Can a Chess Piece Explain Markov Chains?

The video explores the problem of a knight's random movement on a chessboard, using Markov chains to determine the average number of moves it takes to return to its starting square. It introduces the concept of stationary distribution and applies a theorem to solve the problem. The video also discusses pi in different metrics, highlighting its variability and irrationality.

What new insight or understanding did you gain from this video?