The game of poker presents a serious challenge for artificial intelligence. The game is basically about dealing with uncertainty: unobservable opponent cards, undetermined future cards, and unknown opponent strategies. Coping with these uncertainties is critical to playing at a high-level. In this talk I'll outline both the challenges and the state of the art in overcoming these challenges. I'll outline some recent advancements in solving very large extensive games -- four orders of magnitude larger than traditional techniques. This technology enabled our programs to play competitively against professional players in the First Human-Machine Poker Championship this past summer. With this competition as motivation, I'll also discuss the critical problem of separating skill from luck in evaluating poker play. I'll present a general technique for defining unbiased low-variance estimators in extensive games, and demonstrate it by analyzing the results of the Human-Machine competition. If time permits, I'll discuss the future of research in computer poker and just how close we are to an artificial intelligence that can bring Phil Helmuth to tears.