Join Neural Intel for an exhaustive exploration of the theories and algorithms that power autonomous intelligence. Drawing directly from the MIT Press publication "Algorithms for Decision Making" (Kochenderfer, Wheeler, and Wray), we examine the evolution of machine thinking from historical automata to modern connectionism and neural networks.In this episode, we tackle the core pillars of algorithmic choice:• Probabilistic Reasoning: Representing uncertainty through Bayesian Networks.• Sequential Problems: Solving Markov Decision Processes (MDPs) using exact and approximate methods.• State Uncertainty: Navigating Partially Observable Markov Decision Processes (POMDPs).• Multiagent Systems: How agents interact through Game Theory and equilibria.• Societal Impact: The critical ethics of AI safety, inherent biases, and the alignment problem.

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