nal interest. These are challenges of global emergency as well. These challenges are educational in nature, for example, related to scientific literacy and workforce development and just general professionalization and changing times. On the research side, there are some priorities and challenge areas related to the fundamentals of cognitive science and grounding the cognitive science in physics, getting that integrative first principles framework that we expect and prefer. About information science and diverse intelligences, things are faster and different than they have been and active inference is perfectly poised for sense making and decision making in that space. User experience, accessibility and socio-technical design. This is a major open question across different settings. So it's not uniquely held to the Institute. However, we really do believe and really will commit to exploring new kinds of ergonomics and onboardings and experience so that we can come through and be how we want to be from the first time somebody enters the game where we can highlight accessibility, meeting people in the language that they prefer to listen to and speak at the language, in computer language or at the math level that reflects somebody's preference in that moment as well as being able to use augmented and synthetic intelligence systems to ramp up and even push the frontier of what augmented people and other kinds of collective intelligence systems can do. Cyber and cognitive security. These also are broader topics. We've worked on these topics from an active inference perspective many have and we think that this is gonna become increasingly relevant as things do become more and more challenging in certain ways out there. Scaling the active inference ecosystem. We've heard about this from the narrow and technical side. We had estimates of the computational complexity of sophisticated inference about how many computer operations have to happen for a given calculation to play out and that's sometimes called scaling active inference. Like, okay, it took one minute on one gigabyte of data. So how many minutes is it gonna take on this computer with this much data? Scaling the active inference ecosystem though is a broader question and that's where we come to more of the social fabric and the real sense of belonging that we all want to have and co-create and provide for others in the ecosystem. And so this kind of scaling is really transdisciplinary. It's really skin in the game. It's as real as it gets. And applying active inference, some applications we've heard from today, some applications are cited in the early pages of this paper and more and more come through every day and we support at the Institute various applications of active inference. Dean, what can you add in there? Okay, my mute is off, good. So here's something that never came up in the writing of the document but I think it's kind of interesting and I'm curious what you guys have to think about this. 175 years ago, an institution came informed together is called Smithsonian Institute. Now, I don't know if 175 years from this conversation today, there'll be the equivalent of whatever the active inference institute is, 175 years forward. But I think it's okay to start thinking in those terms. It doesn't mean that we follow in their footsteps necessarily, but I think it means that we have a long-term view and that the things that we write about ourselves now have some shelf life, have some real sense of what we can't, there are things that we can and we cannot change. But if you look at the front page, the landing page of the Smithsonian Institute, the first thing that they say is welcome, which is what we say about the active inference institute. And then they say the Smithsonian Institution is the world's largest museum, education and research complex. We are a community of learning and an opener of doors. Join us on a voyage of discovery. So in three sentences, they kind of sum themselves up. What's the formula for not just existing for 175 years, but being a force? And I think, again, Daniel, whatever you decide to highlight in the document today, what I think we're really trying to do is say, do the words that we put out now, will they continue to resonate with the people for decades to come? And again, I don't wanna sound all hand wavy, but I wanna hold up an example of a place that really did set itself up to be something that people could return to again and again and again. And I don't think there's anything wrong with aspiring to having that kind of place, that kind of respect. And I think that the things that you're pointing to right now, hopefully set the, we're preparing properly. So that's what I'm taking away at this point. We know about that preparation measurement cycle from Chris Fields, don't we? We go on, talk a little. Oh yeah, please Pablo, go ahead. No, no, definitely. I completely agree. I've been thinking about that for a long time. You want to build and act for the best of the future. And I think to reflect that on the paper, it's super important. So yeah, definitely up to do that. Yeah. Way ahead, we describe some paths that we want to take and the history of the Institute. We describe our origins, but I actually wanted to come to figure three and four on page 11 to speak to a building. One of the most amazing and encouraging things has been the all ages, all time zone, active build, contribute mindset. An earlier version of myself, I might have thought like, hmm, people who do variational Bayesian methods and machine learning, they're going to have no issue on boarding to active inference. It's just going to be like as simple as saying, yeah, it's a variational Bayesian model with action. So it's just signal processing on the inbound that's just control theory on the outbound. But if you're familiar with variational Bayes and variational free energy, this isn't changing it that much. Oh yeah, we use a particular partition and it's related to the free energy principle, but don't worry too much, you'll get there. I thought that that would be the path for so, so many and future analyses will reveal what they do or don't reveal, but what we found in our enacted work as it really played out that there was inclusion and unique contributions to conversations ranging from philosophy of science to fundamentals of statistics and meta science and the role of quantitative studies and social systems, all these amazing topics and people who just scampered so rapidly through the prerequisite, what could be seen as prerequisite from a disciplinary perspective was facilitated by the recognition that one was already in the space and in the way that they needed to be to be an active inference. And that was not written out before and even now the traces are barely recorded, but it will become to be understood more fully. And to your point, Daniel, I think especially what the pie chart shows is the actual spectrum aspect of this. So I know that right now that maybe the neuroscience and the psychology piece take up a bigger portion, but I think one of the things that we're trying to promote is the idea that you can come from just about any background as opposed to, oh, all paths lead to, like as you said, an earlier version of you said, oh, okay, well, I can see where everything is gonna, where the confluence is going to. No, I think what this demonstrates is that it's actually becoming more distributed the longer we continue to exist. Yeah, this figure, of course, I'm super happy with everything that we were able to do, but this figure is like almost a little bit like a a paper target that we're just going to blow out later because it was a fraction of only people who had formal affiliations listed, which means only people for whom we had done full literature-based polling of their information. So it doesn't even represent the actual participation profile. This is more like the invitation list for live streams. So we are gonna see another long, a long tail that covers the part that's not the long tail, I believe. The rest of the paper continues along, possibilities, challenges, next steps, more about the institute organization, various other pieces of information that potentially eager stakeholders or participants or donors or liaisons or other people would like to learn more about. It's there to engage with. We want people to hop into our Discord and continue the conversation or email us or otherwise get involved. It's why we called this symposium in acting ecosystems of shared intelligence at the, of course, relevant suggestion of Carl Friston because we really do wanna enact it. So if you feel like you're on the sideline, waiting to jump in or you're on the beach and people are out there surfing the 100-foot wave, then maybe it's time to get more involved.  Well, just real quick. Again, if you want a consolidated document that speaks to the now, but it also points to a direction that we, none of us really know is certain, but it's also really exciting. Have a look at the doc. Pablo? Yeah, I'm here to make sure that I act as one of those people of all backgrounds and perspectives that has taken advantage through one year of learning and acting on this institute. And hopefully you see you in one hour. So thank you and have fun. Thanks, awesome. Thank you, thank you, Dean. Thank you, Pablo. Welcome, Raph. Also, come on through on the paper. So, how are you doing? I'm good. What about you? Pretty good. Other than this brief interval of white light, I'm just hanging out. Fantastic. So I'm trying to figure out here how I can, I'm using a different slide software so I need to make sure that I can pull this up in the right way. So give me just a quick second. Yeah. Great. Cool. Just to read off what's coming while you figure that out. So right now we have Raph Kaufman and this presentation is gonna be GAIA, an active powered network for planetary scale sense making. Following this 30 minute presentation we'll have Evelle Guinean-Carlou on embedded normativity. That looks perfect. That's perfect. Awesome. All right. Thanks Raph to you. Thank you. All right. I was gonna try to see if I could blow this up to full screen, but. I got it. I got it on the stream. Oh, looks good on the stream. Go for it. Cool. So I'm just pulling up my notes here. All right. So let me just test that this moves. Did I just move the slide? Great. Cool. All right. So as they said, hi everyone. It's a huge pleasure to be here and thank you so much to Daniel and everybody else at the Institute for organizing this. I'm going to talk about what I think is the single most important application of active entrance, not as a detriment to the others, but it's sort of upstream of everything else. And that is how do the 8 billion of us humans make sense of what's going on in our planet and all scales? And how do we use that understanding to survive as a species? So this presentation here is a bit of a technical presentation and it has a lot of content. It's going to move pretty fast and assume that you're familiar with some concepts, but I'm going to try to leave time for Q and A at the end. So first of all, I'm going to talk about the motivation behind what we're doing in particular the concepts of the medic crisis and the third attractor. Then I'll give an overview of our network architecture and the application so far and cover the many remaining challenges. And last but not least, I plan to convince you that this is the single most important thing you could ever be working on and I'm doing this because we need your help. So that's a taster of what you have. And so as I said, let's jump right into it with the motivation and what greater motivation could there be? If you haven't heard of the medic crisis, it's a term that it's originally from Dr. Who, but it was borrowed by a thinker called Daniel Schmackenberger to talk about the total risk for humanity and the biosphere that is posed by the combination of three factors. The first being the increasing interconnectedness of everything from climate to food security to national security to biodiversity and everything in between. The second being the risks associated with self-evolving technology including runaway artificial intelligence. And the third being the increasing omnipresence of coordination failures personified as the God of coordination failures, MOLOC. And so the combination of these three factors as hypothesized by Schmackenberger will lead to either one of two attractors, one of them being chaotic breakdown of all structure and the second being a reversal to oppressive authoritarian control. And obviously both of those qualify as bad in my book, I assume in yours as well. So the question of course is, can we actually find a third attractor that is positive sum as opposed to negative sum? And rather the question is, can we design it? So what I claim and what we as the God of consortium claim is that yes, we can and we can design this attractor and it's the design goal is basically to build resilience and the stabilization into our bio-social economic system. And to do this subject to multi-level system constraints like the planetary boundaries, the desirability, meaning it has to be something that people actually want so they will help it happen. So it needs to preserve standards of living and so on and it needs to be feasible, it needs to be achievable from our current initial conditions. And of course there's a lot of design complicators in the mix, some of which partial observation, there's lots of information asymmetry going on. There's a lot of uncertainty about how the pieces of the world system work from a scientific perspective. There's obvious computability constraints associated to this. And last but not least, you can't really control what happens in most cases, we can only create incentives, recommendations and nudges. So easy, right? Of course it's not easy, but we think we can do it if we apply the sum of the principles from cybernetics that we already know and apply them in a decentralized positive sum composable way. So what we're talking about here is a decentralized network a decentralized hybrid AI human network for planetary scale decision support and automation. It features, it recognizes that we will have human and artificial agents interacting by the billions in an open network. Its goal is to facilitate both confidence or model alignment and coherence or goal alignment at multiple level of scales up to the global scale, meaning the survival of the system as a whole. It will need to feature built in incentives and governance and it will need to be privacy preserving intrinsically. So the below listed participants are participants, founders of the consortium I'm gonna talk about it later. So this is the framing, this is the motivation