Theories of Everything with Curt Jaimungal - Society Is At A Major Turning Point | Karl Friston Λ Anna Lembke

Episode Date: March 21, 2024

In today's episode, Karl Friston and Anna Lembke emphasize the urgency of reevaluating our personal and societal practices in the face of environmental, mental health, and addiction crises, through the lens of "active inference". Please consider signing up for TOEmail at https://www.curtjaimungal.org  Support TOE: - Patreon: https://patreon.com/curtjaimungal (early access to ad-free audio episodes!) - Crypto: https://tinyurl.com/cryptoTOE - PayPal: https://tinyurl.com/paypalTOE - TOE Merch: https://tinyurl.com/TOEmerch  Follow TOE: - *NEW* Get my 'Top 10 TOEs' PDF + Weekly Personal Updates: https://www.curtjaimungal.org - Instagram: https://www.instagram.com/theoriesofeverythingpod - TikTok: https://www.tiktok.com/@theoriesofeverything_ - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - iTunes: https://podcasts.apple.com/ca/podcast/better-left-unsaid-with-curt-jaimungal/id1521758802 - Pandora: https://pdora.co/33b9lfP - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Subreddit r/TheoriesOfEverything: https://reddit.com/r/theoriesofeverything  

Now we're at a point where there's a whole generation of people that are coming to grips with the fact that we need to change everything about everything that we do, and we need to do it fast. And I think that's just a wonderful frame or concept for all of us living in the world to periodically just take a moment and take these models and just say, you know, everything I think I know about the world, I'm going to temporarily suspend it, and I'm just going to be open. And when that happens, we can be present and learn in a way that it's not possible when we're just trying to reinforce our models I was invited by the innovative and prestigious Active inference Institute to host an all-star academic panel where questions explored are what's the difference between building a model and building a narrative?

What about the relationship between predictions and what constitutes you? So that is personal identity. You're going to hear from top neuroscientist Carl Friston at the University of College London, Anna Lemke, who's a professor of psychiatry at the Stanford Addiction Clinic and author of Drug Dealer and Dopamine Nation, links to which are in the description. You'll also hear from Rafael Kaufman, who's the CTO of Digital Gaia. You'll hear from Burt De Vries, who's a professor at Einhoven University of Technology. Lastly, you'll hear from Guillaume Demas, who is an associate researcher in computational psychiatry at the University of Montreal. So my neighbor.

Because why? I am Kurt Jaimungal and my background is in mathematical physics from the University of Toronto, which is where I'm based, and the whole project of this channel is to analyze various theories of everything usually in the physics sense that are proposed, including grand unification with gravity plus dualities and other schemes, as well as what constitutes consciousness. You can find this podcast by typing in theories of everything onto YouTube or whichever podcast catcher you have or you can just click the subscribe right here. The links to the Active Inference Institute are in the description as well. A special thank

you to Daniel Friedman for putting this all together. Enjoy. Okay now speaking of the brain as a computer we frequently hear in these circles the brain as a predictive machine. Where does this the quote-unquote brain as a computer, we frequently hear in these circles, the brain as a predictive machine. Where does this, the quote unquote brain as a predictive machine have its limits? And also, is that to be interpreted as implying conscious experience is a predictive machine as well? Okay, and if not, why not? I think Carl, you'd be a great person to start this off and then we'll go around the table again. Yeah. I was mindful of Mao's presentation, the past half hour, um, the notion of

temple thickness, um, and I'm sure that's got a lot to do with the necessary conditions for the kind of consciousness. I'm guessing you're referring to, you know, that freedom from the moment. Um, and if one reads prediction in its psychological or pretension nature in terms of being able to predict what will happen in the future, I think that's going to be a key bright line between things that do not possess a certain kind of sentience and things that do. And that bright line just rests basically upon, well, from the perspective of active

inference, having a generative model that includes the consequences of your own actions in the future. So just by having consequences, you're now talking about the future and the consequences in the future now become random variables, therefore you have to infer them, which leads you directly to the notion of planning as inference, which means that bright light is just the difference between things that plan and do not plan. I would guess that that's where you probably want to start in terms of foregrounding the role of prediction as being an aspect of self-organization and its reading under active inference that can try this conscious

things from non-conscious things, namely the ability to plan. Does that make sense? I have follow-up questions. We'll get to them at some point. For now, Anna, do you have any comments on that and Bert as well afterward? Well, you know, I'm new to this field, so I'm just familiarizing myself even with the language. But I can respond to the question, the brain is a predictive machine in terms of the work that I do clinically in the kind of psychopathology that I see. When I'm working with patients, you know, I work primarily with narratives, the stories

that they tell about their lives. One of the recurring themes I've seen through my work is that when patients tell stories in which they are perpetually the victims of other people's actions or the world, that tends to be a predictive model for them so that they will then go out into the world and unconsciously create scenarios which will perpetuate their victimhood. And that part of getting into wellness is to stop seeing themselves as entirely the victim of other people or circumstances, instead begin to appreciate what they contribute to their life problems.

So I guess when I think of the limits of the brain as a predictive machine, I think in some ways, one of the big limits is that it's a very powerful predictive machine that actually allows us to subsequently shape what actually happens to us and or our perceptions of what happens to us, which then can perpetuate a false narrative. And I'll just give one small example from my own life. I've had my conflicts with my mother and one of my beefs about her is that she's a very poor communicator and that whenever I email her, I either get a cryptic response or no

response at all. And it drives me crazy. And about five years ago, she sent an email, asked me some questions. I wrote her back and responded, and it was clear to me that that required yet a response from her, which I never got. And that then perpetuated my narrative about her as a very poor communicator and many other negative things. And then about three months after I sent that email, I found the email in my draft in my draft box. So I had never actually responded to my mother's email. I hadn't actually sent the email.

