This Superhuman Poker AI Was Trained in 20 Hours

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📝 The paper “Superhuman AI for multiplayer poker” is available here:
– https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/
– https://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman.pdf
– https://science.sciencemag.org/content/early/2019/07/10/science.aay2400

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This Superhuman Poker AI Was Trained in 20 Hours

10 thoughts on “This Superhuman Poker AI Was Trained in 20 Hours

  1. Top pair with a semi-decent kicker on a flop that threatens a straight is not a strong hand. Its a very weak hand.

  2. In the advert, I like how you said WAN-D-B like it's a database, instead of W-AND-B for weights and biases lol

  3. I see no evidence here. As a pro player myself i know that AI i far from kicking any pro humans in this game(s). The data we have on these good bots is bad at best… a few early games.
    Give pro player a year again this bot he/her will destroy it completely.

  4. Jeez. Never thought poker would be conquered by machines. Inevitably, a machine will will be able to do everything a human can do.

    Machine/human integration is the next step in evolution. Dont believe me? The majority of people reading this have their phones on them 24/7. That's how it starts 😉

  5. Not impressed. The advantage the AI has is it doesn't fatigue mentally or tilt which is pretty significant. If a human can master those two weaknesses they can play poker at the highest levels.

  6. Okay, I'm not a machine learning expert, but I don't understand where the "secret sauce" is in this paper. What, if anything, was stopping people from using this approach a long time ago? The paper mentions that the training algorithm isn't very different from ones used by previous poker AIs, and it didn't take a huge amount of money or specialized hardware (about $144 on commercial cloud services!) to train, or even to run (two high-end CPUs worth about $4,000). So why did nobody reach nearly this level of performance before?

    I feel like I must be missing something important here. Usually, even if it's not clear why it works, I can at least get a sense of what was different. Here I just have no idea, but clearly they did something significantly better than anyone before.

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