Physics as Information Processing ~ Chris Fields ~ Lecture 2 Active Inference Institute 2026-09-21 | Physics as Information Processing ~ Chris Fields ~ Lecture 2
Active Inference Institute ~ 2025 Quarterly Roundtable #3 Active Inference Institute 2025-09-27 | Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Twitter: https://x.com/InferenceActive Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 121.1 ~ Conscious active inference (Wiest and Puniani) Active Inference Institute 2025-09-24 | "Conscious active inference II: Quantum orchestrated objective reduction among intraneuronal microtubules naturally accounts for discrete perceptual cycles"Michael C. Wiest, Arjan Singh Punianisciencedirect.com/science/article/pii/S2001037025003770In the first of two companion papers, we argued that classical neural mechanisms proposed to implement conscious active inference had failed to establish their biological plausibility in terms of realistic biophysical models. We further explained that conscious (temporally deep) active inference is mathematically equivalent to the path integral that underlies quantum dynamics. As such, we proposed that a quantum model provides a natural, biologically plausible mechanistic implementation of the processing required by active inference.In this second paper we review the evidence establishing discrete non-overlapping cycles of perceptual inference, and argue that classical process models have so far failed to motivate or describe these discrete cycles in terms of realistic neural mechanisms. We then point out that the Orchestrated Objective Reduction (Orch OR) theory of consciousness naturally solves this fundamental problem.Along the way, we review independent strong theoretical and experimental evidence from my (Wiest) lab and others’ supporting the Orch OR quantum theory of consciousness as a collective quantum property of intraneuronal microtubules (MTs). This includes demonstration of room-temperature quantum effects in MTs, MT resonances controlling membrane spiking in living neurons, evidence that volatile anesthetics target MTs to cause unconsciousness, and direct biophysical evidence of a macroscopic entangled state in the living human brain. Intraneuronal MTs thus offer a biologically specific and experimentally supported substrate for implementing conscious active inference in brains.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
5th Applied Active Inference Symposium (Part 3, Nov 14, 2025) ~ LIVE Active Inference Institute 2025-09-18 | 5th Applied Active Inference SymposiumPart 3: November 14, 2025http://symposium.activeinference.institute/More to come!Active Inference Institute information: Website: https://activeinference.institute/ X: https://x.com/InferenceActiveDiscord: https://discord.activeinference.institute/Donate: https://donate.activeinference.institute/ YouTube: youtube.com/c/ActiveInference Active Inference Livestreams: https://video.activeinference.institute/
5th Applied Active Inference Symposium (Part 2, Nov 13, 2025) ~ LIVE Active Inference Institute 2025-09-18 | 5th Applied Active Inference SymposiumPart 2: November 13, 2025http://symposium.activeinference.institute/More to come!Active Inference Institute information: Website: https://activeinference.institute/ X: https://x.com/InferenceActiveDiscord: https://discord.activeinference.institute/Donate: https://donate.activeinference.institute/ YouTube: youtube.com/c/ActiveInference Active Inference Livestreams: https://video.activeinference.institute/
5th Applied Active Inference Symposium (Part 1, Nov 12, 2025) ~ LIVE Active Inference Institute 2025-09-18 | 5th Applied Active Inference SymposiumPart 1: November 12, 2024http://symposium.activeinference.institute/More information to come! Active Inference Institute information: Website: https://activeinference.institute/ X: https://x.com/InferenceActiveDiscord: https://discord.activeinference.institute/Donate: https://donate.activeinference.institute/ YouTube: youtube.com/c/ActiveInference Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 120.1 ~ Temporal Cognition and the Emergent Properties of Consciousness Active Inference Institute 2025-09-18 | "Temporal Cognition and the Emergent Properties of Consciousness"Ifedayo-Emmanuel Adeyefa-OlasupoIfedayo-Emmanuel Adeyefa-Olasupo is a theoretical physicist focused on understanding the laws that govern biological systems, the Mind behind Conversation Net, and founder of Figbox.https://conversationnet.vercel.app/figbox.coActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 119.1 Thermal Macroeconomics: An axiomatic theory of aggregate economic phenomena Active Inference Institute 2025-09-09 | "Thermal Macroeconomics: An axiomatic theory of aggregate economic phenomena"Nick Chater (Warwick Business School) and Robert MacKay (Mathematics Institute, University of Warwick) (joint first authors)Thermal Macroeconomics: An axiomatic theory of aggregate economic phenomenaN.J.Chater, R.S.MacKayarxiv.org/abs/2412.00886arXiv:2412.00886 [econ.GN]An axiomatic approach to macroeconomics based on the mathematical structure of thermodynamics is presented. It deduces relations between aggregate properties of an economy, concerning quantities and flows of goods and money, prices and the value of money, without any recourse to microeconomic foundations about the preferences and actions of individual economic agents. The approach has three important payoffs. 