Originally posted on May 12, 2015.

Humans have now been defeated by computers at heads up no-limit holdem poker.

Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no bluffing. Poker must be different! It is too human to be automated.

The game space of poker is different than that of Go. It has 10^160 different situations–which is more than the number of atoms in the universe. And the game space keeps getting bigger as the stack sizes of the two competitors gets bigger.

But it is still possible for a computer to beat a human at calculating game theory optimal decisions–if you approach the problem correctly.

Libratus was developed by CMU professor Tuomas Sandholm, along with my guest today Noam Brown. The Libratus team taught their AI the rules of poker, they gave it a reward function (to win as much money as possible), and they told it to optimize that reward function. Then they had Libratus train itself with simulations.

After enough training, Libratus was ready to crush human competitors, which it did in hilarious, entertaining fashion. There is a video from Engadget on YouTube about the AI competing against professional humans.

In this episode, Noam Brown explains how they built Libratus, what it means for poker players, and what the implications are for humanity–if we can automate poker, what can’t we automate?

Stay tuned at the end of this episode for the Indeed Prime tip on hiring developers.

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