for what we're doing and now I'm gonna give a whirlwind tour of the architecture so far and I'm gonna cover mostly what we've built the version of this architecture that we've built so far thinking about this explicitly and then I'm gonna talk about how this connects to other architectures that already exist in the world just very briefly. So we've been framing this as an architecture for building decentralized digital twins which are local models of real world systems that run in the network. And the role that such digital twins play is that they help us understand the costs and benefits of strategies and projects that happen in the real world. And we, in our organization, Digital Gaia we started out caring primarily about agro-ecology. So how do you actually make better decisions meaning better recommendations, negotiations, valuations, investments into a farm, into a forest, into a bio landscape to make it better, to make it more likely to survive and thrive. So as a practical implementation it needs to tackle several real world challenges. First of all, it needs to handle distributed non-IID data, data is sparse, it's heterogeneous, it's private, it's potentially unreliable. It needs to enable localized, collaborative forecasting and planning, personalized planning and privacy preserving. It needs to accept the fact that it won't be able to directly control but only recommend it as we discussed. And the outline of what we came up with is this concept of the Gaia Network which is a mesh of AI agents called natural entities or ENDS for TORC that act as proxies for real world systems. Each ENDS runs on an engine called Penguin which it uses to inquire, learn, plan and allocate resources. And ENDS communicate in a language called the Gaia Protocol. They use this language to independently interact with their environments and each other. And if we do this right, I'll show a bit more on this later but if we do this right, the Gaia Network's behavior approximates a single composite agent that handles, that interacts with and couples with its global environment. Cool, so just to give you an overview of how this works behind under the hood, the Penguin engine is really the heart of our architecture. And the core loop that is mentioned here is a typical active inference loop but to make this work in very heterogeneous and distributed context, there's lots of work involved. So just some highlights that I think are gonna be particularly interesting for this crowd. First of all, so as I said, the same core engine needs to be able to work for very heterogeneous context. So each agent has access to a library of generative models. And these models are specified using a Python based probabilistic programming DSL and they declare what context they are appropriate for using a shared ontology. Using that ontology, Fangorn can then select the most appropriate model to use online given the configurations like project configurations and the kinds of observational data available. So using the model that's appropriate for a form or for a forest, what kind of form or kind of forest, what kind of ecosystem, what kind of, you know, their context energy project. I'm not gonna get into the examples right now because there's more on the applications later on. So we end up with a network of heterogeneous agents, but that's okay. They actually form a single model in a formal sense because they're all connected hierarchically by a system of hyper priors. And this means that Fangorn needs to support inference of generic hierarchical generative models. And we actually do this automatically using approximate posteriors with short throw constraints. And so we have this global model that connects all the nodes and describes general facts that apply over multiple contexts. And this means the whole network needs to jointly perform inference over this global model using a peer-to-peer or federated algorithm. So initially, this way this works initially, the network sets the global posterior to be the same as the global prior. Each node then independently performs local optimization of the variation of free energy and then passes the parameters of the new global solution to the peers in the network where they use it as a hyper prior. And this works over a broad range of topologies and other pretty general constraints. And by the way, I'd like to mention that there's a lot of overlap and interaction between the stuff that I'm talking about here and some of the other presentations that you're seeing today. And I wasn't able to reference them explicitly in this talk, but I'm sure you guys are gonna see the connections. Cool, so what it turns out is that the shared parameters that represent our global model are effectively stored by the network, stored in a federated parameter store, meaning that each node stores their own local parameters, their private stuff about their own local context and multiple nodes stored copies of the global parameters. And this architecture, because it supports heterogeneous local models, it actually allows us to introduce another kind of nodes which we call antmoot nodes. These are nodes that aggregate scientific and empirical information using a meta-analysis model. And we actually are able to use that meta-analysis model to constrain our project-level models, or ants, using quote-unquote imported knowledge that wasn't directly observed by them, which is left to right in this picture. But this actually also goes two ways. So empirical findings from the projects, what actually works on the ground for this particular project also informs the antmoots posterior, which is right to left. And so we actually have a complete cycle of science and empirics all implemented using hierarchical active inference. So finally, just among the global parameters that network learns by federated inference are the precision associated with data coming from different data providers. And this gives each node a way to consume data without trusting ground truth and basically to learn together which sources they can rely on for what. This is just some of the more core features we're working on integrating many other capabilities into this architecture. Not gonna go through all of this, but basically what we're finding is that we have all the building blocks to turn this into a full-featured decision network complete with its own internal knowledge economy, meaning that contributors' contributions get attributed and rewarded in proportion to the free energy reduction that they afford. Cool, so jumping into applications, as I mentioned, our main focus as an organization has been on modeling agroecological projects like regenerative agriculture and agroforestry. And this exercise is all the constraints that we've talked about so far. So again, I'm not gonna go into full detail, but just to give an overview of what is the anatomy of generic model for a farm or forest. So we started at the bottom right with observables that are things that we can measure about that ecosystem, about that local context, like the plant count and size, vegetation indices from satellite data, and so on. These are linked to latent states which form a nonlinear dynamical system that's parameterized by the policy, the things that modulate the latent states, like agricultural interventions and practices. You can think of the timing of planting and harvesting, what kinds of crops or trees get planted, what products like pesticides get applied or not anymore. As well as covariates, so external events like weather, physical risks, wildfire, drought, and so on. And so there's some scientific and measurement parameters that go into this as well, things like the effect sizes of the various actions and covariates. And these local parameters are conditionally dependent on a set of global parameters, which as we discussed, they link information between different ends and different sources of knowledge. And so these same global parameters are used in the meta-analysis model and they actually establish the link between what is found to happen on the ground and what is reported in the literature. And so I'm not gonna go into this because I think I'm gonna run a bit long, but basically the concept is that we have these project nodes that correspond to many farms and they're each on independent nodes in the network. They're also connected to this common meta-analysis model and together all of these nodes, they incorporate academic studies, expert knowledge, and locally relevant context. They do so privately, so only processing the local data locally and through this parameter sharing, they actually jointly and iteratively estimate the global posterior in a cycle forever for as long as there is new data about the project, as long as there's new studies and new expert knowledge. So all of this is happening basically in an online way. And again, the topology can vary but the principles remain the same. So just to show a little bit about how this works, this is just two epochs of the global posteriors. And this was just a location and scale for two parameters, the slope and the intercept of the relationship between vegetation index and the tree biomass in one given hectare of forest. And as you can see this converges pretty quickly. It's comparable to local only methods. And just to highlight how this can be used to, also the limits of how this can be used to estimate source reliability. Here's a really successful example at the left. This is pretty straightforward to use this to estimate reliability of satellite data. And for other cases like very local, very context specific data, you actually need more data or more data from more independent sources that we had in this demo here. Another very useful use case of this is as we discussed, we don't have direct control over what happens at a farm. We can only recommend and nudge. Often we also like we get, there's a node gets stood up, a net gets stood up for an existing project that has already happened. And we wanna find out what actions we're taking in the past. And the same generative model can be used to estimate what happened in the past and the exact example it infers when planting started and when harvest started for different fields. So bringing it all together, one of the applications that we've been using, we've been working on, it actually uses all this machinery together to validate claims about projects performance and to estimate future performance. We've deployed this in real world projects. This one, the examples here in the scene this photo are by relevant to agroforest reforms in Colombia. And we found that this actually helps the project developers and their funders to come to a shared understanding of how successful the project is, how much it's worth in terms of its impact and its fundability, whether it should be scaled up or needs to change course, what is the quality of the strategy versus other potential strategies for natural regeneration and so on. So it's actually useful on the ground for decision making in this context. Cool. So finally, I wanna talk about the end state and going back to our original motivation of solving everything, I forgive you for thinking this is nice, but hey, we've been kind of in this downward spiral towards the meta crisis for a long time and what you're telling me doesn't even come close to solving everything. And you're right, there's a lot of moving pieces that need to be built and connected, but here's the good news, we can do this. There's no mystery here and we don't need perfection. I love this quote from Edward Folbrock that says that to define a policy, we don't need exact empirical measures or optimality. If one jumps from an airplane, it may be nice to have an altimeter, but what we really need is a parachute. So there is only so much that we can get in terms of dimensioning returns, what we should be refocusing on is creating and refining solutions everywhere and connecting them everywhere. And you might be thinking about how does this actually scale to global goals and how does this actually get used for scalably building into the fabric of decisions in our society. So about scaling to global goals, I again won't be able to get into the details, but maybe Connor mentioned this in this presentation earlier. We have actually some proof of concepts that we can actually build active agents that are made of active agents. So we've shown in this paper here that a collective of interacting active agents is able to perform approximate inference at the ensemble level. And that this further that this happens through specific mechanisms that are mediated by cognitive capabilities like theory of mind and goal alignment. They work together to improve collective performance by eliminating ambiguity and through actively exploiting diversity. So that's one. And the other question is how do we actually build this kind of stuff into not just these ad hoc decisions, but into really the fabric of decision-making and that is our global economic system. So talking about here work that I did with Casper Hesk, this is a game that highlights the ability of, and it's just like the ones that we just described to infer through the system state and use it to credibly incentivize long-term positive outcomes in an externality-happy scenario. This scheme, we were able to show that it works even with adversarial bot strategies and even with high uncertainty and the intentional misinformation. So you can actually get these ends to work with budgets and incentives to drive decision-making that internalizes externalities and compensates for occlusion and generates long-term positive outcomes. That's not all. There are other solutions that have been around for longer than hours that apply similar principles to global decision-making problems in the real world at scale. This is one case study from our partners at Cognizance that they developed for the COVID pandemic. And this is not using active and formalism. This is a combination of traditional recurrent neural networks for prediction and evolutionary optimization for prescription. And it's actually able to achieve some pretty amazing stuff. It discovers a Pareto front between prevention of cases and economic costs. It's also a showcase for hybrid human AI intelligence. They did an initial version of this model. Then they launched an X-Prize. They got a bunch of contributions for other models. And then they did a meta model that discovered a better Pareto front. And even better, this tool has actually been used to advise policy in the real world. So it's actually very exciting that we're working with them and connecting the dots because we need to connect the dots. We need to bring it all together. This illustration here is just, I think a subset of the effects of the climate change crisis, which