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Dr. Sanjeev Namjoshi - Active Inference

Machine Learning Street Talk (MLST)

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Understanding Active Inference Framework

This chapter explores the technical foundations of active inference, emphasizing how agents interact with their environments through sensory data and generative models. It discusses Bayesian inference principles, variational free energy, and the importance of minimizing surprise to enhance perception and decision-making. Additionally, the relationship between action, state representation, and the optimization of inferential accuracy is examined, highlighting the significance of a reliable world model in reducing uncertainty.

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