And that was for me, just a personal, wonderful example of the ways in which a wonderful example of the ways in which we can, our actions can actually be manipulated to support our models and perpetuate falsehoods about the world that we live in. Right. And I have a quick question, Bert, just for Anna, before we get to you. So use the word narrative there. How are you defining the word narrative? Is it the same as model? Is it a sequence of events? Like what is the specific definition of narrative? You know, I never really thought about it in those terms, but when I talk about

narrative, I'm talking about the stories that people tell about their lives, because that's sort of my data. And, and also about models because what I discovered about self-narrative is it's not just a way to organize the past, it also becomes a roadmap for the future. That's the language that I use, but I see it maps very nicely onto your all's language of modeling the world. I see. Professor DeVries, please. Yeah, it's clear that the ability to predict is the essence of

intelligent decision-making. Yeah, I have a different background, right? I'm not a psychiatrist. So I think about these things in different ways. The thing that I think about when I think about prediction is I would assume that brain predicts far ahead, that things get less accurate, right? If I predict that I want to go three quarters around, around about, I don't care about the centimeter where my car goes when I'm around there, I just want to get in the right lane. And I would assume that the brain doesn't take much,

takes less computations if you care about things less precisely. But that's very hard in a computer. We have, this is what kind of kills us in, I think in our current way of implementing active inference. When we want to predict a deep have, we don't care about the accuracy, but we don't know how to do it much cheaper when things are less accurate. And so that's still a, that's a problem that we need to be working on. It might be a key to building or to scaling up active interest agents if we can actually compute messages that we want to know that we don't care about if they are less precise, that we also spend much less computation on them. Thank you. Now, Professor Kaufman.

I am not a professor, but I'll answer anyway. They just call me Rafa. Yeah, so there's so many really interesting avenues here and it's not often that I get the pleasure of discussing this kind of stuff with this kind of diverse crowd. But I do, I mean, I've been interested in, in these questions for a pretty long time. And I think what comes to mind is like how, how active

inference as a lens, for instance, it enables us to, to, to get another sense So to get another sense of what non-dualist views on consciousness are saying when they get us experientially to notice the difference between what we actually perceive and watch between the different various different processes or things that are going on in our in our head and our tendency to like lump them all together into the same, okay, this is my experience that I'm immersed in. So noticing, being able to notice is different within the process of proceeding and acting, and to what extent it's automated, and the narrative or the various narratives that are going on in parallel in my head, whether I'm aware of them or not,

and our ability to just put kind of seemingly arbitrary meta level on top of it to try and make sense of it all and to force that experience of having multiple narratives and multiple framings going on at the same time makes sense of that experience in a way that aligns with our presuppositions about how things are. So I think that's super interesting. I also want to highlight that, this view of consciousness as being defined by having an narrative or an internal model that's about self or that at least is about self.

That leads, as Carl said, to planning as inference. That's actually super deflationary in a way that people are not used to thinking about it. And so it's an exciting, it's opening the doors to all sorts of exciting inter-disciplinary conversations to be had on the basis of, at least I feel like better, less talking past each other than we've ever had. So I'm excited about that. Sure. And how is it that you're using the word deflationary there? Carl, I remember I asked you about that when we had our podcast together. But Rafael, similar question. So you said deflationary view when we have narratives of self.

Do you mean it relegates us to something that consciousness is much less than? Yeah, I mean, it's not necessarily much less than in the in the all subjective sense, but it's perhaps much, much less than in the subjective sense, but it's perhaps much less than in the scientific sense. So one example of one not saying exactly that, but maybe something goes to what Daniel Dennett calls hetero-phenomenology, which is basically the statement that the sum of what one can say scientifically about consciousness is equal to what can be studied and modeled and theorized and communicated and learned and agreed upon on the basis of objective data about what people, including you, but also other people say about their experience, which, and of

course, like what can be measured of neural correlates and whatever, which is not the same as our, not necessarily the same as our experience of our directive, but we think is our direct experience of being conscious, right? So one way to look at it is, the science of consciousness doesn't necessarily have to put the primacy on on people. The, for, for instance, people saying they have qualia, uh, that, that we thought in that sense of this deflationary, right? It's, um, yeah, you have to explain why people believe they have qualia, not why people have

quality because it's not, it's not necessarily a scientific truth that people have quality. Professor Dumas. Yeah, so well on the limits of various predictive machines, we should be always careful to not move from one map territory fallacy to another one for sure. So we need to be skeptics and avoid to refind the methods. And well, this world symposium shows how this metaphor is super useful and fruitful. But I can see like two main limits or at least things where we should be careful. So following what Professor Lemke said, like in psychiatry, I think the looping effects and the way people picture themselves can be very unpredictable.

And so the way we think about the brain as a predictive machine, I think prediction for patients would have different signification of what we mean by predictive machines in the context of active inference. And in general, also, like the I can see that certain psychologists or anthropologists would have a big appeal all of a sudden for active inference and pre-energy principles, but taking the words as directly what they think the word means, we need to be careful in the way to communicate it. Typically, following also what Professor DeVries said about prediction as being super important for decision making, I think we should be careful about the weirdness of cognitive science and how it doesn't necessarily expand to a non-Western educated industrialized country

where maybe the cultural value is not about optimization of your decision making or your profit. And so that's the first thing where I think we should be careful. It's more like a matter of how to communicate the theory in that case, not necessarily a limit of the theory itself. But the second one is about the equivalence or not with other frameworks. So, like, I can see how we had Mao, Alvazan talking about category theory. I'm very curious about right now among the different frameworks that are out there, how can we define what is equivalent or not with active inference to be able to see those limits?