is just one aspect of the meta crisis on humans, on our society and the economy. And so we actually need to be able to deploy solutions that work across many different domains and to connect these dots. This means two things. The first is interoperability, aligning decisions across context so that we don't get unintended consequences. And second is reusability. So being able to transport and transfer learning and structure across context. So for instance, using that evolutionary approach from the COVID example in our agricultural context or in many other possible contexts. And this takes a form of libraries of components, APIs and so forth. So that's what we're working on. So we're launching this consortium. We just put up the website a few minutes ago and we have the goal of having a minimal but functionally complete implementation by the end of next year. And this is an open project. We are working on this at our organization called Digital Gaia, but we're also launching this open consortium that welcomes contributors from anywhere, both active entrance experts and non-experts. And we need a lot of contributors. We need all of you because this is the most important thing that we could ever be working on together. And we need your help to tackle the remaining challenges. So if you wanna help us build the planetary brain, please go to gaiaconsortion.org and or reach out to me, you know where to find me. And together we can design, build and learn what it takes to achieve planetary regeneration. Thank you. Well, Raph, thanks for the great presentation. I'll post the link to Gaia Consortium into the YouTube live chat, but let me get a few quick questions in. So here we go. First question. This is from Marco Lin. I agree with the critical importance of such a project. How will you deal with the challenges of scaling given the dependence on real-world entities like humans? For dependents on real-world entities like humans for major metacrisis domains such as geopolitics, sociocultural fragmentation and other particularly hard sense-making domains. First of all, hi, Marco. Yeah, thanks for the question. And I 100% agree that I'm not claiming that this is sufficient. I'm just claiming that it's necessary. We do have a lot of energy that, a lot of ideas and a lot of energy that we wanna put into exactly what you described, how to weave what we're doing into the existing incentive landscapes, whether they're market incentives or policy incentives, but ultimately this needs to be an enabler, this needs to meet people where they are and that we need to be cognizant that change is not gonna happen overnight, at least not right now, right? Typically these things take a while to mature and then they snap into place. And we're already finding some of this happening in some of the domains that we're working on where nothing happened for a very long time and then bam, hockey stick. And we believe that the same can happen in pretty much anywhere. So we're very optimistic. Great. What are currently the greatest challenges for Gaia as an organization? I love that kind of ambiguous question, like is that the Gaia consortium as an organization or is it Gaia, but let's hear both. What are the greatest challenges or frictions for the consortium and for the bigger picture too? Also, I'm not gonna talk about the technical stuff for a change, I'm gonna talk about the more meta stuff. It's actually very related to what your previous presentation, Daniel, about the paper about the institute talked about. And it's really related to building out this common, what we were calling the common cognitive kernel for sense-making and decision-making. And relatedly, breaking through this culture of sort of suspicion and third force and getting people trying to stake their claim to something. So we are and always have been extremely open, but it's kind of hard anyway to build trust. So we are aware that it's going to take a while to build this trust with everybody. And I think the only answer to that is we need to be as participatory and transparent as possible. Also being humble and knowing where we haven't, we don't have the answer and asking people to help us. Awesome. Any closing words or thoughts? Well, if there are no further questions, again, I thank you all for paying attention and I'm going to be reaching out a lot more. You're going to hear a lot more about this consortium. As I said, it was just form. Did you just put up a website in time for this presentation? And by the way, thanks so much to Mahi and of course, Steve and everybody else who's been part of forming and getting this ball to start rolling. We are very early stage, very formative and still very client. So we definitely are, I just gave you my perspective on a bunch of these things, the shape of it that has come from all work on this so far and even not all of it, just a piece that we could cover in 20 minutes or so. But we are very plural, we want to engage with you on different paths forward, different opportunities to build things, to experiments, to connect the dots, to learn together. And ultimately, we really just want to provide a platform a convening space, a protocol in the classic sense of the word of a shared language, a shared means of communication, a shared means of understanding to find together what we need to do to actually build this third attractor. Awesome. Call Gaia Trimtab. Thank you. Exactly. Thanks so much, Daniel and see you guys soon. Bye. Bye. All right, what a cool presentation. Next up, welcome Avail. We have Avail, Gwenen, Kalu with embedded normativity. So thanks, Avail, to you. Hello, can you hear me? Yep. Good. And can you see the slides? I only see the top left of the slide. You only see the top left? Yeah, just, yeah, try again, thank you. Okay, what now? Yep, looks great, thank you. Okay, good. So I am Avail, Gwenen, Kalu. I'm interested in a multi-scale collective organization. This is why I came to Active Inference to begin with. And the core question I've been asking is, basically when we look at human organization, there is creative evolution, there is a challenge through time of the specific patterns that are enacted. And this is very hard to account for. And I have been trying to pin down more and more specifically what exactly is Gwenen and how to model it. So this specific work is a work I wrote in the context of what my PhD, of course, which is funded by the XCAPE project. A project in cognitive archeology that's interesting, interested, sorry, in how the archeological landscape, shape, cognition, and vice versa. The title of it is Anomated Innovativity, an Active Inference of Agency, an Active Inference Account of Agency BM Flesh, because you will see very quickly why. So first, we will go through the existing work on encultured cognition in the context of Active Inference. Then we'll try to expose the concept of embedding activity, which we introduced here, and what it means. And then we'll look at the more semiotic question around the externalization and utilization of normativity in cultural landscapes. So first, encultured cognition in Active Inference. So we know Active Inference, I will go quickly over it. I still need to do it because there are onlookers that are not familiar. So Active Inference say that agents continuously predict the specific cues, sensory cues they will see, and they predict the action or policy they will not take. And they basically enact an ongoing stream of expectation that they reconstruct given a calling signal. So this is best I could inform in this schema which shows the agent as the medical system which engage through environments, through action and cessations. And all of these specific domains, they minimize specific function that is called free energy and whose minimization basically means there is a proactive process going on. And a core property of Active Inference is that it dissolves agency as we typically understand it. We don't have agents that have like internal monologue that drives them to take decision. We have a conscious anticipation of what's going on and whatever an agent anticipate itself to do, it will do. And so this goes for computationally satisfying but very deflationary notion of what is going on with agency. So the question then comes, what is the relation between an agent and the cognitive landscape in which it evolves? I will present very rapidly again the framework of skill and agility pioneered by Brunberg, Kiewoszstein and Riedwilts and which says that skill agents which we understand as agents that understand what is going on around them, they will basically experience the world as the landscape of afferances which is opportunities for actions. And they will, their cognition pretty much reduces to the experience of this landscape and adaptive engagement with it which basically maximize the grip of the agent as on the system. And essentially for reasons we will not go over because it's math, the very existence, experience of this landscape, it is equivalent to the existence of constraints of our attention, the attention that the agent gives to thing which translates the embodied expectation that are two time embodied, but the money expectation that the agent enacts. And so this becomes very interesting when we cross it with the existence of enculturation in humans because the basic experience we have of afferences of what we can do, it is shapes, culturalities shaped by interaction wherein we learn with other humans about what we do, what we can do and what we can do. And this means that the basic human experience, it's thoroughly encultured, it's embedded in what one should call the cultural landscape, landscape of cultural affondances and it is embedded in cultural and material regimes of attention. So why is to pay attention to things? And this is a figure that comes, I believe from this year on the thinking throughout the minds where it shows basically all the action and station cycle when we have several agents, they build a common environments that has epistemic cues on how the world is like and when we are encultured to pay attention to things in a certain manner that makes it so we perceive and enact this landscape. And so the encultured constraint of all the flow of attention because of what we saw just before the deflation of our account of agency, it means that the very expanse of the landscape, cultural landscape that is collectively enacted, it effectively integrates intentionality and it effectively integrates normativity in a way that clashes strongly with classical account of both. So given this state of the art, let us look at what I mean specifically by embedded normativity. So embedded normativity is a kind of normativity, I think you have got it. So normativity, it means basically the criterions by which we judge things to be good and bad. In the context of biological system, they will look for specific, I don't know, chemical concentration and they will avoid other. This is the phenomenon of chemotaxis. This is pretty basic. And this is a form of normativity. But for humans, we will typically think of laws, cultural norms, culturally shared way to judge behavior. And again, at least in the context of flow and choice theory, we typically think there is an agent, he has access to information and we'll think, what should I do? And he will judge rationally about what is good and what is bad based on a collection of values which are internal to the agent. This is not how it works. What I call embodied normativity is by contrast, regimes of normativity, where it is the norms and value that guide the agent's behavior. It is embedded in the material and cultural environment. So basically the agent projects a specific set of norms and value in the very experience of this environment. And it will perceive the locus of normativity as the environment and not as a form of internal judgment. Let us look at what can exactly how a normativity work. It will be more concrete. So first we can materially constrain people to do things.  So when you have a specific pattern of roads, which is constructed by cities, you are pretty much constrained to follow the roads materially. And for this we will talk of structural embedding. And there is another regimes of normativity where we build on environments that will basically, to you suggest you into adopting specific norms and specific values. For example, escape thinks that vertical patterns in archeological culture, they are somehow embedded within what we'd call vertical norms. So hierarchy and certification in social relationship. And for this specific form of embedding, we can talk of semantic embedding. And what is must be very clear is that we don't have structural embedding on one side and semantic embedding, there are basically two mechanism, complementary mechanism in which normative, sorry, embedding normativity occurs. If we look at the city, so this is impression of a Sumerian culture city-states, we have a matter organization that prompts specific kind of behavior. So for example, we have a bridge and this bridge allow you constrain you to go through the river in a specific way if you are on foot. But this material constraints, it is perceivable. So by seeing the bridge, you will also see that you can navigate the world in this specific way. So you don't have a separation between semantic and structural embedding, we just have two ways in which constraints of our behavior that are embedded in the environment work. And something that is a core to the account of embedding normativity is again, that it is not internal, but we need to go into more detail about what we mean by it not being internal because of course, we're talking about agency, agents constrain, what agents do, it's not outside. What it is is embedding normativity is a regime of normativity where the norms and value, they stem from the experience of fordance. For example, if I see a fire, I will not think about, hey, could I put my hand in it and get my hand burned? I think it's really painful, I won't do that. If I even think about putting my hand in the fire, I will immediately feel bad. The normativity it's embedded in my experience of the world. I feel like the fire or more specifically, the possibility of putting my hand in the fire is intrinsically bad. I do not feel there is a possibility that is neutral and then I judge it should be bad. But of course, this judgment does not occur outside even if I feel like it's about my fordances. It emerges from the engagement I have with the material and cultural niche in which I find myself. And if we want to untangle what this means precisely, we will have to go into more detail into the what I call the externalization and internalization of many normativity. So let us look at something very simple, how normativity