Dr. Sanjeev Namjoshi, a machine learning engineer and author of a book on Active Inference, dives into its theoretical and practical aspects. He explains how Active Inference utilizes the Free Energy Principle to minimize uncertainty in biological and artificial systems. Namjoshi highlights its potential to revolutionize machine learning, akin to deep learning's early days. He contrasts it with traditional methods, emphasizing its ability to foster exploration and curiosity, and explores the complexities of agency in AI and its implications for future cognitive modeling.

Join Sanjeev Namjoshi, a textbook-writing, Bayesian-educating enthusiast, in a discussion covering the teaching of active inference, its relation to evolution, and learning mechanisms. Explore topics like unsupervised learning, Bayesian inference, survival strategies in a dynamic world, expectation maximization, simplifying mathematics for active inference, using Python, R, and MATLAB for simulations, the influence of priors in Bayesian modeling, mathematical concepts in active inference, phenotypic priors in AI, and cutting-edge topics on intelligence and gratitude.

Delve into neuroscience and AI with Dr. Sanjeev Namjoshi as they discuss active inference and the free energy principle. Explore the brain's role as a generative model, the efficiency of active inference over deep learning, and the future of AI automation in daily tasks.