Maybe we are living what happened with quantum mechanics in the early 20th century with different interpretation, with different tools to model quantum mechanics. And here with artificial intelligence and cognitive neuroscience, we have also all those frameworks. And I think the limitation would be then to not take this interpretation as the only one, but try to have a cross talk and a differential diagnosis of which one is the best for explaining what. Okay, now my role is the moderator, but I would like you all to speak to one another. So I'm going to ask a question that will serve as well.

You each will speak on it, but as the other people are speaking, just think, okay, is there a question that I have or is there a comment that I have? So the question is what are some of the recent advancements or breakthroughs in your respective fields that you find particularly promising? And well, recent, let's say 2020 till now. So we'll start off with Rafael, you're smiling and you look like you're championing. I was thinking what is my field, right? Because we're very much in the last mile of applying the wonderful stuff that you all come up with and making it useful on the ground. But so I'm extraordinarily excited about research like what what BERT is doing. And you like as as Guillaume was saying, we're also starting to see some some convergence on

different ways to get to to the same, at least the same shape of answers answers and in some cases even to the same to the same results so one example of this is work that Chris Chris fields Carl and others have been doing on on the the quantum basically explaining quantum information theory and how it relates to to to the free energy principle looking to the free energy principle, looking at the free energy principle as the classical limit of quantum information theory. I don't pretend to understand all of it, but as somebody who is coming from a quantum physics

background, just being alive to see this kind of conversions happening, which as I mean, as Guillaume said, we've been at this business of like post-classical, what the hell is going on for over a century now, and it's nice to be at a time where again, we're starting to talk less past each other. And that's from a theoretical perspective, from a practical perspective, I think, just finding that we have all the building blocks

to create fast, interpretable, reliable, and aligned decision-making systems, which includes AI systems, autonomous systems that have all those characteristics. And it turns out that all the things that reinforcement learning and the neural networks people thought were very hard or impossible are sort of in the realm of what we can do. Looking at things from an active inference lens and vice versa, a lot of things that active inference models have in parkour

and so far it turns out if you toss in a neural network approximator here and borrow some other massively parallel computation techniques, it also becomes feasible. So it's converging. Yeah, for me, what's remarkable is that there's so many domains of what we thought was exclusively human or would be exclusively human for decades that in just the past couple years

robots or computers seem to be Just as good if not Exceeding us and I don't know if that's promising or worrisome. But anyhow Carl, please if you don't mind answering the question and then we'll go Anna then Burt and then Dumas Actually just reflecting upon your, one of your observations, I think it's very difficult to identify one thing. In a sense, what is impressive is the diversity of advances and applications. I just say that because that's what I was thinking over the past six hours, just listening to your amazing presentation after presentation and just noting how diverse.

And yet there's this common thread, this common commitment, basically sati our curiosity and using the tools that naturalize that kind of sense making, curious behavior and communication that inherent from sort of either maths or category theory or as Ralph notes now quantum information theory. But just to pick up on a couple of things which are relevant to this conversation. So the work with Chris Fields, couple of things which are relevant to this conversation. So the work with Chris Fields, it sounds lovely and exciting to bring quantum mechanics into active inference, but that's not the move that I think Chris is really wanting to champion. The move I think is something that we've all been addressing in one way or another, which is leveraging the scale-free aspects of this

principled approach to self-organization and hopefully self-organization to some kind of generalized synchrony. So that's where the quantum information theory gets into the game. It is scale-free and indeed he will go further and say it's completely background-free and everything is constructed. So I think that's a lovely move because once you've gone scale-free you then start to ask deep questions about how you couple one scale to another scale and in a sense ecosystems is just that. How do the delusions of an ecosystem, how is it constituted, how is it co-constructed, what is the structure of it? These are all questions about how one scale links to another scale.

So I think there have been lots of advances in that direction in many, many different fronts. And you can read that either in terms of coupling different sort of spatial or spatial scales, but probably more importantly, sort of spatial or yeah spatial scales but probably more importantly sort of temporal scales and you see that you know wherever you look you'll just come back to what do you mean by a narrative it is exactly I think as I said it's just a a plan it's just a story but notice the story has a temporal aspect to it. I have

narratives about being a good person, being a good father, being a good scientist. I also have narratives about, I want my cup of coffee or I have to go look after the, I don't, but Rafa has to go look after the child. So we've all got narratives at very, very different time scales. And of course, if you're, I just came back to Bert's example of, I'm an autonomous vehicle and I'm sentient and we're five years into the future and I have to drive around the roundabout. Yeah. What temporal scale and what kind of temporal course graining to define

the narrative necessary with narratives would be appropriate for that kind of situation and the ensuing planning. So a short answer to your question, I think there have been many, many advances. I think what they've had in common is basically transcending either different domains but in particular different scales of application. I also agree with the notion, well another I think important and pragmatic advance is something that Bert mentioned which is democratization of this technology. I think Ralph also hinted at, you know, this is the time you start using this for the common good. So I think, you know, things like RX-INFER and PiMDP, and I didn't know about the Gaia

project, but it sounds as though there's been great advances there as well. So this kind of democratization, I think, is really important. This sort of socialization where everybody can play and start to sort of not talk past each other. I think that's a very important, very important advance. And did you say the word scale invariance or scale independence? I said scale invariant. I actually said scale-free and I shouldn't have done that.

