly, but it's kind of like influencing your visual field, almost as if there was like a light behind that you weren't like perceiving directly, but it's like shining into your visual field and somehow like making the rest of your energy body coherent with it. So that's one example of a nimita, but there's also a modal nimitas. So kind of like the feeling of like very strong gravity in one particular direction. Now, all of those things can be interpreted as essentially field behavior, especially we have kind of like an electromagnetic theory of consciousness. In some, you know, formulations, we could actually talk about the way the attention flows as literally following, for example, the magnetic field lines, and that in a sense, when you're shifting your attention from one place to another, you're kind of like shifting the magnetic field lines of your consciousness so that it gets directed in a different way. Now, this kind of like connects with an empirical description of, in a sense, how you do world building with your imagination. And the way

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text:
in which you do this is a sense by generating clusters of, you know, resonant representations interlocking with one another. And this gives rise to this concept. I'm throwing a lot at you, but yeah, hopefully some of you can take it, which is the concept of an oscillatory complement. So if you are like fully absorbed into a particular shape, let's say a hexagon, the crazy thing is that, you know, if you're fully absorbed into a hexagon, it's not that you actually become a hexagon, you know, it's not that your consciousness is shaped like a hexagon. It's actually something a little bit crazier than that. Your consciousness has become the oscillatory complement of a hexagon, meaning that it resonates with a hexagon. And actually the entirety of your experience will consist of something that is shaped like a hexagon, which is a tiny component of the experience, and the rest of the experience, which is the oscillatory complement of the hexagon. And that is what you're becoming. And that is a thing that is actually very low in oscillatory entropy. Okay. And also importantly, there are two things that your nervous system is constantly trying to do. I mean, this is kind of like from, you know, free energy principle from Carl Freeston, but we are blending it with connectome-specific harmonic waves, like Ceylon-Atasoy, and then adding, for example, neural annealing and the symmetry theory of valence to actually create a unified paradigm here. So what I mean by this is, okay, the free energy principle will generally indicate that an organism needs to simultaneously A, minimize prediction errors, while B, minimizing the model complexity that it has about its environment. I mean, you could have like in principle, a huge lookup table that predicts every possible, you know, new sequence of inputs as a function of like sequence of past inputs. But it's, you know, computationally intractable, and it's impossible to in a sense, like actually get all of that trained up, you know, you would need an infinite amount of computation or like a very large amount of computation. So in practice, you actually require a balance between how complex your model is internally, and you know, how good your prediction of the environment of the environment are using that model. And the claim here that we make at QRI is that actually, what the nervous system is truly trying to minimize is dissonance, is dissonance between its harmonic modes. And what does that look like? Well, that looks like harmonic modes that are similar to each other, they're not actually capable of interlocking. So they generate these like strong beat patterns, which in a sense, create these very sharp derivatives, these very sharp transitions, which in a sense, is a risk factor for strain. And strain is the thing that is actually, you know, the thing that actually feels unpleasant. You're kind of this field, and whenever that field is actually experiencing strain, that

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