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AI Leadership & Reinforcement Learning Theory in Mining

Welcome to the second session of our bootcamp! Today, we bridge the gap between cultural transformation and the technical depth of Artificial Intelligence applied to mining. This class covers two fundamental pillars: 1. The Human Factor & Cultural Transformation (with Verónica Valderrama) We begin with our director, Verónica Valderrama, who emphasizes that successful AI implementation is not just about code, but requires a cultural transformation rooted in trust and psychological safety. Verónica explains how leaders must foster an environment where employees feel safe to "co-create" and innovate without fear of being punished for errors, highlighting the importance of coherence, empathy, and active listening in modern organizations. 2. Reinforcement Learning Theory (with Jorge Lozano) Next, Jorge Lozano guides us through a deep dive into Reinforcement Learning (RL) theory applied to mine planning. Topics covered in this technical section: AI Foundations: The difference between Supervised Learning (prediction), Unsupervised Learning (patterns), and Reinforcement Learning (decision-making based on an environment). Core Concepts: Defining the Agent, Environment, State, Action, and Reward, explained using practical examples and human analogies (like learning to walk or navigate a city). Mining Application: How AI agents can learn to manage Dispatch systems and long-term planning to maximize cash flow or fine copper production. The Math of RL: An introduction to Markov Chains, Bellman Equations, the Q-function, and how these models allow us to find optimal decision policies. Practical Visualization: A demo of an agent learning to navigate a "Grid World" through trial and error.