I meant I said scale-free, so the idea that you can apply exactly the same mechanics, and literally, for example, say from Bert's perspective, the same kind of message passing at different space time scales or at different levels in a hierarchical model. So I've actually got a question for Bert in terms of reactive message passing because reactive means that you don't have to prescribe the scheduling prescribe the scheduling. But in addressing the problems or the issues that entailed by having to specify the scheduling of talking

or of message passing, you're bound to deal with time. And in a scale invariant context or with nested nested scales for example you have to deal with the separation of temple scales so. I think it was gonna be very important generic question which technically but will be happy thinking about few years for the past few years i think it was in the world can i have to be addressing soon, which is how do you put the timing of your messages when you make a move or when you listen to a patient or when you actually pass a message on a factor graph? How are we going to be able to put the separation of time scales into the architecture in a way that speaks to this scale invariance? The GEAR project, for example, how do you integrate live feed from traffic flow sensors

with fluctuations in the climate. You know, these kinds of, this kind of data comes at very, very different temporal scales and yet has to be assimilated and modeled, you know, in a way that is also, I think, has to pay due courtesy to that separation of temporal scales. Bert, could you please recapitulate the question for the audience and then begin to answer it? Okay. Um, yeah, the issue is if you, if I'm an active instance, agents are nested agents and the higher levels supposed to operate on a larger temporal scale, but they, they're also working at a lower resolution. And if you, if you look far ahead, you don't care as much about precision.

Not that a centimeter level when I go around around about, I don't care at the centimeter land, but if I'm, but for the next few milliseconds, I do care about, because it may mean the difference between getting in the ditch or not. So, um, I don't want to send, so the higher level, I want to look very deep ahead, but I don't want to send every millisecond message to go to, to look, let's say, um, a minute ahead because so I need to space it out a lot, but then I may miss things. So I need to space it out a lot, but then I may miss things.

So preferably you would just send inaccurate messages, but that only works if you actually have a method to also, let's say, use less computational power to compute a less accurate message. And we're not good at that yet. I think, I mean, what we are thinking about is there's a new field, a new field, but there's a field called probabilistic numerics where we are used in math to just compute everything very precisely or as precise as possible and do not care about how much computation we spend on it. So in probabilistic numerics, I hope we can leverage this for message computations. I would like to spend, let's say, proportionally less computational power on the accuracy of a message. One way possibly would be to consider a message, a latent variable,

that has an uncertainty by itself. But I don't have a totally clear answer for Karl, because we haven't solved that either. But there is a problem in, let's say, what we do on our computers. We're so completely different from computers, let's say from the brain, that we're spending just too many computations on messages that in the end are very, very inaccurate and that's a problem in what we do on our computers currently. Yeah, so I don't know how we go. I mean, what you want to do with this on the higher level, if you want to look maybe 10

times farther ahead and spend about the same amount of computation on the lower level. That's sort of our goal. And we don't have an answer for that either at the moment. And Anna, please feel free to comment on or ask a question to anyone. Yeah, well, I mean, I don't have anything to contribute, unfortunately, to how computers work, but I can tell you that this idea of temporal scales is something that we face often in our work with patients. For example, addicted patients are very focused on short-term rewards. In fact, their ability to control how they feel in the moment, that is partially what

drives the addiction. So when I try to adopt Ural's language of minimizing surprise or minimizing free entropy, that's one of the things that people are trying to do on a short-term time to scale when they become addicted. So I have a young woman who is addicted to nitrous oxide, which has a very fast onset of intoxication over the order of seconds and a very fast offset. And she says that that's exactly what she likes about it because she's controlling it second by second. So when we work with patients to get them out of that short temporal horizon, we actually

rely more and more on action and having them change Something in their lives namely abstain from their drug of choice for long enough to kind of completely reset Their brains and to allow them to see this longer temporal horizon because when they're chasing this short control They're actually not able to see themselves in the longer narrative arc of their lives. So that's what comes to mind for me. I don't think it's going to be helpful to people who are trying to build computers, but that's the kinds of interventions that I make with humans. I have a question about that. So you mentioned control and the short term, and you've also mentioned that you study the positive effects of having a higher power in your life or surrender. Okay. I kind of gave the punchline away by saying surrendering to the higher power where I was

going was, okay, what's the association between seeing yourself in the largest timeframe and a higher power? And then also, is there something that is akin to giving up control when you look farther and farther into the future? Yeah, that's really at the heart of what I'm very interested in because it's a real paradox, right? It's this kind of locus of control within ourselves that really in modern culture, we think is a great thing. But when that's taken to an extreme, and one example of that is addictive behaviors, it's very bad for people and for communities. And so what can pull people out of that is this kind of surrender to a higher power, giving up

that locus of control, locating that locus of control outside themselves, not necessarily like in a theistic sense. One of the things that they talk about in Alcoholics Synonymous, for example, is you don't have to believe in God, it's just not you. You're not driving it. And so I would be very curious from the perspective of Ural's understanding of active inference and the free energy principle and how the brain works. Why is it that sort of embracing our inability to control what happens in our lives can actually be the very source of healing,

especially embedded in this really kind of over controlled, I would even go so far as to say, endemically narcissistic culture. Like I'm really curious. I don't want to take the conversation in a direction. Please, please. That's a fantastic question. So if anyone has a comment on that, please.

I have some thoughts. And I think this applies both at the personal level and at the global level. And I think it has to do with the, my quote from earlier by Edward Fulbrook that if you're following from a plane, yes, maybe an altimeter and some instruments might be useful, but what you really need is a parachute, right? So we have this, we have this presupposition that whatever framing we have operating in our day-to-day is going to be, okay, this is the right framing and it tends to be this rational scientific framing of very linear cause and consequence for most stuff.