1) it provides a new and solid foundation for aspects of standard macroeconomic theory such as the existence of market prices, the value of money, the meaning of inflation, the symmetry and negative-definiteness of the macro-Slutsky matrix, and the Le Chatelier-Samuelson principle, without relying on implausibly strong rationality assumptions over individual microeconomic agents. 2) the approach generates new results, including implications for money flow and trade when two or more economies are put in contact, in terms of new concepts such as economic entropy, economic temperature, goods' values and money capacity. Some of these are related to standard economic concepts (eg marginal utility of money, market prices). Yet our approach derives them at a purely macroeconomic level and gives them a meaning independent of usual restrictions. Others of the concepts, such as economic entropy and temperature, have no direct counterparts in standard economics, but they have important economic interpretations and implications, as aggregate utility and the inverse marginal aggregate utility of money, respectively. 3) this analysis promises to open up new frontiers in macroeconomics by building a bridge to ideas from non-equilibrium thermodynamics. More broadly, we hope that the economic analogue of entropy (governing the possible transitions between states of economic systems) may prove to be as fruitful for the social sciences as entropy has been in the natural sciences.[Submitted on 13 Jun 2025]Convergence to equilibrium for a class of exchange economiesR.S.MacKayarxiv.org/abs/2506.11770 arXiv:2506.11770 [math.PR]For a class of stochastic dynamical models of exchange economies that we call ``fully connected Cobb-Douglas'', the paper proves convergence of the probability distribution to an equilibrium, in total variation metric as time goes to infinity. The convergence is exponential and the equilibrium is determined uniquely by the number of agents, their ``exponents'', and the initial amounts of money and goods in the economy.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active InferAnt Stream 015.1 ~ START/HERE: A Map & What Might Happen Next Active Inference Institute 2025-09-04 | "START/HERE: A Map & What Might Happen Next" Start here! activeinferenceinstitute.github.io/Start/here.html Repo: github.com/ActiveInferenceInstitute/StartDocs: activeinferenceinstitute.github.io/StartActive InferAnt Stream #015.1 youtube.com/watch?v=0cOV7-St2Ws&list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDMore information: http://welcome.activeinference.institute/Obsidian: https://obsidian.activeinference.institute/All Active InferAnt Streams: youtube.com/playlist?list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 118.1 ~ Tadahiro Taniguchi Co-Creative Learning via Metropolis-Hastings... Active Inference Institute 2025-08-21 | Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AIRyota Okumura, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwaraarxiv.org/abs/2506.15468[Submitted on 18 Jun 2025]We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interaction model based on the Metropolis-Hastings naming game (MHNG), a decentralized Bayesian inference mechanism. In an online experiment, 69 participants played a joint attention naming game (JA-NG) with one of three computer agent types (MH-based, always-accept, or always-reject) under partial observability. Results show that human-AI pairs with an MH-based agent significantly improved categorization accuracy through interaction and achieved stronger convergence toward a shared sign system. Furthermore, human acceptance behavior aligned closely with the MH-derived acceptance probability. These findings provide the first empirical evidence for co-creative learning emerging in human-AI dyads via MHNG-based interaction. This suggests a promising path toward symbiotic AI systems that learn with humans, rather than from them, by dynamically aligning perceptual experiences, opening a new venue for symbiotic AI alignment.GuestStream #104.1 youtube.com/live/veGQUGE0hLI Tadahiro Taniguchi : "Collective Predictive Coding and Active Inference: A New Perspective on Emergent Communication"Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 117.1 ~ Robert Stamps: Active Inference and Artificial Spin Ice Active Inference Institute 2025-08-20 | Active Inference and Artificial Spin Ice: Control Processes and State SelectionArticle link copied!pubs.acs.org/doi/10.1021/acsnano.4c13673Robert L. Stamps, Rehana Begum Popy, Johan van LieropACS Nano 2025, 19, 3, 3589–3601.https://doi.org/10.1021/acsnano.4c13673Theory and simulations are used to demonstrate implementation of a variational Bayes algorithm called “active inference” in interacting arrays of nanomagnetic elements. The algorithm requires stochastic elements, and a simplified model based on a magnetic artificial spin ice geometry is used to illustrate how nanomagnets can generate the required random dynamics. Examples of tracking and PID control are demonstrated and shown to be consistent with the original stochastic differential equation formulation of active inference. Interestingly, nonlinear response in the form of spikes and spike trains not predicted by the original theory can appear in the nanomagnet system for certain temperature regimes. A theoretical approach using a mean-field approximation for spin systems is proposed, which describes the transition to nonlinear response. Finally, the possibility to create simple magnetic arrays using realistic models is shown with micromagnetic simulations of a simple 17 element array of nanomagnets that include magnetic anisotropies, and exchange and dipolar interactions. Possible applications are simulated to illustrate how nanomagnetic arrays can be used as the stochastic element for feedback control of processes, investigation and control of magnetic state evolution, and as a method to optimize pulsed field magnetic switching protocols.