Computers beating humans in a game of wits is nothing new: chess grand master Garry Kasparov was first beaten by a computer in the 1990s. Google's AlphaGo programme triumphed over a human professional in 2015.

But both chess and go are closed-ended one-on-one games, where infinite processing power means a computer could look at all available moves and play a "perfect" game. 

Poker - so the story goes - is different. While top players need an expert understanding of probability, pure processing power is not enough to beat the best. To excel at poker, a player needs to read the humans around them and pick up on cues so they know when to bluff and when to call. 

This theory has been dealt a convincing blow by Pluribus, an algorithm created by Facebook and Carnegie Mellon university. 

Pluribus won a big hand against 4 top poker players, despite having a weak set of cards. The algorithm had learnt that by playing aggressively and bluffing their way through, they could bully others into folding and win the hand.

As machine-learning development continues at a rapid pace, algorithms are beginning to expand into areas where the data is murkier and the outcomes less clear.