And it turns out that even like if you inspect or data data behavior, you know, bigger, more complicated models are not necessarily better, which is where we get the success of heuristics under a bounded computation, bounded rationality. And if you scale it up to 8 billion humans interacting in a resource-constraining planet, you know, the world of possibilities but also of challenges, then you just, you have to, we have to drastically lower our bar for, yes, how much control we have, but also even

like how much was where does information gathering reach diminishing returns? Where does modeling reach diminishing returns? There is a whole literature on the business world about the expected value of information, how much you should actually invest. Also in science is also known as optimal experiment design, where basically, you know, acknowledging that that have a limited budget in terms of how much you can act and how much can probe and how much can, how much time you can spend thinking about stuff. And I think what we're doing when we feel like burned out or exhausted from overthinking, we're immediately feeling that, that, okay, we've gone too high and we need to give ourselves

dedication, give ourselves some free time here. I think that doing this in a principled way, we're just not just taking the heuristics and the signals that that we inherit from from evolution, but actually being able to figure out collaboratively and and with with some rigor. by 2100 in order to know that maybe it's a good idea to start doing something about the amount of carbon in the atmosphere or whatever. And so I think this leads to this idea of a real knowledge economy and of things like abstraction as a service, how can you actually build in this kind of like sophisticated translation layers that take some of this burden from ourselves as individuals and even as organizations, right? And just put it out in the world as value-added services,

which is what they are. And Guillaume, if you have any statements or questions or retorts, then please feel free. Yeah, thanks. Now I was still also thinking about the recent breakthrough post 2020 in active inference. To me, like they they were theoretical progress that were very interesting. We heard already about the formalism maturity at the mathematical level

between multiscale and scale-free aspects. I really liked also the development of a more multi-agent perspective of active inference. It's very interesting, especially like, for instance, the multi-agent systems we have just heard, like even society as a whole, as many as one system or many as many subsystems, and how we can use maybe those formalisms to also deal with policymaking. That's a very interesting venue.

The emergence of norms, culture and ideas are so important because one thing that I'm still struggling with in the case of active inference models is how to get outside the checkerboard. You can put a lot in the model a priori and how you make the model creating new stuff that is not baked inside at the first. And on that, like, one very interesting breakthrough was the application of active inference to morphogenesis. I really like the work that has been done on that.

And on the technical aspects, I think the two main focus that I love are all the deep active inference and how to scale up active inference because it's a strong limitation for the adoption of the formalism if it's not scale enough compared to other framework like deep learning. And then the link with empirical data. I really like for instance, the work of Ryan Smith and how to connect with the actual physiology and clinical data.

I think it's also something very important to anchor the theory in the real world and have like falsifiability and empirical validation of those models. And what are some of the applications of active inference to morphogenesis? So, well, I'm not the expert here, Carl Carl would be the best to answer that. I was referring to the work with Michael Levin and how a model can help to create a sort of embryogenesis. So maybe Carl, you can explain better than me.

Please. Yeah, I don't have a reputation for explaining things very clearly. But yeah, that's absolutely right. It was just work with my clever and colleagues showing that you can get quite expressive and by memetic information and movement of different cells into an organization, often described in terms of morphogenesis, simply by communicating your beliefs. So I'm coming back to this sort of cross-cutting theme of communication. So if you just broadcast your beliefs on your little cell and you have, and there are a little ensembles of cells and they all have the shared generative model that includes, if I was in this position, I would sense that.

Now they all have exactly the same generative model, the same predictions, the same expectations, and they're all broadcasting their beliefs about where they are. The free energy minimizing solution is just when they're all in a place that they receive signals that they would expect to receive when they're in this place. And of course, if that is the same for everybody, there's only one arrangement where each cell finds its place. So basically, knowing your place is an emergent property of making the world mutually predictable through communication. So a deflationary account of morphogenesis. What game i thought you're a bit of your units is precision psychiatry so i thought you're gonna talk about the sessions i'm gonna do a game now i'm gonna talk about morphogenesis i can talk about the session now.

I think there's the that's a really nice way just to pick up on themes which everybody's just mentioned, in particular the pathology of precision. By precision, you can read precision in the sense that Bert was talking about in terms of do you use an unsigned integer or a double, how much can I coarse-grained my numeric representation? Or you can use it in terms of coarse-graining in the sense of the renormalization group, it's just chunking things in hours or years as opposed to milliseconds and minutes. Or you can look at it in terms of a statistician describing the reliability or the inverse variance of a signal. And of course, we have to estimate that.

When I say we, I mean, your agents and statisticians, and act accordingly. And certainly in the work of Brian Smith on addiction, much of the mathematical explanation for these addictive locked in OCD-like phenomena rests upon a failure to get that course grading, that precision estimation right. The reason I wonder whether it'd be just worthwhile revisiting addiction and psychopathology or pathology of behavior from the point of view of getting precision wrong and certainly assigning too much precision to low-level processing, which is what Burt wants to avoid doing. It just strikes me that that kind of story also has mileage in terms of why we are in a state of paralysis when it comes to climate change. Because I also noticed, Gia, that you talked about climate action.

I never heard that before. But that seems to me to be the important thing. Why isn't there any climate action? And Anna, you know, it would be like you go into the clinic and find yourself with Parkinson's disease. Why isn't this person moving? Yeah. And the interesting thing, the computational explanation for Parkinson's disease is assigning too much precision to the evidence that you're not moving before you move. So, you know, if you don't, if you can't ignore the fact that nothing is changing or your prior beliefs, your predictions that I'm going to stand up or go to initiate So if you can't ignore the fact that nothing is changing,

or your prior beliefs, your predictions that I am going to stand up or going to initiate walking, don't get a look at it because they are immediately canceled because you've assigned too much precision to the lower level processing. And I'm just wondering whether that kind of pathology is exactly what Ralph is trying to reverse by having morals at hand, recommendations at hand, to provide a more coarse-grained view of things, a deeper view.

Anna, would you like to comment on that? Yeah. Well, I love this idea of the pathology of precision because I think it it manifests in so many different ways You're not just among my patient population, but I think it's almost like a like a cultural sickness in a way You know the ways in which we we seem like obsessed with Certain types of data and we're missing the

big picture. So I'm going to think more about that and I'm going to read more about it. I really appreciate the discussions. Interesting for me. And Anna, when you're referring to the pathology of precision, do you mean so in a more conscious sense that we're over evaluating something that we don't need to. Whereas, Carl, you're referring to it in an unconscious sense, like the brain is putting too much precision on something. Because in Parkinson's, it's not like you consciously are putting precision in a place. Well, I think you could look at it sort of like in both cases. When I think about addiction, people aren't doing it consciously.

