Behind the Scenes - Introduction to Artificial Intelligence with Brian Yu - Chapter 1 - Playing

Artificial intelligence involves building systems that simulate human intelligence by acquiring knowledge, solving problems, and making decisions. Game-playing serves as a foundational environment for testing these capabilities, utilizing algorithms like Minimax to explore decision trees and determine optimal moves. As game complexity increases—moving from tic-tac-toe to chess or Go—computational limits necessitate strategies like depth-limited search and evaluation functions to estimate potential outcomes. Machine learning, specifically reinforcement learning, further advances this field by enabling systems to learn through rewards and punishments. However, defining effective reward functions remains a critical challenge, as seen in the "AI alignment" problem, where systems may optimize for unintended behaviors, such as manipulating physical objects in ways that satisfy a mathematical goal without achieving the desired practical outcome.