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Karl Friston’s New AI Architecture

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. How could Karl Friston’s Free Energy Principle become a blueprint for the future of AI? In this episode of Eye on AI, host Craig Smith sits down with Karl Friston, the neuroscientist behind the Free Energy Principle and advisor to Verses AI, to explore how active inference and brain inspired generative models might move us beyond transformer based systems. They unpack how Axiom, Verses’ new architecture, uses probabilistic beliefs and message passing to build agents that learn like brains instead of just predicting the next token. We look at why transformers face scaling and reliability limits, how Free Energy unifies prediction, perception, and action, and what it means for an AI system to carry explicit uncertainty instead of overconfident guesses. Learn how active inference supports continual learning without catastrophic forgetting, how structure learning lets models grow and prune themselves, and why embodiment and interaction with the real world are essential for grounding language and meaning. You will also hear how Axiom can sit beside or beneath large language models, how explicit uncertainty can reduce hallucinations in high stakes workflows, and where these ideas are already being tested in areas like logistics, robotics, and autonomous agents. By the end of the episode, you will have a clearer picture of how Karl Friston’s Free Energy blueprint could reshape AI architectures, from enterprise planning systems to embodied agents that understand and act in the world. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI 0:00 Preview & Intro 5:06 Karl Friston and Verses 6:09 Free Energy Principle Explained 12:29 Brain as Prediction Machine 19:10 Brain Learning versus Backprop 23:05 Beliefs and Mental Illness 26:56 Uncertainty in AI Systems 34:51 Inside Axiom Active Inference 37:33 Continual and Structure Learning 44:24 Is Axiom a Language Model 46:09 Embodiment and Grounded Agents 50:37 Combining Axiom with LLMs 54:57 Real World Axiom Applications 1:00:26 Active Inference Research Community