It's that they really do see this as adaptive and healthy, and also even on some level that they can't do otherwise. And they're not able to see the true impact of their drug use on their lives. They're genuinely not able to see the negative consequences. I mean, that's, that's what contributes to getting caught in that vortex. But I mean, you could also see it as part of what's happened culturally, like, for

example, like the whole wellness industrial complex, the way that we now count ourselves, um, you know, through all these different devices. And if we could just count our breathing and count our heart rate and supplements, you know, then we would somehow reach some levitating state of precise wellness. And I don't know, I mean, you know, just kind of this is all kind of new ideas for me. That's interesting. So you believe that we can be inundated with health data and that that's detrimental to us. Oh, absolutely. I see that all the time. So for me, I used to have, okay, maybe I'll take this out of the, let me figure out how

to say this diplomatically. I used to have a device that would measure my heart rate, let's say that, and my sleep. And instead of improving my sleep, it led to me becoming obsessed with it. And then noticing, oh, I didn't sleep well. I must not be feeling good today as well because apparently there's a high connection between how you sleep quality and how well you sleep. That's exactly it. Plus added to that, the other layer that I should be able to control it, right? So with all of this data and information, I should be able to reduce free entropy or whatever, reduce surprise

as you guys talk about. I think that's obviously that only goes so far and then can actually contribute to our misery because why aren't we all levitating like the Buddha or whatever when we have all these tools and we can pay attention to all this data. I see this also with people who have productivity tools and not only that, but mechanical keyboards, let's say. The reason why I don't have a mechanical keyboard, even though I think I'd love it, is because I know that why the heck do I care about the clicking sound of a keyboard?

But if I got one, then I'd be like, well, what's care about the clicking sound of a keyboard? But if I got one then I'd be like well What's the difference between clicking sound a versus clicking sound B versus clicking sound C and I become obsessed with the trappings of productivity That is sharpening so-called sharpening the axe rather than cutting down the tree There's this phrase that is apocryphal and it said it's attributed to Lincoln Which is that he'd spend 80% of his time or half the time sharpening the knife than cutting the tree. And then this is just echoed in productivity circles. But it just, it can't be the case. Why would you spend so much time sharpening your axe?

Like look at anyone who cuts down a tree with an axe. Most of the time they're not doing that. Anyhow. So if anyone has any comments on what was just said, please. And can I just talk about what I find exciting in my field about actually sure, sure. Yes. Um, I read, it was a long time ago, but there was a paper, and it's about that it says, well, active influence is not really

a scientific loop, because it's biased. And I read that and the sound of the paper was kind of so this so it's not good. But I think, you know, active inference is maybe not a science loop, it's an engineering loop, because there's bias and we need bias in engineering, we need to make stuff, we need to build stuff, we need to have a bias. So it's an engineering design cycle. I see that everywhere around me. And active inference could be a complete breakthrough in engineering, right? The fields around me are signal processing. I am myself in a signal processing department or group and everybody builds algorithms.

For us, I mean, in ActiveInfo's agents, it's inference over states, then the floor below me, they build control systems, well, it's inference over actions. Then other people are working on machine learning, inference over parameters. Active inference could be, and you'll like this Kurt, it's a very deflationary view on engineering, because everything is just inference. And so rather than building algorithms everywhere, if we become really good at implementing energy minimization, we will be able to build a great engineering design cycles. And we'll be engineering better machines for medical procedures or other things that are important. So it has a tremendous application potential in engineering. In engineering, I think in many fields, people have sort of drifted in different directions.

Control theorists have, I mean, they do almost the same thing as signal processing people, but they speak a different language now. And, you know, so and everybody and signal processing people is completely different group from the machine learning people, but it's all information processing and this brings it to this field can bring it together. So I think it's if but the thing is that in order to to make it successful in engineering, we need to build an application that impresses, right? It's not like a tic-tac-toe thing. It really needs to impress people.

It needs to be better than some other control systems. But once we do that, I think there is tremendous application potential because there haven't been enormous breakthroughs in signal processing and control. The last big breakthrough I think was Kalman filtering and this was 1960s and I mean it's kind of funny that the essence of what we do in active influence is also also carbon silt. So that's, I think there's tremendous opportunities for what we do here for for engineering. So that's why it's getting to me. And do you mind expanding on what you said about acting and

therapists should be helping their patients with that? Oh, sure. Just so I mean, one of my critiques of mental health treatment today is that there's not enough encouragement of patients to actually go and act differently in the world as a way of gathering data, instead of it often being kind of this world building between therapists and patients, not necessarily ultimately adaptive in the world.

So I was really just kind of responding to what Rav was saying, that we need to act in the world. I think that's more true now in modern rich nations than ever before because we are so incredibly sedentary and interacting. Of course, we're interacting with a virtual world and that's, you know, good and bad. But I mean, we need to be actually acting in the world. Hmm. And so there's different forms of therapy, as you know, there's talk therapy, and then

there's also cognitive behavioral therapy or psychotherapy instead of talk therapy. But cognitive behavioral therapy, as far as I understand Focuses on the actions. Is that incorrect? I mean for you know again treating addiction like you're you're not going to really get that far with cognitive behavioral therapy or anything That's focused on just emotions and cognitions people have to go out and actually Try stop using their stopping their substance or their addictive behaviors and gather data from that experience and then come back and process it Mm-hmm. So Anna in your field and this question will go to everyone in your field and what you study Where is the largest gap that you would like to see closed?

