In this episode, theoretical neuroscientist and authority on brain imaging Karl Friston, helps us understand his Free Energy Principle (FEP) for how life works and evolves.
We tackle this theory from five different perspectives to gain a deeper understanding; all the way from RNA and primordial soup to the future and safety of artificial intelligence.
The Free Energy Principle describes with mathematical precision how the brain conserves energy by minimizing surprise. Life at every scale of organization, from single cells to the human brain, is driven by the same universal imperative. This idea has a very great influence and affects the way we work and organize ourselves socially.
But how does this abstract principle translate into our everyday lives? The brain is continuously engaged in an act of interpretation called active inference that explains how we actively forage in the world for evidence that best satisfies our expectations.
Active inference is a corollary of the FEP and is the process through which we build models of our environment that we update with evidence we actively collect. Those familiar with statistics will recognize this description of the brain as particularly Bayesian.
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Who is Karl Friston?
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He is a Professor at Institute of Neurology, University College London and, Wellcome Trust Principal Fellow and Scientific Director of the Wellcome Trust Centre for Neuroimaging
Prof. Friston gained a reputation as the main proponent of the free energy principle, active inference and predictive coding theory, and is the inventor of statistical parametric mapping, voxel-based morphometry and dynamic causal modeling.
“Karl Friston’s free energy principle might be the most all-encompassing idea since Charles Darwin’s theory of natural selection”
Topics:
Welcome Karl Friston to The Rhys Show: (00:00)
What is FEP / active inference* & how does it apply in our daily life?: (01:39)
Understanding Free Energy Principle from 5 different perspectives: (04:26)
Information pre replicators / FEP pre sentient life: (00:00)
Distinction between animate & inanimate kinds / Predictive capacity / Markov blanket: (12:40)
Inanimate things that can exist based off of the law of physics: (18:23)
FEP compared to the principles of natural selection & evolution: (24:11)
RNA replicating in primordial soup; RNA a model of its environment?: (29:59)
About the brain / mutual predictability: (37:19)
Kind of information a bayesian brain can encode: (46:21)
How free energy applies to social systems: (54:27)
Cristianity a sense of a generative model: (01:02:57)
How free energy applies with machines and AI / AI safety & generative models that include us: (01:06:17)
