1min snip

Machine Learning Street Talk (MLST) cover image

#106 - Prof. KARL FRISTON 3.0 - Collective Intelligence [Special Edition]

Machine Learning Street Talk (MLST)

NOTE

Probabilistic Mechanics and Path-Dependent Trajectories

The insights from this podcast snip include the concept of probabilistic mechanics, Bayesian mechanics, and information theoretic mechanics based on paths through time, including trajectories that cannot be localized to a specific time. These trajectories necessarily entail the future and the past, emphasizing the temporality aspect. In physics, contributions like Richard Feynman's focus on the pathological formulation, and when considering information geometry, intelligence, and autonomy, these are not states but trajectories, dynamics, narratives, and paths with a future pointing aspect.

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