Tutorial on Active Inference

In this episode of The Turing Talks, we explore Active Inference, a neuroscience-backed model explaining how the brain works by minimizing surprise and maintaining homeostasis. The brain uses a generative model to predict the likelihood of observations, inferring hidden causes. To achieve the most accurate model, the brain maximizes model evidence—or, equivalently, minimizes surprise. A core concept, Free Energy, represents surprise and can be decomposed into complexity and accuracy. Through active inference, the brain selects actions that minimize free energy, constantly planning to achieve desired outcomes. We also discuss Epistemic Value, which gauges how much can be learned from observations, and how action selection aligns expected and real outcomes. The episode concludes with a look at limitations of active inference, but highlights its promise in fields like computational psychiatry.