Data Compression in Machine Learning

Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The free energy principle describes systems that pursue paths of least surprise, minimizing the difference between predictions based on their model of the world and their sense and associated perception. 

Dr. Thomas Parr is a practicing clinician and prominent researcher in the field of theoretical neuroscience and he currently works as an NIHR Academic Clinical Fellow in Neurology at the University of Oxford’s Nuffield Department of Clinical Neurosciences. He is also a co-author of Active Inference: The Free Energy Principle in Mind, Brain, and Behavior, written in collaboration with Giovanni Pezzulo and Karl J. Friston.



In this episode, we mention:

• Thomas Parr’s book “Active Inference: The Free Energy Principle in Mind, Brain, and Behavior (co-written with Giovanni Pezzulo and Karl J. Friston): https://www.penguinrandomhouse.com/books/704196/active-inference-by-thomas-parr-giovanni-pezzulo-and-karl-j-friston/
• Troxler's fading: https://en.wikipedia.org/wiki/Troxler%27s_fading 
• The Invisible Machines episode “Digital Natives Are Now AI Immigrants” with Marc Prensky: https://youtu.be/CAKumte_JP8?si=OxPKZbRKIegEjBvh 
• Landauer's principle: https://en.wikipedia.org/wiki/Landauer%27s_principle 

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