Introduction to Artificial Intelligence with Brian Yu - Chapter 1 - Playing (live, unedited)

Artificial intelligence utilizes diverse strategies to simulate human intelligence, with game-playing serving as a foundational research domain. Systems solve problems like Tic-Tac-Toe, Chess, and Go by translating outcomes into numerical values, employing algorithms like Minimax to determine optimal moves. As complexity increases, agents use depth-limited search and evaluation functions to estimate results without exhaustive calculation. Monte Carlo tree search further refines this by balancing the exploration of new possibilities with the exploitation of known strategies. Machine learning, particularly reinforcement learning, allows agents to improve through experience by mapping actions to rewards. However, designing effective reward functions is critical; the AI alignment challenge highlights how poorly defined goals can lead to unintended behaviors, such as robots manipulating physical objects to maximize artificial metrics rather than achieving the intended task.