Well, I mean we're facing a huge mental health crisis. We have more and more young people coming in with depression, anxiety, suicidality, addictions of all sorts. And these are not necessarily people who are struggling by virtue of trauma or socioeconomic disparity. These are people who have really privileged lives in many instances. So it's a, it's really a puzzle. You know, what, what is going on for people? lives in many instances. So it's really a puzzle. What is going on for people?

And I think a big part of it is the fact that people are not having embodied experiences. They're not having experiences in the world. And also the experiences they are having are these kinds of very quick fixes and fast pleasures. So I think the co-created sort of models through healthier communication that allow people to feel part of a community and also to have like truthful, co-created narratives, trying to use the language here, I think that's really important.

So for example, one of the things, remember, mentioned what he's excited about. One of the things I'm excited about in the field of addiction medicine is mutual support and the proliferation of things like Alcoholics Anonymous, but also other mutual help groups, a lot of them existing now online, and the way that people are together, creating healthier narratives and acted together to counteract a lot of the unhealthy narratives that I think are driving a lot of decision making today. I wanted to know, is there a correlation between the rise in mental health or sorry, mental illness or mental health issues, whatever we want to call it and a certain trait of people? whatever we want to call it, and a certain trait of people, like is it affecting every, is it affecting the population

the same? So the whole population is raised 20% in terms of how many mental health issues they have per year. Or is it affecting people who deal with abstractions more and more? So for instance, we're talking over Zoom and some people study abstractions just like us and then there's some people whose work it is to do something physical like running or swim. Is it affecting everyone equally? Or are you noticing that there's some broad trend? Well, the broad trends that are out there are just correlational, but the more time that people spend in the virtual world, the more likely they are to suffer from depression in society and other mental health problems. People haven't really been able to narrow that down to specific

content, but they have been able to save just the sheer amount of time that spending that you're spending online increases your risk for certain mental health, you know, poor mental health outcomes. Again, if anyone has any comments or questions, please just raise your physical hand. I can see that. And, okay, Rafael, sorry. Rafael Rodriguez I was just going to say that I think another notable trend is that, and

I just saw somebody say this on YouTube just yesterday that young people are disproportionately affected by things like climate grief, because they're the ones that are going to be alive to deal with it. And I think that applies more generally that I know Peter Senge already like 30 years or something ago wrote about the inescapable network of neutrality, the reality that what we do affects each other. And we took advantage of this huge resource buffer that's called the biosphere and earth to pretend that it didn't for quite a long time

and got a lot of mileage out of it. But now we're at a point where there's a whole generation of people that are coming to grips with the fact that I'm gonna stop myself from saying a swear word, but oh my God, we actually need to change everything about everything that we do, and we need to do it fast. And by the way, it's not just what we do in, it's not just what we do out there,

it's also what we do inside, how we get ready for how to show up for life internally. So no wonder that impacts myself, I've dealt with anxiety and a lot of other things, uh, and a lot of, of, of other things. We've had conversations about what are we doing, bringing a daughter into this world and all these kinds of things. And I think it's, uh, it's only natural that, that it's, it's coming to a head in this way right now. Guillaume, you have your hand up and I can't see you. Yes.

Um, yeah, it's connected with, uh, what has just been said. Um, so, and your initial question about the gaps that needs to be closed. I think like, uh, the scale free model of health and mental health particularly would be great. Like, uh, uh, we are in silos in biomedical research and the fact that someone is having depression can come from interacting genes as much as interacting people and also is related to climate change and so on. So how we can have a new health systems that doesn't deal with those silos and integrate

those different scales. To me, it's like really a big challenge, but a challenge that current models and work that we can see go in that direction. And I'm very enthusiastic about that. Bert? And Bert? Yeah, Dean. In my field in engineering, active entrance is not understood. Because almost all papers are written by neuroscientists and they're hard to read. So I was really happy to hear today that I think it's Sanjeev Namjoshi who is writing an engineering book on active inference. So that will I think it will really

help that together with the availability of good toolboxes for implementing active inference should make a lot of engineers much more enthusiastic about active inference. Because that's, it's not something that is, it's not understood at the moment in engineering circles. So I hope that the book will be good. I'm enthusiastic about that. And Carl, where are some gaps in the research that you'd like to see addressed? Um, all the gaps is a whole empty space out there yet to be explored. But in terms of in terms of what seems to be

emerging from the session and specifically the past few answers, it does seem to be important to have this very generic, just to take Bert's sort of line that this is just one deflationary simple and probably the right way to understand stuff and to make recommendations or to describe people's actions possibly to themselves in a therapeutic context. And as such, it should be push button technology and it should be democratized and socialized and

I think that's the challenge practically and one may ask why would you want to do that. For me there are two clear imperatives, one is very abstract and it's not really my within my comfort zone and the other one is in my comfort zone. The other one is outside my comfort zone. This notion of interactivity and hyper-connectivity and the meta-crisis that we heard about. And, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and,

and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and,

and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, and, contrast with growth is good versus sustainability. And of course, the maths of the free energy principle is just about sustainability. It's just a description of the physics of systems, random dynamical systems that self-organize to some non-equilibrium steady state. That is what we are.

So for me, there's something deeply, if you like, apt about the free energy principle and its core researches, active inference in application to ecosystems and lived ecosystems and realized ecosystems. So if those basic principles can be brought back into globalization, back into globalization, into the market, into fintech, into social media, into politics, into climate action, I think that would be a good thing. I'm just mindful, this struck me in a number of the presentations today. If you remember before, Bert was saying, if you look at the brain, which is a really lovely example of a self-organizing system to a non-equilibrium steady state, then it's empty. And what did he mean by that?

It didn't mean you're empty-headed. What he meant was it's incredibly sparsely connected. Now that tells you immediately that a pathology of connectivity is hyperconnective over connectivity, which immediately, well, it made me very alert to the presentation of the Meta Crisis. One of the first three things that was underwrote, the Meta Crisis or the current crisis we're contending with, is a destruction of that sparse, delicate connectivity that defines thingness and defines ensembles of things technically in terms of market blankets.

