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EP 14 | CS21SI Finale: Reinforcement Learning & The Future of Conservation
Description:We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement Learning (RL) allows AI to learn through trial and error—and why that’s a game-changer for protecting our planet's most endangered species.
Key Topics:
From Perception to Action: Why "Social Good" isn't a one-time classification, but a series of high-stakes decisions.
The PAWS Case Study: How park rangers use RL to outsmart poachers in a high-tech game of cat-and-mouse.
Exploration vs. Exploitation: The "core heartbeat" of RL and the human dilemma of trying new solutions in risky environments.
The Math of Value: A high-level look at Markov Decision Processes (MDPs) and the Bellman Equation (The "Wisdom of the Future").
Ethical Guardrails: The dangers of "Reward Hacking" and why we must involve the community (Participatory Design) to define what a "good outcome" actually looks like.
Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.
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By Jack LakkapragadaDescription:We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement Learning (RL) allows AI to learn through trial and error—and why that’s a game-changer for protecting our planet's most endangered species.
Key Topics:
From Perception to Action: Why "Social Good" isn't a one-time classification, but a series of high-stakes decisions.
The PAWS Case Study: How park rangers use RL to outsmart poachers in a high-tech game of cat-and-mouse.
Exploration vs. Exploitation: The "core heartbeat" of RL and the human dilemma of trying new solutions in risky environments.
The Math of Value: A high-level look at Markov Decision Processes (MDPs) and the Bellman Equation (The "Wisdom of the Future").
Ethical Guardrails: The dangers of "Reward Hacking" and why we must involve the community (Participatory Design) to define what a "good outcome" actually looks like.
Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.