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Generalized Coordinates in RxInfer.jl Active Inference Institute 2025-08-08 | RxInfer example repo: github.com/ReactiveBayes/RxInferExamples.jlThe code in this video is in github.com/docxology/RxInferExamples.jl/tree/main/research Background on Generalized Coordinates, see "Bayesian Mechanics for Stationary Processes" arxiv.org/abs/2106.13830 , covered in Livestream #026 series: .0 youtu.be/eZlG_J7sPj4 , .1 youtu.be/1rHz3Ir5v9c , .2 youtu.be/SH2v6joMD4k Get involved in the RxInfer project at the Institute! https://rxinfer.activeinference.institute/ Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 116.1 ~ Max Aifer: Solving the compute crisis with physics-based ASICs Active Inference Institute 2025-08-07 | "Solving the compute crisis with physics-based ASICs"arxiv.org/abs/2507.10463[Submitted on 14 Jul 2025]Maxwell Aifer, Zach Belateche, Suraj Bramhavar, Kerem Y. Camsari, Patrick J. Coles, Gavin Crooks, Douglas J. Durian, Andrea J. Liu, Anastasia Marchenkova, Antonio J. Martinez, Peter L. McMahon, Faris Sbahi, Benjamin Weiner, Logan G. WrightEscalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approaching limits of conventional CMOS scaling. Physics-based Application-Specific Integrated Circuits (ASICs) present a transformative paradigm by directly harnessing intrinsic physical dynamics for computation rather than expending resources to enforce idealized digital abstractions. By relaxing the constraints needed for traditional ASICs, like enforced statelessness, unidirectionality, determinism, and synchronization, these devices aim to operate as exact realizations of physical processes, offering substantial gains in energy efficiency and computational throughput. This approach enables novel co-design strategies, aligning algorithmic requirements with the inherent computational primitives of physical systems. Physics-based ASICs could accelerate critical AI applications like diffusion models, sampling, optimization, and neural network inference as well as traditional computational workloads like scientific simulation of materials and molecules. Ultimately, this vision points towards a future of heterogeneous, highly-specialized computing platforms capable of overcoming current scaling bottlenecks and unlocking new frontiers in computational power and efficiency.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 115.1 ~ Energy-Based Transformers and the Future of Scaling Active Inference Institute 2025-07-31 | "Energy-Based Transformers are Scalable Learners and Thinkers"Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Peixuan Han, Hyeonjeong Ha, Aman Chadha, Yilun Du, Heng Ji, Jundong Li, Tariq Iqbalarxiv.org/abs/2507.02092Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes. Further, we find that EBTs achieve better results than existing models on most downstream tasks given the same or worse pretraining performance, suggesting that EBTs generalize better than existing approaches. Consequently, EBTs are a promising new paradigm for scaling both the learning and thinking capabilities of models.Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)Cite as: arXiv:2507.02092 [cs.LG] (or arXiv:2507.02092v1 [cs.LG] for this version) doi.org/10.48550/arXiv.2507.02092Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 114.1 ~ How do inner screens enable imaginative experience? Active Inference Institute 2025-07-18 | "How do inner screens enable imaginative experience? Applying the free-energy principle directly to the study of conscious experience"Chris Fields, Mahault Albarracin, Karl Friston Alex Kiefer, Maxwell J.D Ramstead Adam SafronNeuroscience of Consciousness, Volume 2025, Issue 1, 2025, niaf009, doi.org/10.1093/nc/niaf009academic.oup.com/nc/article/2025/1/niaf009/8117684?This paper examines the constraints that the free-energy principle (FEP) places on possible model of consciousness, particularly models of attentional control and imaginative experiences, including episodic memory and planning. We first rehearse the classical and quantum formulations of the FEP, focusing on their application to multi-component systems, in which only some components interact directly with the external environment. In particular, we discuss the role of internal boundaries that have the structure of Markov blankets, and hence function as classical information channels between components. We then show how this formal structure supports models of attentional control and imaginative experience, with a focus on (i) how imaginative experience can employ the spatio-temporal and object-recognition reference frames employed in ordinary, non-imaginative experience and (ii) how imaginative experience can be internally generated but still surprising. We conclude by discussing the implementation, phenomenology, and phylogeny of imaginative experience, and the implications of the large state and trait variability of imaginative experience in humans.Highlights- We present a model of imaginative experience that is compliant with the free-energy principle (FEP).- We particularly address the questions of how imaginative experience is controlled and how it can be surprising.- We emphasize the roles of thermodynamic energy flows and metacognitive control in regulating both imaginative and non-imaginative experience.