work for embodied agents. So embodied agents, they do things with hopefully in a niche that is material. And this niche will be marked by the activity of my agents and this marks they can then act as constraints either through structural cementing and bedding in the agents' environment because they basically signify that in the past I wanted to do that, maybe doing that is good. For example, we have centuries with, I don't know if century is the English word, but we have path. If animals that have legs, this is actually important, work on a specific path, they will compact the earth and it will become proper to grow anything, even grass. And you will have a path that will emerge that are pretty visible. And I'll tell you, hey, not only this earth is compact and allow you to walk on it like without a fort, but also this has been widely judged to be a good outlier for navigation in this specific environment. So the very experience of a path basically invites you, it affords, you perceive the affordance of walking through the path. And this is something that again can result from your own activity that can be returned into the environment by your own activity. And so this begs the question of how, okay, there exists embedded normativity, but how do we embed normativity? What is the activity of embedding normativity like? And the word I will use for that is externalization. We perceive the world because we externalize specific causes of sensation into an imagined objective external world. So when we see the path, we see a path. We do not see compact earth that we then compute to mean that other things have walked on it. We just see, hey, there is a path. And this is because we have externalized a specific cognitive kind, which is the path. And this is an argument that several people made. Here, Lena Steiner, which she found out well, worked on the production of devices that help blind people see things through, I believe, an array of repository device on their shoulder. So the core argument they make is that if you could give people that, they will start seeing things. They will start perceiving things as being before them rather than having vibration on the shoulder and then thinking about, oh, what did it mean? And so the basic argument is that perception is just picture of what is the structure of an environment, given the existence of a specific coupling device that relates action to sensation and vice versa. And this critically relies on the internalization of specific, we could say a lot of things that I will say priors, specific priors in perception. So missing the path as a path relies on me having the prior knowledge that there is such a thing as path and I should be expecting to see them around. And so you have duality of externalization, in the production of embedded normativity. I can only see norms out there if I've internalized, integrated a specific apparatus of perception that entails that there are indeed those specific norms that are embedded in those specific afferences. And so this produce a more general question of semi-optics, basically of information theory. How do reading and writing and learning activity works? So when I externalize, I treat the world as having specific natural categories that I pretty much imagine. And I can use them because I mentioned them as an excellent memory that allow me to read and writes normative cues. This can be, you know, letters, pure signs, but as far as I'm concerned here, all signs are at least implicitly normative there about how you should interpret the sign. And this means that we can experience specific landscape of afferences and we can write things out there. We can write normative cues by a behavior that will then constrain further behavior, but nothing tells us that we can communicate through this. The communication, it works because of the internalization of prayers. If a given community of agents have internalized a specific set of prayers, that is a set of prayers that enable externalization of normative cues, then only it can use the environment as a way to write and read normative cues as an embedded normativity thing. And this communication, it can be read in terms of information theory. Of course, it can be read in terms of semantics, but what is critical to analyze that the process of internalization, it is there is no trust system, there is no private key that allow you to be sure that the way you internalize norms is very specifically the way in which the MLM activity was encouraged to begin with. There is a loss or there is ambiguity or there is, I'd rather say it like this, an active demarch of reconstruction of given prayers by the agents. And so this gap between the intent of other communicants and my intent by understanding of what I meant by their active engagement with the world, it builds a tiny gap in information that is somehow enough to drive the opening evolution of cultural landscape. And this is something that I do not think is embedded in active inference. And I think is of major interest to research, to the research, at least in the evolution of, open-ended evolution of cultural landscape. So to sum up things, I just presented the paper that is called Anomalydormativity, and that speaks unsurprisingly of embedded normativity. First major point is that if active inference is correct, then our notion of agency is dissolved essentially in the notion that we can perceive and enact cognitive landscape, umvalide if you want. And given that this is the case, we will experience the norms and value that guide our behavior as a property of the cognitive landscape we experience, the affordance that we can perceive. And this specific regime of normativity, I call embedded normativity. And the third thing is that you have a critical thing going on, you have a semi-autically constraining thing going on in the externalization and internalization of regimes of embedded normativity. Because those two things, they respectively enable nomad, embedded normativity to exist, to have a causal power. And they enable also the open-ended evolution of the norms that are embedded in a given landscape, in a given material landscape. And therefore, the open-ended evolution of the associated cultural or cognitive landscape. So thank you. I will enroll my references slides in case someone has the, really needs to know what the reference is and is ready to move around the video for this. And do you have questions? Awesome, thank you, Avail. While people are typing any questions, could you maybe just give a little introduction to the Active Inference for the Social Sciences course? Like, what brought you to want to facilitate this effort and where are we at and where do you want it to go? What do you want it to be for people? Oh, unmute and then continue. Good point, thank you. The Active Inference Institute organizes and carries research, organize a course on the Active Inference in the social science that is based, essentially in explaining the basic demarch, the basic hypothesis and the consequences of the cultural landscape formalism that I hinted at slightly in this presentation. The goal of the course is to give a basic ontology that is an interface between complexity science, let us say physics written large, cognitive science and social science to afford interdisciplinary integration in the social science and in the study of the evolution of human societies. And I think it is cool, but I'm biased because I've organized it some, maybe you should ask Daniel, which is absolutely biased, whether the course is cool. I've been enjoying it a lot. We just finished last week with my section on collective behavior. First we had the introduction lecture from you, then Ben White on the basics of being an active agent and we have Lorena and Mao's sections upcoming, as well as your more dedicated session. Yeah, it's been a really fun experience and it's gonna provide a really useful missing piece in the ecosystem. I guess just one question I have. How do we take some of these topics you're bringing in about the embedded normativities and think about them digitally? Like are we talking about conceptual embeddedness in information environments and or are we talking about the ergonomics of sitting at our desk or how do we bring that kind of pleasure path, stigmergy on the ground into the digital? Are we talking about something that's embodied and embedded in the information space or are we still talking about bodies? So if we talk about humans, we talk about bodies with the brains that do things in a material environment. But your question is, I'm not sure I understood this. How can we bring this specific concept in the study of digital phenomena like Twitter, internet? Is that it? Yeah. So you have basically the whole study on the affordances you have. So the recipient of design has incorporated the WILO goal, the national influence in its structure. And most of the study in the digital design center on what platforms afford or do not afford. And you have a richer stance when you take into account not what you can do with a specific interface, but what the specific interface invites you to do given a set of cultural return large. It can be self-learning. But given a set of power that you share with the platform in a way that is formal but indirect. And so yes, I would look at it in terms of user interface design. I think this concept specifically is useful for our interface design. Then you have implicitly the question of the flow that platforms such as, I don't know, Twitter, Facebook. How specific structural environments bring specific collective dynamics that might be a bit richer, not richer specifically, but that might go in a direction that the user interface question do not go into. And that can still be modeled in terms of constraints and therefore in terms of embedded normativity. Is that on point? We'll go to a question. Yeah, that's interesting about what the interface does and then what it invites. It might invite things in order for that invitation to be fulfilled that has to actually provide that possibility. But it might invite things that aren't relationally possible. Like there might be a wall which invites climbing to a given observer yet they cannot climb it. And then on the other side is a capacity that isn't invited and that is the adjacent possible of tool use where something that was being used for this purpose, it's like all of a sudden it just fell out of someone's hand. They grasped it a different way. It invited a different engagement and then that can become re-entrenched and that points to that sequential making and breaking of new spaces that your work has featured over the last few years. Yeah, so exactly this work has been developed with while thinking about tool use, while thinking about the cognitive ecology, while thinking about the experience of cities, the way urbanism from specific behavior. But if I can get back on the internet thing, I'd like to make it more specific. So the interface design as far as I know is focused on interface. That is not surprising. But in doing so it is focused on specific affordance what you can do. You have another layer in ecological psychology which is the layer of solicitation. So for example, Twitter does something very specific which is when you log in, it shows you the number of notification you have. Then it removes it and you have to wait to know which were those notification and you have to wait now, one second, not one minute, but a random amount of time. So this is what they call dark patterns. They are patterns in design that are explicitly designed to create addiction. So this is a layer you can't afford in affordance because from an affordance perspective, there is just moment you can access the information and then the moment you can't and then the moment you can again. There is nothing going on. If you don't look at the motivation and the way a specific structure environment is perceived, the semantics maybe, the meaning that given an escape as for an agent. And even if you look at this, you are still looking at the platform in a way that is embedded from the specific patterns, that specific patterns of things that people do on the platform. If you have Twitter without the aggressive curatees and the way to aggressively signal that you are in whatever it is is going on, you don't have Twitter much, you have Mastodon maybe. That has very similar affordances and very, very, very dissimilar solicitation. So this is a way to take a multi-scale account of things and when you take a multi-scale account of things, you have to look at the semantics basically behavior, how specific coordination occur at specific scales around specific communication, specific message passing. And once you look at how specific message become meaningful to specific agent, you have a problem basically because this is subjective, this is constructed through the activity of the agent and this is whatever is the range of time you are looking at, you will have the construction of new possibilities of adjacent possibles as you put it. And yeah, this is the big question that I think we are missing at the moment in active inference. Awesome. Well, thank you for this presentation. Thanks for the social sciences work and we're all looking forward to seeing how the projects play out. Thank you, goodbye. Farewell, Abo. All right. The next presentation is with Pablo Fernandez Maquietto. So welcome back, Pablo. Hello. I'm here. Okay. I did. Take us on an adventure of curiosity. Yes. I'm gonna start sharing my screen. Let me know if everything is all right. Should be perfect. Yep. Cool. So this is one of my favorite memes and probably most of you know it. This reference is the cave of Platon and to me it was kind of a fine-tutive truth that now with active inference, seems like we are closer to demonstrate that that's how it is. So the first time that I start act and serve at the Institute, I just... This was kind of my feeling or my face. It was looking into something that it was complex, scary and look interesting. So I went and why I decided to go on a adventure and go through it in a world where people is driving by strong incentives that motivate them to reduce uncertainty in the systems they are part of. My approach is through games, which are gonna go later, but I really on this talk want to encourage all kinds of persons or people that are curious and I want them to act and get involved in the community because you may find that for now most of the actors are scientists and very well unrepentant people and you might think that you are not a part of it, but very far away from that, the Institute and corrects the opposite. So I'm super glad to have been through this journey and I wanted to share a little bit of my background and then we go to the game. Here's the second and last meme. The world you were raised to survive is not longer exist.  