So if we want a world in which lots of different things can coexist in some kind of generalized synchrony in a sustainable way, you need sparse connectivity. And the pathology, the thing that will destroy that is over-connectivity. So it seems to be very important that we get that into play in terms of machine learning, artificial intelligence, politics, fintech, climate change. And the only way it's going to get there is epistemically by equipping people to actually build their own little models and ask their own questions. You can't tell people this. They're going to learn. They're going to learn it for themselves. Just very quickly, because I'm sure we've only got a couple of minutes left. The other agenda which I'm more familiar with is exactly Anna's and Guillaume's agenda which is

making this work in the context of neurology and psychiatry. So if you can democratize and socialize this way of describing things so that people can now build models of their particular patient in the other use of precision psychiatry. I suspect the games unit was called after. So to personalise medicine that is really personalised in the sense that you actually have your digital twin of your behaviour. And then you've got that, you optimise your digital twin to become a model of your patient. And then you can start to do experiments on that model, behavioral interventions, or even share that model very much in the spirit of CBT with the patient and say, look, this is you, this is what would happen if you went out and did this, and this is what would happen

if you went out and did that. That to my mind, and indeed that was the initial motivation for much of this work was actually to build observation models of psychiatric conditions to work out both the pharmacological and physiological basis and the disruption of the pathology of precision and message passing on the factor graphs that are on our brain, even though they are very empty on the one hand, but also get that behavior, that key thing that Hannah was talking about, that active engagement with the lived world into that model and hence active inference. And just to conclude, you know, that activity, that sort of physical engagement, that embodiment,

that sort of foresees and everything else, I think it's really coming to a head now in terms of people's, you know, after the large language model, after the chat GPT moment, the bounce back has been what's missing, what's not there, and of course what is not there is agency and embodied engagement with the world. And that's why I think there's still a lot of work

to be done in bringing artificial intelligence read as active inference to, in a way that matters to people who, you people who can make a difference, which is basically everybody, but specifically politicians and doctors and the engineers and the like. So you all now have 30 seconds to 60 seconds to speak directly to the audience. What closing message do you have? You're speaking directly to someone who's listening, they're a curious person,

they're interested in active inference, they also wanna lead better lives, hopefully, and do something propitious. So what message do you have for them? Anna, we'll start with you. Gosh, I'm just gonna say what pops into my mind right now is that one of the things I have learned from my patients who are trying to get into recovery from severe addictions is something that they call the set-aside prayer, where they set aside all of the notions

that they have about how the world works and try to be completely open and receptive to information coming into their minds. And I think that's just a wonderful frame or concept for all of us living in the world to periodically just take a moment and take these models and just say, you know, everything I think I know about the world, I'm going to temporarily suspend it and I'm just going to be open. And when that happens, we can be present and learn in a way that it's not possible when we're just trying to reinforce our models.

Great. Fantastic. And Bert, and then we'll go Carl and then Raph and then Guillaume. Well, I just enjoyed today very much. I thought there was a lot of material, both for people from let's say psychology, neuroscience, but also for engineers. So if you haven't watched some of the talks,

go look through the schedule because some of the talks were really good. And so are really enjoyed and then yeah but should I tell people go work out do a lot of sports it's good for you Carl yeah sorry I was going to make a joke second just to thank Daniel and his team for this. So if you want something to do, you should go and watch the live streams and get involved with this ecosystem. I hadn't seen that paper being presented before but I was really impressed with you know with the sort of the Active Inference Institute and its openness and its welcoming attitude and

vision like the Smithsonian. So if you want to pursue these ideas get involved and if you haven't got time just make sure you attend next year's Active Inference Institute celebration. Thank you, Daniel. Rafael? Yeah, so I'll second what Carl said and follow on with it's an invitation not just to participate in the Active Inference Institute, but also an invitation to participate in building this Gaia tractor, this new way of doing things that acknowledges the value of growth and also the value

of sustainability joins it all together in this thing called regeneration. And it really is And it really is a collective effort, collective learning effort. So, and this also by the way also applies to the panelists as well. I think obviously what Burt and Carl are doing, it has immediate things having to have to do with what we're after. But one of the main things that we keep discussing

is also like this, the intersubjectivity and the importance of being able to operate well as humans together. And that connects directly to cognitive science, psychiatry, and so on. And yeah, so everybody that wants to be engaged and be a part of building a better world should be thinking about what am I doing as an individual or as an employee of an organization or as

a researcher or as a leader or the family member, what am I doing? How does it contribute to this new non-equilibrium set of state? So yeah, that's kind of it. I probably blew through the 60 seconds, but here it is. And Guillaume? I would thanks also all of you for the discussion and the organizer for what they are doing. Indeed, the work of the Actual Inference Institute is very

laudable and interesting from an open science perspective. They are really embodying that. So big kudos to them. And well, Berthold Fritz was saying like to do sport is good for you. I'm not very good at sports, but some say that science is a team sport. So at least have a good team perspective when doing science and being kind to each other would be the best advice to everyone.

Well, thank you all. I also would like- You get yours too. Somebody else has to come in from outside the Markov blanket though. Well, I wanted to just thank you, Daniel. Thank you, Daniel and Rafael. Well, and also Carl and Anna and Bert and Guillaume. This was a tremendous amount of fun. I hope I get to speak to you all individually. As usual, I have way more questions than we were able to get to.

Thank you all. Thanks, Eric. Thank you. Thanks, Eric. That was exciting. Thank you. Thanks, everyone. Firstly, thank you for watching. Thank you for listening. There's now a website, curtjymongle.org, and that has a mailing list.

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