- We discuss the implementation, phenomenology, and phylogenetic distribution of imaginative experience, as well as state and trait variability in imaginative experience in humans.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active Inference Institute ~ 2025 Quarterly Roundtable #2 Active Inference Institute 2025-06-28 | Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Twitter: https://x.com/InferenceActive Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 113.1 ~ Bayesian Mechanics of Economic Choice (Ernesto Moya-Albor et al.) Active Inference Institute 2025-06-27 | "Bayesian Mechanics of Economic Choice: Computational Foundations of Economic Behavior"Ernesto Moya-Albor, Sergio Samuel Montanez Jacquez, Luis Alberto Quezada-Téllezresearchgate.net/publication/391768845_Bayesian_Mechanics_of_Economic_Choice_Computational_Foundations_of_Economic_BehaviorThis paper presents a theoretical unification of neuroeconomics with the Free Energy Principle (FEP) framework. We demonstrate that economic decision making can be formulated as a variational inference problem where agents minimize expected free energy, balancing risk (aligning predictions with preferences) and ambiguity (reducing uncertainty). Our formal analysis establishes mathematical equivalence between divisive normalization in neuroeconomic models and precision-weighted prediction error minimization in active inference. We show how Expected Subjective Value Theory (ESVT) from neuroeconomics naturally emerges from the FEP under Gaussian assumptions, explaining context-dependent valuation, reference-dependence, and risk attitudes through a common computational mechanism and generative model. This unification has significant implications for artificial intelligence, providing computational principles for developing more human-like decision-making agents that balance exploration and exploitation in an information-theoretic way. By bridging Bayesian mechanics with divisive normalization, we provide a neurobiologically plausible foundation for economic behavior that encompasses both classical utility maximization and information-theoretic approaches to decision-making under uncertainty. By integrating thermodynamic principles of information processing, we demonstrate how economic decision-making operates under physical constraints, offering a theoretical foundation for AI systems that must optimize computational resources while managing uncertainty.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 112.1 ~ Brain-like variational inference (Hadi Vafaii) Active Inference Institute 2025-06-26 | Brain-like variational inferenceHadi Vafaii, Dekel Galor, Jacob L. Yatesarxiv.org/abs/2410.19315Inference in both brains and machines can be formalized by optimizing a shared objective: maximizing the evidence lower bound (ELBO) in machine learning, or minimizing variational free energy (F) in neuroscience (ELBO = -F). While this equivalence suggests a unifying framework, it leaves open how inference is implemented in neural systems. Here, we show that online natural gradient descent on F, under Poisson assumptions, leads to a recurrent spiking neural network that performs variational inference via membrane potential dynamics. The resulting model -- the iterative Poisson variational autoencoder (iP-VAE) -- replaces the encoder network with local updates derived from natural gradient descent on F. Theoretically, iP-VAE yields a number of desirable features such as emergent normalization via lateral competition, and hardware-efficient integer spike count representations. Empirically, iP-VAE outperforms both standard VAEs and Gaussian-based predictive coding models in sparsity, reconstruction, and biological plausibility. iP-VAE also exhibits strong generalization to out-of-distribution inputs, exceeding hybrid iterative-amortized VAEs. These results demonstrate how deriving inference algorithms from first principles can yield concrete architectures that are simultaneously biologically plausible and empirically effective.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active InferAnt Stream 014.3 ~ The Sound of Uncertainty: Auditory Rendering of Generative Models Active Inference Institute 2025-06-24 | The Sound of Uncertainty: Auditory Rendering of Generative Models in the Field of StreamsDaniel Friedmangithub.com/ActiveInferenceInstitute/GeneralizedNotationNotationPaper: zenodo.org/records/7803328 - Smékal, J., & Friedman, D. A. (2023). Generalized Notation Notation for Active Inference Models. Active Inference Journal. doi.org/10.5281/zenodo.7803328More information: http://welcome.activeinference.institute/Obsidian: https://obsidian.activeinference.institute/All Active InferAnt Streams: youtube.com/playlist?list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf OrgStream 009.1 ~ The Value of Open Source Software (Hoffmann, Nagle, Zhou) Active Inference Institute 2025-06-19 | The Value of Open Source SoftwareManuel Hoffmann, Frank Nagle, Yanuo Zhoupapers.ssrn.com/sol3/papers.cfm?abstract_id=4693148Abstract: The value of a non-pecuniary (free) product is inherently difficult to assess. A pervasive example is open source software (OSS), a global public good that plays a vital role in the economy and is foundational for most technology we use today. However, it is difficult to measure the value of OSS due to its non-pecuniary nature and lack of centralized usage tracking. Therefore, OSS remains largely unaccounted for in economic measures. Although prior studies have estimated the supply side costs to recreate this software, a