Okay, who I am, I'm Pablo, my name is Pablo, but more define what best define me is my action and my experiences. I think role game plays and sports and movies are super important and as a kid I was dyslexic and the system didn't feel my necessities. So I really hated a school, but I love learning and it's something that I've been doing all my life and it's curious how a system that is designed to give you knowledge and to help you with the learning. In my case, it didn't work at all. And just that's a little starting point. I believe in the copy, transformation and combine all things and on my adulthood everything shifted and I became successful professional and I love IT teams. What I do for a living is a product manager and what I do on my pet projects, site projects, call it as you want is related to gaming and gamifying organizations and experiences. Nowadays, I trust more my intuition which is something that it really was broken during my childhood and you will see a little bit more about that. Here are some things of my interest, licenses, like open science, which is the case of the Institute, philosophy, that's the first meme, education, governance, family, art, love, communication, technology and nature and a bunch of other things that I will be happy to share any other time. On one of the questions that Daniel asked me for this talk, it was what was exciting for me about all this, what, right? And for me, I always, when I give talks, I like to serve a view of the last 100 years and the industrial revolution and how the ones that were on this world before us did something right. I'm sure that we all know big mistakes are big things that have failed or that could be better through iterations, but on data, it's very promising and very good. I think it's inspiring to have the basic education, how it grows, the vaccination, what it did for us, democracy and how poverty dropped drastically, how literacy it grew and because all these things, child mortality just went very low. So basically, the future of education is what makes me be excited and how I ended on this ecosystem. It's super random and I'm gonna explain it in hopefully two to five minutes. I, as a product manager and very interested in technology, I heard about blockchains back in 2015 and I didn't understood anything at all. And then back in 2017, I heard about the STOs, I did not disturb them, I did not play that game, but it was raising a fun thing to consider which is digital assets and ownership of those digital assets. For now, we didn't had a technology that could scale and that was good enough to ensure that the digital assets that you do own were yours and you had the freedom of transaction on them. I did understand that very fast and because I'm a game player and as a game player, you always have your different assets and do things with them and as well, I have been collecting music and all kind of digital assets. I bought this very famous collection of punks sold them very fast before I could be a millionaire but during 2021, I decided that I wanted to build something but then I went through this beautiful project that was the first art show on Ethereum which one of the artists, it was Daniel Friedman. So I as a curious person and a person that has been working with artists, I always like to know what is the driver of those artists, what they do and try to get in their walls to understand better and to get as much information as I can before I decide to encourage or collect or just always deeper on music or art for me is very nice thing to do. So I discover these cards and education was one of them and I went through Flickr which is where it was the first at least that I found a place where it was and the license it was a creative commons license of not commercial use, but it was an open license which I've been a part of creative commons community for a very, very long time since I was working with musicians and I think it's one of the best things that happened to the internet. These are some of the pragmatic things that the audience cares about. Hopefully we will have some of these on our games AI for sure is gonna be there. Free energy principle is gonna be one of the things that hopefully we help to minimize and to make it funnier games or happier games or games that help organizations to be better. The human factor, it's super important for me and implementation and action I learned by doing and it's one of the values that the Institute encouraged more. So I just jump on things and try to understand them through doing and sharing as I'm doing now. Here are the epistemics and collective is one of them that are as well super interest. Agents, of course, practical and curiosity which is the venture that I have the feeling that I'm being involved all my life and that I encourage always my two girls to be curious and to open things and look behind the doors and just be an active. Here are some values that I'd like to point with our exploration, the culture of curiosity that I did mention a lot. Continuous learning because that experience on the school I think I just keep going and keep going and it's something that sometimes bad experiences could turn out in good habits. Learning by doing as I already told integrity and inclusivity, honesty, accountability, a big one, trying to be professional on everything that you do, for me, I started as a human being curious. People of all backgrounds and perspectives are welcome to the institute. I could be very happy to be the link and to speak with someone that has doubts or if I could help, I think I could help on the very early stage and if you're not a very technical person I think I could help there and yeah. And as we are learning by doing, I would love to everyone that's on the stream, I'm gonna share this link, which is a new game that we are testing and I'm excited to share it with you. It's the adventure of curiosity and here, Daniel, I'm gonna copy and paste the link. It would be awesome. Okay, there's a tiny typo on your slides. It just ends with a curiosity. Thank you. But I'm gonna paste it into the YouTube live chat. Okay, thank you. Yeah, I told you I'm dyslexic. I have all kind of typos. My life. Sorry for that. I always try to improve it. Let's, okay, so you sir. Yeah, I put it in the YouTube chat so people can head over there. All right, yeah, could you tell us about this platform and this is the first active game? Yes. We started thinking on how to do a game a while ago and we decided to go with this picture that explains how it works. Actually, it's simple and you have four pieces, the internal states, external states. And in the middle, you have the sensory state and the active state. So, yeah. Here is the mission. You have some instructions that you can read and it would be fun if you go through them and you can go either way. And here we are gonna go to the sensory state where you're gonna listen to a music where some notes you can censor that are not in the famous song of Forelis. So, you have to take notes and you can go and you can see that we are on the mark of Langkid, which is so everything very Dalinian, the Dalibut painted. And here is your active part of the game where you have to do something, which is add the different notes that your music professor is telling you don't miss because you're probably on the external states are trying to play the music. So, you have to infer that you are missing some notes and go to the active state and start walking through the right notes. If I miss the note, I just follow into the nothing and come back into my internal state. So, probably would have to go back and censor again and once I take the right actions and the right notes, I can go and solve the puzzle. And the idea here is to have a tool, a gaming tool to explain or so you can experience how this active inference and free energy works. And once you go to the end of that adventure, which I'm not gonna finish because it's something that players should do, you're gonna have a door where you have to put the sequence in low keys of what were the missing notes and it's gonna allow you to come inside the treasure room where you will have feedback that you can send us and we will be very happy to have it. And then if you have a wallet, you will be able to have a digital asset. If not, we will add information to help people to go from web two to web three and to get those digital assets. And here to be developed would be future adventures that I would be very happy to do. So this was pretty much the presentation and hopefully we have new players in the coming days. Hope you have fun and I really, really encourage anyone to discover and play with all these ideas and thoughts and experiments and things that I have the gut feeling that are gonna revolutionize how we understand the world and how we can improve in it by reducing uncertainty. So thank you. And I don't know, Daniel, if you have any comments or something in this last 10 minutes. Oh yeah, we can definitely talk for a few minutes. I'm seeing many fun comments and questions in the chat so people can post it. Yeah, the gameplay is smoother on even a normal computer. It just like, it may look choppy on the live stream but it's quite a nice playing experience. Like maybe just a little context like how did you get into building these worlds or what kind of tools are these? And we obviously don't see them in today's mainstream educational and research offerings but like what could that even be to bring together this kind of immersion and role playing with education research? Yes. I've been playing role games and role games. You have very diverse kind of players. So I was playing with guys like me like have had a struggle through the school and guys that end up being researchers like yourself because they were very good in those environments. And for me it was like we are sharing a lot of cognitive values and information and they are really having fun with me. So what the academia is telling me is not what I'm feeling in outside academia. So that through the years it became more and more clear that was one of the drivers to have a very diverse kind of friends. And that one of the most important things for me to learn it was having fun and to be a good professional I have to be enjoying what I'm doing and to enjoy what I do it has to have that gamified feeling of it. And so in 2021 I was playing with a friend I just went to try to explore that road because blockchains what can do is have a persistent sea of objects and attributions to those objects and to those wallets that you can add and build on top of them. It's like having a huge database for a game purpose and then you figure out that again is kind of for our organization. So you need rules, you need to serve values, you need to have like what we are building now which is the CC on CEDO which you agree with the players what's gonna be about and how it's gonna work and we are building this fantastic storytelling of how a kind of world where it was in the early 20s compared to what's happening now in this imaginary world that's complementary to what's happening in real life. And we are just exploring those areas for three years and once I got into the institute we started sharing these ideas you send us to do system thinking course that it was super cool which we explore and learn and this document that I can say it was very important for us to understand and we find out the necessity to be able to computize what we are doing. So we start focusing more on ontology, on all the breaks that you need to understand to build the skeleton of something that could last for decades which is the idea and we are trying to build it so everyone can copy paste fork it and do whatever they want with the game adapt it to their own organizations and use it on their favor.  That's awesome and there's so many pieces there it's true about the fun and the curiosity those intrinsic motivations like being six hours deep into counting ants in the desert or 5,000 pipette pushes deep or whatever it is in the trenches and in the last mile and it can be easy to lose sight of but when we lose sight and when we put out the fire and the spark it's a cold dark night and no career or status or extrinsic valuation is gonna provide like simply the joy and the deeper meaning of like two people going on a little adventure running around this space being inside active inference rather than active inference being like this high speed train that's leaving anyone behind or you're on the outside and it's taking these Swiss interns and you're getting thrown off. It's like it is cool and air conditioned in this space and people play and watch a lot of games where they wields violent arms and not even like saying whether that's good or bad it just it is a genre to wanna have a camera either in a character's head, first person perspective or a camera in a second or a third person perspective which you can switch between here and like those kinds of topics about like egocentric versus allocentric navigation and our ability to have a perspective swap on ourself and be able to take that kind of like out of body situational awareness in real spatial settings and just to be able to cruise through this space and it's like, yeah, there's no right way to boomerang from extra, you can go from external to sensory you can go here or there. It's an appetizer and a fertilization that like we've really just never seen. Okay. Yeah. Thank you very much, Daniel for this. Any last thoughts or comments? No, just enjoy while we are here, which is I think our mission. All right, thank you Pablo. I'm sure everyone's gonna really enjoy this and let's also of course continue to develop these games and continue to offer these on ramps and have times where we're meeting up in these spaces and playing around. Thank you. All right, very well. All right. All right. Welcome, Mal. This will be the final of the presentation sessions in the symposium. This is Mal Albarasen with the presentation shared pretensions in active inference. So thank you, Mal, to you. And you're the first to join with an AI steward slash guardian. Awesome. Shall I share my screen? Yes. Let's do, let's do, let's do. And it's just a second. I'm looking for a window. I think this will do. Okay, so can you see the screen? Yep, looks good. Thank you. Awesome. So unfortunately, I imagine that you are seeing the entire screen and not just the presentation. I apologize about that, but I got it too. It's cropped. I got it. Okay, wonderful. Thank you. All right. So welcome to the presentation on shared pretension under active inference. We're going to cover a lot of topics. Shall I introduce myself a little