Despite AI successes in perfect-information games, the hidden information and large size of no-limit poker have made the game difficult for AI to tackle. Libratus is an AI that, in a 120,000-hand competition, decisively defeated four top professionals in heads-up no-limit Texas hold’em poker, the leading benchmark for imperfect-information games and a long-standing challenge problem for AI in general. In this talk I will explain why past techniques intended for perfect-information multi-agent or imperfect-information single-agent settings break down in imperfect-information multi-agent settings, and the advances in Libratus and my later work that overcome those challenges. In particular, I will describe new general methods I developed for state-of-the-art equilibrium finding and real-time planning in imperfect-information games. These techniques all have theoretical guarantees in addition to strong empirical performance.