lack of data has hampered estimating the much larger demand-side (usage) value created by OSS. Therefore, to understand the complete economic and social value of widely-used OSS, we leverage unique global data from two complementary sources capturing OSS usage by millions of global firms. We first estimate the supply-side value by calculating the cost to recreate the most widely used OSS once. We then calculate the demand side value based on a replacement value for each firm that uses the software and would need to build it internally if OSS did not exist. We estimate the supply-side value of widely-used OSS is $4.15 billion, but that the demand-side value is much larger at $8.8 trillion. We find that firms would need to spend 3.5 times more on software than they currently do if OSS did not exist. The top six programming languages in our sample comprise 84% of the demand-side value of OSS. Further, 96% of the demand-side value is created by only 5% of OSS developers.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 111.1 ~ Modeling and Predicting Self-Organization in Dynamic Systems (G Georgiev) Active Inference Institute 2025-06-12 | Modeling and Predicting Self-Organization in Dynamic Systems out of Thermodynamic Equilibrium: Part 1: Attractor, Mechanism and Power Law Scalingmdpi.com/2227-9717/12/12/2937by Matthew Brouillet, and Georgi Yordanov GeorgievAbstract:Self-organization in complex systems is a process associated with reduced internal entropy and the emergence of structures that may enable the system to function more effectively and robustly in its environment and in a more competitive way with other states of the system or with other systems. This phenomenon typically occurs in the presence of energy gradients, facilitating energy transfer and entropy production. As a dynamic process, self-organization is best studied using dynamic measures and principles. The principles of minimizing unit action, entropy, and information while maximizing their total values are proposed as some of the dynamic variational principles guiding self-organization. The least action principle (LAP) is the proposed driver for self-organization; however, it cannot operate in isolation; it requires the mechanism of feedback loops with the rest of the system’s characteristics to drive the process. Average action efficiency (AAE) is introduced as a potential quantitative measure of self-organization, reflecting the system’s efficiency as the ratio of events to total action per unit of time. Positive feedback loops link AAE to other system characteristics, potentially explaining power–law relationships, quantity–AAE transitions, and exponential growth patterns observed in complex systems. To explore this framework, we apply it to agent-based simulations of ants navigating between two locations on a 2D grid. The principles align with observed self-organization dynamics, and the results and comparisons with real-world data appear to support the model. By analyzing AAE, this study seeks to address fundamental questions about the nature of self-organization and system organization, such as “Why and how do complex systems self-organize? What is organization and how organized is a system?”. We present AAE for the discussed simulation and whenever no external forces act on the system. Given so many specific cases in nature, the method will need to be adapted to reflect their specific interactions. These findings suggest that the proposed models offer a useful perspective for understanding and potentially improving the design of complex systems.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active InferAnt Stream 014.2 ~ Generalized Notation Notation for Generative Model Supply Chains Active Inference Institute 2025-06-09 | GNN for Generative Model Supply Chains: A Golden Spike Moment for Multiagent Trajectory Planning with RxInfer.jlDaniel Friedmangithub.com/ActiveInferenceInstitute/GeneralizedNotationNotationRxInfer.jl Multiagent Trajectory planning page: coda.io/d/RxInfer-jl-Active-Inference-Institute_ddtS-XZ4BJb/Multi-agent-Trajectory-Planning_sutlGFZ9 Paper: zenodo.org/records/7803328 - Smékal, J., & Friedman, D. A. (2023). Generalized Notation Notation for Active Inference Models. Active Inference Journal. doi.org/10.5281/zenodo.7803328More information: http://welcome.activeinference.institute/Obsidian: https://obsidian.activeinference.institute/All Active InferAnt Streams: youtube.com/playlist?list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 110.1 ~ Triple equivalence for the emergence of biological intelligence (Isomura) Active Inference Institute 2025-06-04 | Takuya Isomura: "Triple equivalence for the emergence of biological intelligence"nature.com/articles/s42005-025-02059-4github.com/takuyaisomuraIntelligent algorithms developed evolutionarily within neural systems are considered in this work. Mathematical analyses unveil a triple equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable Markov decision processes, and differentiable Turing machines, by showing that they minimise the shared Helmholtz energy. Consequently, canonical neural networks can biologically plausibly perform variational Bayesian inferences of external Turing machines. Applying Helmholtz energy minimisation at the species level facilitates deriving active Bayesian model selection inherent in natural selection, resulting in the emergence of adaptive algorithms. Canonical neural networks with two mental actions can form a universal machine by separately memorising transition mappings of multiple external Turing machines. These propositions are corroborated by numerical simulations of algorithm implementation and neural network evolution. These notions offer a universal characterisation of biological intelligence emerging from evolution in terms of Bayesian model selection and belief updating.