bit? Or have you already done that? Awesome. So my name is Mal Albarasen. I'm a PhD student in cognitive computing. I work in active inference in the interaction between social sciences, philosophy, consciousness science and artificial intelligence. We're going to start a presentation on pretension, an active inference. So I'm going to define a lot of terms. It's going to deal into philosophy terms that are very deep. And so let's get started. If you will just give me one small second. So shared pretension is a term derived from phenomenology and it refers to the shared anticipation of future events among a group of individuals. This philosophical study of experience and consciousness becomes particularly interesting when we consider it in the context of active inference. When we combine these two concepts, we get a powerful tool for understanding how groups of individuals can coordinate their actions and work towards a common goal. This is the main focus of the discussion today. So imagine a group of people planning a trip. They all share a common goal to have a successful and enjoyable trip. The shared goal forms the basis of their shared pretension. They all anticipate future events related to the trip, such as booking tickets, packing their bags and visiting various attractions. As they start to take actions towards their goal, they're also engaging in active inference. They're making predictions about what will happen next and then adjusting these predictions based on what actually happens. So for example, they might predict that a certain flight will be the cheapest based on their past experiences, but when they actually check the prices, they find that another flight is cheaper. So they adjust their prediction and book the other flight. This constant cycle of prediction and adjustment guided by their shared pretension helps the group coordinate their actions and make their trip a success. So we're going to delve deeper into the concepts and explore the role of past experiences known as retentions in shaping our current knowledge and future predictions. Let's explain first the concept of retentions. Retention is a key component of time consciousness that retains the past trajectory of a temporal object. It's the preservation of the content of a now-past, hyaletic datum in our present consciousness. A hyaletic datum refers to the raw, uninterpreted sensations that arise in our consciousness. When we retain these data, we are essentially keeping a record of past sensations and experiences that can inform our present and our future actions. In active inference, we can formalize retention by thinking of moments as binning of states which are bound to a hierarchical state. This way, retention is seen as the binning which becomes the current state in forming the next expected state. To further connect retention with active inference, we can consider the way in which retention plays a role in updating an agent's belief and generative model. The past experiences influence the current expectations and actions, thus shaping the agent's understanding of the world and the future. So let's consider a practical example to illustrate this concept. If you had a good experience at a particular restaurant, you might choose to go there again in the future. Your past experience influences your future decision. In hustling temporal phenomenology, we would think about much shorter timescale, but if we extend this concept, we can see a much larger structure wearing the retention as simply the past state. It's like having a mental note that says, this place is good and this is how an example of retention works in your everyday life. They're not merely representation or memories, but presentations of a temporally extended present, which distinguishes them from recollections. This involves the presentation of some already constituted content in conscious experience. Retentions also play a crucial role in mapping past knowledge onto current beliefs contributing to the development of shared narratives or goals that we will delve into in the rest of the presentation. They can be former now points and the current awareness of the past and far sedimented retentions can be engraved in scripts or deep temporal priors or more fluid deontic cues. This phenomenon corresponds to a specific application of message passing between agents in the present moment and in the sensorium. Retentions and protentions may also emerge jointly within a group forming a collective understanding that transcends individual perspectives. Protentions, as we saw, refer to the anticipation of future events based on past experiences and current knowledge. It's thus forward-looking. It's the forward-looking counterpart to retention which is backward-looking. Protention is the anticipation of future trajectories of a temporal object. It's the process of looking ahead, predicting what's to come based on what has been and what currently is. So in active inference, it could be formalized by considering the future states that are expected based on the current state and past states. Protention is thus the anticipation guiding the next action. We can consider the way in which protention plays a role in updating an agent's beliefs and generative models, influencing the actions and thus shaping the agent's understanding of the world and the future. So for instance, if you see dark clouds in the sky, you might anticipate that it's going to rain and decide to carry an umbrella. This anticipation of future events and its pretension, it's also going to determine whether or not you're wet when you go outside and therefore, whether or not you expect to be wet. Your brain is constantly making predictions about what's going to happen next. So let's put this in the context of temporal flow, which ties together those concepts of primal impressions and we will see retention and pretension. Temporal flow is the structure that represents the continuous progression of time from the past through the present and into the future. Agents are constantly revising their generative models and they do this by integrating the observations, which we could refer to here as primal impressions. This updating process is a balancing app between maintaining the predictive density of the agent's current beliefs and adapting to new information. Pretensions and pretension structure time for a given agent creating a framework that shapes their perception and anticipation. Different agents or systems will exhibit varying qualities and quantities of retention and pretension, which resembles something like Mike Levin's concept of cognitive light cones. So given the presence of pretension, we can anticipate a potential horizon, not merely as an arbitrary expectation. Pretensions are thus the point at which agents' beliefs about the world are updated to match their current observations, which leads to a more accurate understanding of the environment and allows for more effective, goal-directed behavior. Primal impressions are the fulfillment of a pretension, which is the time-bound nexus of perception and existence. It is resolved, sorry, it is the resolved probability that redefines a local gradient. It's in primal impression that reality collapses. The primal impression is thus modeled as the essential Bayesian cognitive moment. It is the juxtaposition of prior beliefs or retentions with ongoing sensory information in an unfolding future-oriented present or pretension. So now we have a description of temporal thickness from the phenomenology, which is compatible with generative modeling in active inference agents. For Husserl, this flow of conscious experience is primarily made up of sensations that well up in a raw, uninterpreted form that we called earlier, hyaletic data. So this data is the now phase of a temporal object. It is formatted according to the idetic or cognitive laws of consciousness. This is specifically relative to inner time. We can understand how temporal flow structures, how agents are able to make choices, act to self-evidence, connect the passive structure with the active component of decision-making or goal pursuit. Individuals have drives and teleology insofar as they have preferences. These preferences are protented goals reached towards by what we would call potentially the fiat, where the fiat constitute the will to act towards a desire. So the fulfillment of a pretension by fiat drives the agent towards a teleology across a trail set. In a retensional extension, more information is amassed. So the longer your retention, more information can be caught in a moment. As individuals amass enough information, they can then find a structure to the information which becomes a map rather than a territory. It is computationally more efficient than the sum of all the information amassed. This new state of information may lead individuals closer to a shared goal. The information amass turns into an emergent goal of the retention, which grabs focus at the present moment because it seems to bring signals to the model that the pretensional goal is closer and is computationally simpler to retain. So sharing of retentions and pretensions among group members in a social context can facilitate coordination and the achievement of a shared goal. By aligning their beliefs about the past or retentions and expectations about the future or pretensions, group members can better predict each other's actions and intentions. This allows them to work together more effectively. In some cases, group members may have retentions and pretensions that align naturally due to common experiences or understanding, even without active communication. However, group members often actively communicate their retentions and pretensions, aligning their beliefs and expectations through dialogue and interaction, thereby enhancing their ability to predict each other's actions and intentions. Retentions and pretensions may also emerge jointly within the group, not confined to any individual agent's own vault, forming a collective understanding that transcends individual perspectives. This complex interplay of individual and collective dynamics can be explored further in hierarchical generative models, encompassing resonant cognitive models, communication, and even joint emergence. The structure of time consciousness encapsulated in retention and pretension thus plays a significant role in the emergence of shared pretensions. As agents retain and process similar elements from their environment, they can co-compute shared pretensions by mapping each other's cognitive pathways and building upon one another's trials. This process is formalized by Bayesian updating. The relationship between the topology of pretensions and the expectation gradient, as well as the role of retention in mapping past knowledge on the current beliefs, plays a role in development of shared narratives and goals. We can use this to get a deeper insight into the processes that drive the emergence of collective behavior and the development of shared understanding among agents. There is no better way to do it than using active inference by extending generative models of individual agents to account for the beliefs and goals of other agents. This is the Kintu theory of mind. It enables them to anticipate and adapt to the actions of their peers by offloading to the group. So we discussed a little bit earlier shared goals. Pretentional goals are future-oriented objectives that individuals or groups strive to achieve. They're not just mere desires, but they're actively pursued through the act of will that we called earlier fiat. The fulfillment of a pretension drives the agent towards a teleology, that trail set we discussed. The focus of a prediction entails less surprise and therefore the agent self-evidence is by seeking to accurately predict their surroundings. Agents select the series of actions or policies which generate transitions between states that produce expected observations. So through the choice of policy, an individual can reach their pretended goal by minimizing the expected free energy that a given policy affords them. This seems pretty central and simple in terms of active inference. So farther pretensions are represented by the policies of longer time depth. In a hierarchical model, this corresponds to a policy tied to a higher level action in a slower time scale. The choice of policies driven by the expected free energy of that higher order policy or the entire multi-scale generative model is based on the agent's preferences, allowing an agent to conduct golden-acted behaviors across temporal scales. So this is where we can cash out Jeff Yoshimi's trail sets where agents assimilate actions into the anticipated continuity of objects in their surrounding world. This dissimulation ensures a smoother understanding of the environment and helps agents adapt effectively. Near pretensions act as the near present contextual proximity and the far pretensions rely on passive synthesis of deontic cues which are sedimented retentions in the environment. The preferences of the agents aligned by policies correspond to attracting sets which in the socio-cognitive setting can be instantiated as narratives. Having shared narrative is a necessary aspect of any exchange between artifacts, people, institutions that have some kind of attracting set, generalize synchrony which we will discuss a little bit later as well. So a team working on a project has a potential goal to complete the project successfully. The shared narrative might be the project plan that guides their actions. It's like everyone in the team has a shared story about what they're trying to achieve and how they're going to do it. The relationship between current states and future observations is captured within the B matrix transitions. This matrix represents the probability of transitioning to a new state based on the current state. So the B matrix transition and the A matrix is utilized to determine which new observations is most likely to occur given the current state and potential transitions. In the Husserlian framework, the expectation gradient structure plays a crucial role in defining the depth and fulfillment of pretensions. The structure represents the varying degrees of certainty and confidence agents have in their predictions about future states. The topology thus is inter, and the arrangements is interrelation, sorry, there's an interrelation between the pretensions or the arrangements of future and expectations which is proportional to the expectation gradient. The higher the gradient, the more detailed and accurate the pretensions are likely to be. The potential horizon represents thus the static states of the expected futures of the temporal object while the continuation horizon is the amalgamated trail of the