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According to The Verge, "we're at a superhuman level for AI - and that's not going to change." Pluribus, the AI bot trained by Facebook to play poker, won an average of $5 per hand with hourly winnings of $1,000 versus the top human No-Limit Hold 'Em poker players.
Not only is the information needed to win hidden from players (making it what’s known as an “imperfect-information game”), it also involves multiple players and complex victory outcomes. The game of Go famously has more possible board combinations than atoms in the observable universe, making it a huge challenge for AI to map out what move to make next. But all the information is available to see, and the game only has two possible outcomes for players: win or lose. This makes it easier, in some senses, to train an AI on.
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