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf MathStream 014.1 ~ Jesse van Oostrum: A concise mathematical description of active inference Active Inference Institute 2025-05-31 | A concise mathematical description of active inference in discrete timeJesse van Oostrumsciencedirect.com/science/article/pii/S0022249625000227?github.com/jessevoostrum/active-inferenceIn this paper we present a concise mathematical description of active inference in discrete time. The main part of the paper serves as a basic introduction to the topic, including a detailed example of the action selection mechanism. The appendix discusses the more subtle mathematical details, targeting readers who have already studied the active inference literature but struggle to make sense of the mathematical details and derivations. Throughout, we emphasize precise and standard mathematical notation, ensuring consistency with existing texts and linking all equations to widely used references on active inference. Additionally, we provide Python code that implements the action selection and learning mechanisms described in this paper and is compatible with pymdp environments.Jesse van Oostrum, Carlotta Langer, Nihat AyActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 109.1 ~ Hemming: Free Energy, Attentional Narrowing, and Cognitive Rigidity Active Inference Institute 2025-05-24 | "Free Energy, Attentional Narrowing, and Cognitive Rigidity: A Hierarchical Predictive Processing Approach"Alexander HemmingPredictive processing precision weighting is not always exact in its application, this models into how the study of social psychology generally point out when societal influences create inefficient, abnormal or problematic effects. In order to connect predictive processing theory to these phenomena we need to understand how precision weighting can be non-domain specific and how attention can reach outside of a person’s tendency to focus from the common elements within predictive processing.This means, that we need to understand how stress can cause attentional narrowing as a product of predictive processing, and how this can facilitate a dominant response, reduced acceptance of alternative perspectives and promote cognitive rigidity. In doing so, we address the process of error minimisation, especially in how heuristics allow for specific rules that can reduce the free energy requirement of error minimisation and combine the research from affective neuroscience, predictive processing and cognitive rigidity to give a unifying scope of the internal processes that can promote.Suggestions are made towards the future research directions surrounding choice paralysis, paranoia, emotional absolutism, ideological extremism, critical thinking capability, an alternative explanation for reactive approach motivation, conventionalism and an explanation for reduced integrative complexity in belief patterns.The presentation is delivered by Alexander Hemming.Collaborators on the project are Dr. Dylan Grove and Dr. Kirstin Wagner----Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 108.1 ~ Next-Generation AI for Emergent Semantic Communications (Christo Thomas) Active Inference Institute 2025-05-23 | Christo Kurisummoottil Thomas"Next-Generation Artificial Intelligence for Emergent Semantic Communications"Creating artificial intelligence (AI)-native next-generation wireless networks (e.g., 6G) faces technical hurdles due to data-driven, training-intensive AI limitations, such as black-box models, limited reasoning and adaptability, data dependency, and energy inefficiency. To overcome these challenges, we propose that next-generation wireless systems must embrace advanced AI models that mimic human-like reasoning and planning capabilities. Along this direction, in this talk, we present a forward-looking framework grounded in causal machine learning, which is founded on the principles of causal (cause and effect relations) discovery, representation learning, and inference. Such causal inference frameworks are poised to facilitate dynamic adaptability, resilience, cognition, and time-sensitive operations in future-generation networks, thanks to explainability, generalizability and sustainability induced by causality. Further, we illustrate the potential of causal reasoning AI frameworks through its application in an emerging paradigm known as semantic communications. In particular, we introduce a pioneering approach called "emergent semantic communication," by leveraging signaling games, and causal machine learning to craft a nuanced semantic language between the communicating nodes that mimics human-like communication. This language aims to optimize semantic representation, that reduces transmission by incorporating reasoning components at both ends of the communication process. Moreover, we present an example illustrating how semantic abstractions facilitate planning-defined as a sequence of network and control actions-to enhance the quality of experience for a remote autonomous agent controlled by a base station. Finally, we will also look at future directions, including designing emergent language for multiuser semantic communications using active inference framework. Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 107.1 ~ David Benrimoh The Role of Affective States in Computational Psychiatry Active Inference Institute 2025-05-22 | "The Role of Affective States in Computational Psychiatry"David Benrimoh, Ryan Smith, Andreea O. Diaconescu, Timothy Friesen, Sara Jalali, Nace Mikus, Laura Gschwandtner, Jay Gandhi, Guillermo Horga, Albert Powersarxiv.org/abs/2503.06049Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf MathStream 013.1 ~ Alexis Toumi: DisCoPy: Monoidal Categories for Active Inference Active Inference Institute 2025-05-21 | Alexis Toumi: "DisCoPy: Monoidal Categories for Active Inference"discopy.orgIn a recent line of work, Tull, Kleiner & Smithe have proposed category theory as a foundational framework for predictive processing and active inference. In particular, string diagrams provide a graphical representation for describing how the free energy of an agent can be described as the composition of the free energies of its parts. We argue that the same string diagrams can also be used as the underlying data structure for a modular implementation of active inference. We then give a demo of DisCoPy, the Python library for computing with string diagrams, and discuss its potential as a toolkit for the active inference practitioner.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active InferAnt Stream 014.1 ~ Generalized Notation Notation: From Plaintext to Triple Play Active Inference Institute 2025-05-16 | github.com/ActiveInferenceInstitute/GeneralizedNotationNotationPaper: zenodo.org/records/7803328 - Smékal, J., & Friedman, D. A. (2023). Generalized Notation Notation for Active Inference Models. Active Inference Journal. doi.org/10.5281/zenodo.7803328More information: http://welcome.activeinference.institute/Obsidian: https://obsidian.activeinference.institute/All Active InferAnt Streams: youtube.com/playlist?list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 106.1 ~ Advances and Challenges in Foundation Agents Bang Liu et al. Active Inference Institute 2025-05-06 | Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemsarxiv.org/abs/2504.01990The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges. This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research. We structure our exploration into four interconnected parts. First, we delve into the modular foundation of intelligent agents, systematically mapping their cognitive, perceptual, and operational modules onto analogous human brain functionalities, and elucidating core components such as memory, world modeling, reward processing, and emotion-like systems. Second, we discuss self-enhancement and adaptive evolution mechanisms, exploring how agents autonomously refine their capabilities, adapt to dynamic environments, and achieve continual learning through automated optimization paradigms, including emerging AutoML and LLM-driven optimization strategies. Third, we examine collaborative and evolutionary multi-agent systems, investigating the collective intelligence emerging from agent interactions, cooperation, and societal structures, highlighting parallels to human social dynamics. Finally, we address the critical imperative of building safe, secure, and beneficial AI systems, emphasizing intrinsic and extrinsic security threats, ethical alignment, robustness, and practical mitigation strategies necessary for trustworthy real-world deployment.Authors: Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang, Tanjin He, Sirui Hong, Hongzhang Liu, Shaokun Zhang, Kaitao Song, Kunlun Zhu, Yuheng Cheng, Suyuchen Wang, Xiaoqiang Wang, Yuyu Luo, Haibo Jin, Peiyan Zhang, Ollie Liu, Jiaqi Chen, Huan Zhang, Zhaoyang Yu, Haochen Shi, Boyan Li, Dekun Wu, Fengwei Teng, Xiaojun Jia, Jiawei Xu, Jinyu Xiang, Yizhang Lin, Tianming Liu, Tongliang Liu, Yu Su, Huan Sun, Glen Berseth, Jianyun Nie, Ian Foster, Logan Ward, Qingyun Wu, Yu Gu, Mingchen Zhuge, Xiangru Tang, Haohan Wang, Jiaxuan You, Chi Wang, Jian Pei, Qiang Yang, Xiaoliang Qi, Chenglin WuarXiv:2504.01990Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf GuestStream 105.1 ~ Paola Di Maio: Systemic Deviation, A Key to Explain Systemic Failures Active Inference Institute 2025-05-03 | Paola Di Maio: "Systemic Deviation, A Key to Explain Systemic Failures"- Live at this YouTube link on May 2 at 12 UTC youtube.com/live/TKksAVCOUCU .- Questions in the live chat during the live stream will be read and addressed by the presenter and conveners (if entering a question in the chat, give your real name as well). - To register and receive an invitation to the Zoom (subject to capacity) and be informed of follow up events fill out the form: https://forms.gle/mp8PLdHUaR4RqQqq7 . Intelligent and autonomous systems are increasingly embedded into the fabric of society. Innumerable risks underpin their complex architectures and widespread deployment, contributing to existential risks This presentation introduces the notion of Systemic Deviation, as a theoretical framework to support the conceptualization and cognitive elaboration of paradoxical situations where functional systems result in dysfunctional outcomes Systemic deviation can be used to understand how complexity can hide misaligned processes that lead to malfunction, failure and in some cases can result in outcomes directly contrary to the stated goals, without stakeholders understanding what is happening and how. The presentation discusses how systemic deviation can have adverse consequences on general systems *technical, and sociotechnical, and how it contributes to confounding, cognitive fog, and cognitive dissonance, which can lead to unnatural psychological states including suppression of intelligent functions, depression, loss including neuronal atrophy and synaptic loss in the medial prefrontal cortex. Mitigation strategies and the outline of a research agenda are presented.Paola Di Maio, PhD is a multidisciplinary systems analyst and research scholar, practicing internationally as lecturer and independent consultant in the fields of intelligent socio technical systems, with active publications in the fields of neuroscience, policy, natural health and current affairs.Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf ModelStream 018.1: Robust Decision-Making Via Free Energy Minimization (Russo & Jesawada) Active Inference Institute 2025-04-29 | Robust Decision-Making Via Free Energy MinimizationAllahkaram Shafiei, Hozefa Jesawada, Karl Friston, Giovanni Russoarxiv.org/abs/2503.13223Code: github.com/GIOVRUSSO/Control-Group-Code/tree/master/Robust%20Decision-Making%20Via%20Free%20Energy%20Minimization Despite their groundbreaking performance, state-of-the-art autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirable behaviors or even catastrophic failures. Robustness towards these training/environment ambiguities is a core requirement for intelligent agents and its fulfillment is a long-standing challenge when deploying agents in the real world. Here, departing from mainstream views seeking robustness through training, we introduce DR-FREE, a free energy model that installs this core property by design. It directly wires robustness into the agent decision-making mechanisms via free energy minimization. By combining a robust extension of the free energy principle with a novel resolution engine, DR-FREE returns a policy that is optimal-yet-robust against ambiguity. Moreover, for the first time, it reveals the mechanistic role of ambiguity on optimal decisions and requisite Bayesian belief updating. We evaluate DR-FREE on an experimental testbed involving real rovers navigating an ambiguous environment filled with obstacles. Across all the experiments, DR-FREE enables robots to successfully navigate towards their goal even when, in contrast, standard free energy minimizing agents that do not use DR-FREE fail. In short, DR-FREE can tackle scenarios that elude previous methods: this milestone may inspire both deployment in multi-agent settings and, at a perhaps deeper level, the quest for a biologically plausible explanation of how natural agents - with little or no training - survive in capricious environments.----Active Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
Active InferAnt Stream 013.1 ~ NoOrg is a Real Org Active Inference Institute 2025-04-26 | github.com/ActiveInferenceInstitute/NoOrgMore information: http://welcome.activeinference.institute/Obsidian: https://obsidian.activeinference.institute/Code: github.com/docxology/AgentLaboratoryAll Active InferAnt Streams: youtube.com/playlist?list=PLNm0u2n1IwdpcuXO_NfcLoQXcAkkGYVUDActive Inference Institute information: Website: https://www.activeinference.institute/Activities: https://activities.activeinference.institute/ Discord: https://discord.activeinference.institute/Donate: http://donate.activeinference.institute/YouTube: youtube.com/c/ActiveInference X: https://x.com/InferenceActive Active Inference Livestreams: https://video.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 24 (Chapter 10) ~ 4/17/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 5 (Chapter 2 +++ Open Discussion) ~ 8/12/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 3 (Modeling & General Discussion) ~ 7/29/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 4 (Chapter 2, part 2) ~ 8/12/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 25 (Chapter 10) ~ 4/24/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 1 (Chapter 1, Part 1) ~ 7/15/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 4 (Chapter 2, part 1) ~ 8/5/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 16 (Chapter 6) ~ 2/20/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 11 (Chapter 4) ~ 9/23/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 20 (Chapter 8) ~ 3/20/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 23 (Chapter 9) ~ 4/10/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 17 (Chapter 6) ~ 2/27/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 8 ~ Session 7 (Chapter 4) ~ 4/3/2025 Active Inference Institute 2025-04-24 | coda.io/d/ActInf-Textbook-Group_d4CkUI-iA_K/Cohort-8-Live-meetings_suDc1Fzphttps://textbook-group.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 22 (Chapter 9) ~ 4/3/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 6 (Chapter 3) ~ 8/26/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 21 (Chapter 8) ~ 3/27/2025 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ https://www.activeinference.institute/
ActInf Textbook Group ~ Cohort 7 ~ Session 7 (Chapter 4) ~ 9/16/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
ActInf Textbook Group ~ Cohort 7 ~ Session 2 (Chapter 1, Part 2) ~ 7/22/2024 Active Inference Institute 2025-04-24 | Full playlist for Cohort 7 textbook groupyoutube.com/playlist?list=PLNm0u2n1Iwdpx8_PMXvrxOuUlRnm7QX3cMore information on the Parr et al. 2022 textbook group at the Institute https://textbook-group.activeinference.institute/All Active Inference Institute livestreams and videos: https://video.activeinference.institute/Active Inference: The Free Energy Principle in Mind, Brain, and BehaviorBy Thomas Parr, Giovanni Pezzulo and Karl J. Fristonhttps://mitpress.mit.edu/9780262045353/active-inference/ activeinference.org