temporal object. Potential and continuation horizons are carried in protensions while the imminent horizon consists of the primary impressions. So agents can coordinate through co-construction where agents or individuals interact and collaborate to build this shared understanding and shared goals. This process is deeply interconnected within a complex web where agents typically coexist with other agents or subjects. Forming the collective often refer to as multi-agent groups, swarm ensembles or sandwich. By aligning their beliefs to the signal found within their niche, they can better adapt to the environment and develop a shared understanding of their surroundings. Sharing information with other agents is beneficial as it allows them to trust one another and pool their resources. This collaborative approach is preferable to a conflict-driven scenario in which every other agent is perceived as a potential source of risk and surprise. By sharing information, agents could collectively construct a more accurate representation of their environment leading to more effective decision-making and action. Through these interactions, shared narratives and goals can emerge from a co-construction of the world and each agent's generative model. So these narratives and goals are the result of dynamic process that in fact can be mathematically modeled through the equations that we introduce in the paper that this presentation is based on but that I will present a little bit later. They capture the process of mutual learning where agents adapt their generative model based on the information gathered from their interactions with others. Agents may start off with slightly or greatly divergent generative model. So in order to minimize surprise, there will be a natural alignment within a group as agents adapt to the evolving dynamics of their environment and social contexts. For example, again, consider this team working on the project. Even if you have your own understanding of the model or of the project, you'll interact and share information and learn from each other and then adapt the model. Over time, you'll develop a shared understanding of the project and a shared goal for its completion. This is an instance of co-construction. It's like building a house together where everyone contributes to the final structure. The emergence of shared narratives and the learning and recognition of them may be made more efficient through certain mechanisms such as language. In an experiment illustrated in a paper that Carl was on, two agents share the same sensorium, ask each other questions. I think it's a paper on 20 Questions in Emma Holmes with a shared narrative entailing a common language. This illustrates the market efficiency and the minimization of free energy simply by having a shared language, a common ground. So shared potentials are common, future anticipations or goals that emerge among groups of individuals through co-construction. It allows them to better anticipate each other's actions and intentions and work towards a common goal. It's strongly tied to the conscious efforts of individuals to work towards that goal and to effectively create an organizational structures where we're engaged in a conversation in an exchange, assuming that this exchange is meant to align our generative models. So think of, for instance, the football team. Again, they have a shared potential to win the match. They anticipate each other's moves and adapt their strategies based on the ongoing game. Everyone in the team has a shared vision of victory and they're constantly adjusting their actions to make it happen. So individuals infer each other's mental states by observing cues, which they can attribute to and causes. They use their own model or models that they have learned to recognize about others. This process is known as generalized synchrony entailing mutual predictability and can be interpreted as a collective minimization of free energy. In a group setting, individuals create cues in the world that direct other agents' attentions towards the same signals. They're models of the world aligned by sharing similar goals and similar environments and by reinforcing patterns of sampling reality. So we could try to represent this through polynomials and morphisms. Each agent's interaction with the environment corresponds to a morphism and the collective interaction of all agents can be represented as a polynomial. So a morphism, so if a polynomial p is defined, a morphism between two polynomials p and q consists of a pair of functions f and g such that f maps p of one to q of one and g maps p of i to q of the quantity one. This morphism must satisfy the commutative requirements meaning that the following diagram commutes p of one, p of i maps to q of the quantity i and p of one maps to q of one. So we could see how we could create a framework to further explore the cooperative and competitive dynamics among agents. For multiple agents indexed by i with boundaries p and share environment boundary q, their interaction can be represented by a morphism of polynomials, which allows us to understand the shared potential in multi-agent systems and provides a foundation for further exploration of cooperative dynamics. We allow each agent to predict normally its own behavior but also the behavior of its companions and the environment's response to their actions. So the dynamics of shared productions are really useful here and can be properly cashed out mathematically. So recursive cognition and prediction on the shared environment of the agent's actions and response to each other's actions. We can use Sheep Theoretic and Tobos Theoretic approaches to understand multi-agent systems. A multi-agent system is a system where multiple individuals or entities interact. So for example, a traffic system can be considered a multi-agent system where different vehicles are interact. But Tobos is a category that has both spatial and logical structure, allowing for the expression of logical propositions and deductions within it. So we can use these approaches to construct a coherent understanding of the world from the perspective of multiple agents. The internal universe of agents, which can be seen as mathematical objects, containing beliefs, perceptions and predictions of individual agents are represented using Topoi. These internal universes provide a foundation for understanding and representing the complex interactions and shared understanding among multiple agents in this dynamic environment. We try to construct a consensus, Tobos, by patching together the internal universes or Toboi of multiple agents. And the idea is to use the Sheaf Theoretic and Tobos Theoretic structures to build a shared understanding. So we can thus derive the synchronization of goal-oriented actions among agents over time and leverage mathematical insights for category theory and generative models. This connection is the mechanism through which the group synchronizes over time and ultimately achieves their objective. Agents within the system might possess varying levels of certainty in achieving the objective. Some may choose to expand less energy temporarily, decelerating their progress towards a desired outcome. This variation in certainty and approach leads to the emergence of different roles within the group, with some agents focusing on computational tasks, while other monitor outcomes are engaged in different actions themselves. The nested and distributed structure of generative models in active inference allows for the integration of both short-term and long-term goals. Agents with similar or compatible preferences can coordinate their actions by aligning their policies and working towards common narratives. So this alignment reduces the overall expected free energy of their joint actions, increasing the likelihood of achieving shared goals and facilitating the sharing of goals and expectation among group members. They can learn, which allows them to learn a central role in shaping agents' preferences and strategies over time. Agents can gather new experiences and update their generative model, they refine their preferences and methods for achieving goals. They can influence the formation and modification of shared narratives by having different perspective over different parts of a goal. Here, the concept of general life synchrony is manifested when all agents have learned compatible or harmonized aspects of the overall generative model. The synchronization is rooted in the retentional and potential dynamics of agents and can be more accurately described using mathematical models. So in conclusion, we tried to shed light on the concept of shared potential goals and multi-agent systems. We've emphasized the importance of understanding this concept as it plays a crucial role in the coordination and synchronization of agents' actions and intentions. In the future, continuously highlight the role of cues in the environment and how they can be simple like marks on the road or more complex like symbols and language. The cues become carriers of passive synthesis, far pretensions and far retention and become a way to minimize free energy as they minimize the amount of any individual has to compute. It also allows us to delve into the concept of other evidencing in which the environment is in effect the self and others must learn to navigate in terms of others and self. We've provided mathematical models to an equation to analyze the dynamics of shared potentials more accurately in terms of category theory. We've explored the dynamics of shared potentials and potential in hierarchical generative models which reflect the complex interplay of individuals and collective dynamics. We've encapsulated the structure of time consciousness and how this allows agents to co-compute shared potentials by mapping each other's cognitive pathways. So our approach basically begins an examination of Husserl-Temperl phenomenology integrated with the hierarchical active inference model, polynomial morphisms and polynomials. This enables recursive cognition and prediction of not only an agent's actions but also the environment's response to these actions. So I think this comes to the end, Daniel. I think I'm literally at 30 minutes. Thank you. Impactable. Thank you, Mao. Yeah, great presentation, awesome topic. So thank you for joining and looking forward to seeing how the work continues to develop. Farewell, Mao. See you. Bye. See you. All right. Okay, all right. The next session is gonna be the final session of this symposium. It's been a really incredible journey and I think this final session is gonna be great as well. So we have a whole panel here and people can use their cameras even though I'm not if they want to. Welcome everyone. I'll just introduce the facilitator, Kurt Geimangel. So Kurt is a legend in the active ecosystem and has done incredible work with a variety of researchers and curious individuals in the space. So we could think of no one better to host this conversation and bring it all together. So Kurt, I'll pass it to you. You'll keep an eye on the live chat. Anyone can call it if they need any supports. Otherwise, thanks everybody for joining and we don't see you yet, Kurt, but. Yeah, it would be all right if I left and then came right back for whatever reason my camera's not working. Sounds good, sounds good. Okay, I'll be right back in about 25 seconds. Well, welcome everyone. We will get started in just a second. So odd. I apologize for that. Okay, well, you can hear my voice at least. Yep. Give me a moment, I apologize. Boy, okay. Well, we'll do what we can. Okay. So you all can't see me, but that's all right. So we have this all-star panel here today, which to me means my invitation must have been a mistake, but I'll take it. Thank you to the Active Inference Institute and I'll go around this virtual Zoom table and introduce everyone briefly. Carl Friston is the Welcome Principal Research Fellow and Scientific Director at the Wellcome Trust Center for Neuroimaging and Professor of Neurology at University College London. He's also the Chief Scientist at Versus. Anna Lemke is Chief of the Stanford Addiction Medical, sorry, Medicine Dual Diagnosis Clinic at Stanford University. Her popular books include Drug Dealer, MD, and Dopamine Nation. Rafael Kaufman is CTO of Digital Gaia and the on board, sorry, and is on the board of directors of the Active Inference Institute. Bert De Vries is a professor at Eindhoven University of Technology, where he directs the bias slab research team and also works with industry. Guillaume De Ma is an Associate Professor in Computational Psychiatry of the Faculty of Medicine at the University of Montreal and the Director of the Precision Psychiatry and Social Physiology Laboratory in the CHU Saint Justine Research Center. He's also affiliated with Mila or Mila, which is Quebec's Artificial Intelligence Institute and Other Art Science and Consciousness Service Initiatives. My name's Kurt Jemungal and I use them, I use my background in Mathematical Physics to analyze various theories of everything that are proposed. These include both the theoretical physics side of grand unification with gravity and dualities, other schemes, as well as attempting to understand what constitutes consciousness. You can find the podcast by typing in theories of everything onto YouTube or whatever podcast catcher you have. So my question to everyone, the initial question is, what have you been working on in the past few months and what excites you about it? We'll start with Bert, you seem to be smiling and your name sounds like mine. Okay, so, all right, so my name is Bert, and yeah, I lead a lab in Electrical Engineering Department and so our lab is called BISLAB, as you mentioned. So we are interested in Bayesian inference in general, but more specifically in doing it as fast as possible. That has lots of applications outside active inference, also in signal processing and other control systems, but definitely, of course, also for active inference. So half our work over the past few months, but even over the past few years has been on developing a toolbox to support or to get as far as we can go with trying to do real-time Bayesian inference or real-time energy minimization and trying it in applications. So that's the work that I try to lead a team of PhD students who do the real work, of course. I work around with a cup of coffee, but that's what they have been working on. Great, and Professor Lemke, please. Yeah, well, what I've been working on in the last year or so is spending a lot of time thinking about how it is that a faith framework in particular surrendered to a higher power improves people's lives. And I've been really struggling to come up with a way of talking about this that is inclusive, thinking