#033 Prof. Karl Friston - The Free Energy Principle
Machine Learning Street Talk (MLST) - A podcast by Machine Learning Street Talk (MLST)
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This week Dr. Tim Scarfe, Dr. Keith Duggar and Connor Leahy chat with Prof. Karl Friston. Professor Friston is a British neuroscientist at University College London and an authority on brain imaging. In 2016 he was ranked the most influential neuroscientist on Semantic Scholar. His main contribution to theoretical neurobiology is the variational Free energy principle, also known as active inference in the Bayesian brain. The FEP is a formal statement that the existential imperative for any system which survives in the changing world can be cast as an inference problem. Bayesian Brain Hypothesis states that the brain is confronted with ambiguous sensory evidence, which it interprets by making inferences about the hidden states which caused the sensory data. So is the brain an inference engine? The key concept separating Friston's idea from traditional stochastic reinforcement learning methods and even Bayesian reinforcement learning is moving away from goal-directed optimisation. Remember to subscribe! Enjoy the show! 00:00:00 Show teaser intro 00:16:24 Main formalism for FEP 00:28:29 Path Integral 00:30:52 How did we feel talking to friston? 00:34:06 Skit - on cultures (checked, but maybe make shorter) 00:36:02 Friston joins 00:36:33 Main show introduction 00:40:51 Is prediction all it takes for intelligence? 00:48:21 balancing accuracy with flexibility 00:57:36 belief-free vs belief-based; beliefs are crucial 01:04:53 Fuzzy Markov Blankets and Wandering Sets 01:12:37 The Free Energy Principle conforms to itself 01:14:50 useful false beliefs 01:19:14 complexity minimization is the heart of free energy [01:19:14 ]Keith: 01:23:25 An Alpha to tip the scales? Absoute not! Absolutely yes! 01:28:47 FEP applied to brain anatomy 01:36:28 Are there multiple non-FEP forms in the brain? 01:43:11 a positive conneciton to backpropagation 01:47:12 The FEP does not explain the origin of FEP systems 01:49:32 Post-show banter https://www.fil.ion.ucl.ac.uk/~karl/ #machinelearning
