Podcast with Daniel Polani on information theory and embodied cognition

What if evolution discovered that information itself is the most reliable local gradient for finding good solutions? Computer scientist Daniel Polani explains how information theory provides a normative framework for understanding why sensors are optimized, why brains are expensive, and why cognition is fundamentally constrained by the physics of embodiment. Subscribe for more from the Convergent Science Network podcast series. Daniel Polani joins Paul Verschure and Tony Prescott at the BCBT summer school to present his information-theoretic approach to embodied cognition. Starting from the observation that biological sensors often operate near their physical limits, Polani argues that information serves as a local proxy that evolution uses to direct adaptation , organisms that capture more relevant information gain access to new ecological niches, creating a positive feedback loop between sensory refinement and behavioral complexity. The information bottleneck framework allows relevant information to be distinguished from noise, providing a principled way to think about what an organism needs to sense versus what it can afford to ignore. The discussion moves from sensor optimization to the metabolic cost of processing. Polani draws an analogy to the Carnot cycle, proposing that at every level of biological organization , from ATP management to cellular logistics to high-level cognition , there is information processing happening, with each hierarchical level consuming most of the available free energy for administration and leaving only a fraction for novel computation. He introduces the distinction between open-loop and closed-loop control to formalize how sensing adds power to an agent: the extra entropic influence of a closed-loop agent is bounded by how much information it takes in, establishing that cognitive performance has hard informational limits. The conversation addresses how embodiment constrains the information flow available to an agent, why memory is the natural next step beyond reactive sensing, and how the framework generates sub-goals naturally from the interaction between long-term goals and environmental structure. Polani argues that unlike abstract AI approaches that treat decision-making as unconstrained, this information-theoretic view reveals tangible physical limits on what any embodied agent can achieve. Key topics include the evolution of sensors, relevant information versus noise, the metabolic cost of cognition, open-loop versus closed-loop control, Landauer's principle and its connection to biological information processing, and why parsimony in neural computation is an evolutionary necessity. Part of the Convergent Science Network podcast series from the BCBT Summer School.