about it, talking about it. It gets to a kind of core piece of it that I'm interested in, which is not so much whether or not God exists and what the proof is or not of whether God exists, but really what is it that changes in people's lives when they undergo a spiritual transformation, when they surrender to a higher power? Why do their lives get better when they do get better? And I'm really new to the active inference world. Daniel Friedman is the one who's introduced me to these ideas. I'm really excited about the ways in which the whole active inference model might help me at least think about what's going on there. And so that's what I'm working on. It's a little weird, but that's what I'm interested in. Great Professor Friston, you're muted. Yes, that's going to be the title of my next book, Can't You're on Mute? I think that's a retro from the good of the future. I generally work on what I'm told to work on. So I've just been trying to list what I've been working on in the past few months. Interestingly, it actually starts with Daniel Friedman and his fascination along with Axel Constant for evolutionary explanations, speak to many of the scale-free issues that we've been hearing about in the prior session. So that's what I was working on, really a scale-free approach to understanding temporary nested processes as free energy minimizing processes as a kind of active inference and how that would lead to a variational synthesis of evolution. And then I was told to work on belief sharing by Mao and colleagues versus and worked on that in the context of synthetic language and to try to establish a really simple proof of principle that communication was an emergent property of any free ensemble free energy minimizing system. Just to open brackets, just to make the observation, it struck me time and time again during the session today how important communication is. Whether we're talking about Burt's passing messages or responding to your, you've got a new posterior or we're talking about the kind of communication that Mao was talking about, one of everybody's been talking about. It does seem to be a really, and indeed the Gaia notion of overconnectivity and too much communication or the wrong kind of communication. It does strike me that's quite central to both the theme of the workshop and the specific presentations that we've heard. And then in the past few weeks working on stuff, I think that Burt would be more interested in, which is fast and frugal message passing schemes that enable the evaluation of expected free energy not to suffer from the limitations imposed by deep tree searches. So possibly Ashwin has spoken about this earlier on, but you're using ideas from dynamic programming backwards induction to try and take the pressure of the message passing to get fast and efficient evaluations of the expected free energy in sort of single unit, sort of single agent active inference. What have I been doing this week? Can't remember. That's what I've been doing. Great, Rafael Kaufman, please. Just a moment. And can everyone else hear? Okay. What about now? Yeah. Great, yeah, thanks. It's contagious, I think. So I've also been drinking a lot of coffee as I've been caring for my now nine month old daughter. So. Congratulations. Thank you. So aside from sleep deprivation, what I've been doing is I told you guys about this in the session earlier today, but in a nutshell, I've been working on what we call the Gaia Protocol or the Gaia Network, which is this open approach for building a common engine. You can think of it as a common runtime and a common language with the building blocks of shared decision-making in the context of the Metacrisis and getting to a set of building blocks for decision-making that don't kill us all. And that's kind of it. Rafael, how much coffee is a lot of coffee? Well, it's espresso, so I don't think it's quite comparable to American coffee. All right. And Guillaume Dumas, please. Yes, sure. Can you hear me? Yes. Okay. So in the context of actual inference, I have mostly focused on, well, the two big topic of my lab, so precision psychiatry and social physiology. So on the precision psychiatry side, Nielke Boyten presented earlier in this symposium our work on trying to model theory of mind, being able to evaluate through digital phenotyping the level of sophistication of interaction in patients with neurodevelopmental disorders. And that's connected with also our interest in the social mind and how we can connect our understanding of how we deal with other people but with also ourselves. And so that connect with other work at the Miele with deep learning architecture with higher order function in humans where we're trying to combine different theories of consciousness such as global workspace and attention schema theory. And in the context of attention schema theory, there is this kind of recycling of self and other mechanisms. So that's one big work. And on the other side, I'm very interested also in multi-agent systems. And so with the Nathalie Castle, we're gonna join us soon. We've been working on creativity and the emergence of cultural norms in multi-agent systems with the idea of applying that to climate action and also a new project that is starting here with the NFRF of a big Canadian project on indigenous knowledge and how we can think about new narrative around AI and typically a less solipsistic and individualistic way of dealing with AI. So that's the two main field of research. Can you explain what you mean by less solipsistic? Less solipsistic when it comes to the AI? The Californian view of AI because the narrative is mainly driven by US and California right now tends to circle around optimization, profits. I mean, even open AI states that AGI is about optimizing profitable human tasks. They put like profitable in their definitions. But in the case of indigenous systems of knowledge, we are more talking about community sustainability and the view already only in the application is less individualistic. But also in the cognitive science point of view, we have like the hardcore computationalism that I would say the brain is just like a computer in a very restrictive sense. And so AI, like typically those transformers that are massively used right now are kind of like brain in a vat in a very silicon sense while we are embodied systems that are constituted by our interaction with others. And so like, in a way, all we can think about artificial intelligence in this kind of more co-constitutive way and through a developmental and cultural lens. Okay, now speaking of the brain as a computer, we frequently hear in these circles the brain as a predictive machine. So 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? So 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 and a notion of temporal thickness. And I'm sure that's got a lot to do with the necessary conditions for the kind of consciousness and guessing you're referring to, that freedom from the moment. And if one reads prediction in its sort of psychological or pretentive or pretension nature in terms of being able to predict what will happen in the future, I think that that's gonna be a sort of 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 line is just a difference between things that plan and do not plan. And 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 it's sort of reading under active inference that characterizes 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. But 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, I work primarily with narrative, the stories that they tell about their lives. And 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, they tend to then, 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 circumstance 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 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 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 also about models because what I've 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 a 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 it predicts far ahead that things get less accurate, right? If I predict that I want to go three quarters around the roundabout, 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, it 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 our, I think in our current way of implementing active inference. When we want to predict deep, 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 inference agents if we can actually compute messages that we want to know, that we don't care about if the LS 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.  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 these questions for a pretty long time. And I think what comes to mind is like how active inference as a lens, for instance, it enables us 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 what is between the various different processes or things that are going on in our head and our tendency to lump them all together into the same, okay, this is my experience that I'm immersed in. So noticing, being able to notice the difference between the process of perceiving and acting and what it says 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 look kind of seemingly arbitrary mental levels on top of it to try to make sense of it all and to force or static experience of having multiple frames 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 wanna say I like that, like this view of consciousness as being kind of defined by having a narrative or an internal model that's about self or at least is about self and that leads as Carl said to the planning as inference, that's actually like super deflationary in a way that people are not used to thinking about it and so it's an exciting, it's opening the door to all the sorts of exciting interdisciplinary 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.  Carl, I wanted to ask you about that when we had our podcast together, but Rafael has a similar question. So you said deflationary view when we have narratives of self, do you mean it leads us to something that consciousness is much more than? Sorry, much less. Yeah, I mean it's not necessarily much less than in the subjective sense, but it's much less than in the scientific sense. So one example of one not saying exactly that, but maybe something close to what Daniel Bennett 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 of what people, including you, but also other people say about their experience, which is, and of course, like what can be measured up neurocorrelates and whatever, which is not necessarily the same as our experience. So what we think is our direct experience of being conscious, right? So one way to look at it is that the science of consciousness doesn't necessarily have to put the primacy on people, the preferences, people saying they have quality that we thought in that sense, it's the inflationary, right? It's, you have to explain why people believe they have quality or not why people have quality because it's not necessarily a scientific truth that people have quality, right? Professor Dumas. Yeah, so well, on the limits of various parts of machine, well, 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 refide the methods. And well, this world symposium show 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, 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 pretty different principles, but taking the words as directly what they think the word mean, where it needs to be, we need to be careful in the way to communicate it. And typically following also what Professor DeVries said about prediction as being super important for decision-making, I think like 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 the Mao Al-Basin talking about category theory, and I'm very curious about right now among the different frameworks 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 gonna ask a question that will serve as, well, you each will speak on it, but as the other person is speaking, as sorry, 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.  Well, I was thinking what is my field, right? Cause we're very much in the last mile of applying the wonderful stuff that y'all come up with and making it useful on the ground. But so I'm extraordinarily excited about research like what Bert is doing. And I feel like as you all were saying, we're also starting to see some convergence on different ways to get to the same, at least to the same shape of answers. And in some cases, even to the same results. So one example of this is work that Chris, Chris Fields, Carl and others have been doing on the quantum, basically explaining quantum information theory and how it relates to the free energy principle, looking at the free energy principle as the classical limit of quantum information theory. And I don't pretend to understand all of it, but as somebody who's coming from a quantum physics background, just being alive to see this kind of convergence happening, which as I mean, as Guillaume said, we've been at this business of like both classical, what the hell's going on for over a century now? And it's nice to be at a time where again, we're starting to talk less about 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 a fast, interpretable, reliable and aligned decision-making systems, which includes AI systems, autonomous systems that have all those characteristics. And that it turns out that all the things that the 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 haven't incorporated so far. It turns out if you toss in a neural network approximator here and some boroughs and 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 of 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, Karl, please, if you don't mind answering the question and then we'll go Anna, then Bert and then Dumas. Actually just reflecting upon 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. It's listening to amazing presentation after presentation and just noting, found diverse. And yet there's this common thread, this common commitment, basically satiate our curiosity and using the tools that naturalize that kind of sense-making and curious behavior and communication that inherit from either maths or category theory or as rough 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, 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 it will go further and say it's completely background free and everything is constructed. It's just, so I think that 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 the 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 difference of spatial or yeah, spatial scales, but probably more importantly, sort of temporal scales and you see that 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 jus