In the six player hold-em poker game, it's not generally possible to calculate the Nash equilibrium. In these games, calculating and playing the Nash equilibrium guarantees that statistically (and in the long run), the player cannot lose, no matter what the opponent does. Many of the games in which AI was able to beat elite human players in the past, like Chess or checkers or Starcraft 2, are two-player zero-sum games.
In real life, there are usually more than two actors, there is hidden information to one or more of the adversaries, and it's not a zero-sum game. In many ways, multi player poker games resemble real life situations better than any other board game.
Not knowing the opponent's cards introduces the element of bluffing, which is generally not something algorithms are good at. Playing poker poses a challenge that the above mentioned games don't - that of hidden information. Libratus, which was Pluribus's predecessor, was able to beat humans in the two-player variation of Hold'em poker back in 2017. In the past years, computers have progressively improved, beating humans in checkers, chess, Go, and the Jeopardy TV show. Facebook AI Research's Noam Brown and Carnegie Mellon's professor Tuomas Sandholm recently announced Pluribus, the first Artificial Intelligence program able to beat humans in six-player no-limit Texas Hold'em poker game.
