Anyone that's interested in reading a more detailed account of the experiment can do so here:<p><a href="http://www.pokerlistings.com/libratus-poker-ai-smokes-humans-for-1-76m-is-this-the-end-42839" rel="nofollow">http://www.pokerlistings.com/libratus-poker-ai-smokes-humans...</a><p>The above article spells out some of the details of the competition. The winrate (14.72bb/100) that the AI achieved over the 120k hand sample is almost certainly not due to luck. It is a huge winrate that most pros have to employ strong game selection techniques to achieve (only play against bad players).<p>Here's a layman's explanation of how a poker AI can be trained:
<a href="http://www.pokersnowie.com/about/technology-training.html" rel="nofollow">http://www.pokersnowie.com/about/technology-training.html</a><p>And some details about the weaknesses resulting from how they've abstracted the game:
<a href="http://www.pokersnowie.com/about/weaknesses.html" rel="nofollow">http://www.pokersnowie.com/about/weaknesses.html</a>
Andrew Ng posted a comment about this on facebook: "I'm thrilled about Libratus' Poker triumph--this is a huge step for AI. Othello/Checkers/Chess/Go were theoretically solvable with minimax tree search and sheer computation; but poker, which requires bluffing, needs sophisticated modeling of your opponents and new algorithmic principles. CMU's Tuomas Sandholm has also (in a private email) promised to publish their algorithms, which I look forward to. Congrats CMU!!!"
<a href="https://www.facebook.com/andrew.ng.96/posts/1260889373966967" rel="nofollow">https://www.facebook.com/andrew.ng.96/posts/1260889373966967</a>
it won't be long before we hearing more headlines like:<p>"AI beats top 10 hedge fund managers"<p>to<p>"AI run hedge fund blows up due to black swan event"<p>regardless it's an incredible feat. It really casts questions into what our edge as humans are which is slowly disappearing and we didn't even need to put a brain in a jar and hook it up to a computer....it's deep learning reinforced algorithms that is appearing to outlearn, outthink the best of humans.<p>I just can't emphasize what a monumental period in history we are at. Humans are producing specialized algorithms that learn and hold information about the deep web of relationships between myriads of parameters to produce superior performance than humans.<p>It's almost like we've uncovered ways to automate our intelligence very much like we've been automating human and animal labor in the past couple centuries.<p>So the question is, how does an average joe hacker like me exploit and leverage this wonderful thing called deep learning? I'm not interested in reading PHD papers with advanced calculus.<p>I want to have a map of what AI, ML, DL, NN methodologies to use and when and who to hire based on that. This is no time to be a luddite and don't count on basic income from appeasing the masses anytime soon. Much like people took the most hit in the early rise of industrial revolution, our generation and immediate generation will be hit the hardest.
I suspect Libratus' overbet frequency is overfit to this particular reduced-variance game format. In a normal game, the opponent doesn't take chips off the table after winning a hand and might stand up at any moment.<p>It's hard to know how much that affected the strategy, but in the Reddit thread, the human players said the overbet frequency was what they were most surprised by.
Reddit AMA from the live match:<p><a href="https://www.reddit.com/r/IAmA/comments/5qi3i9/we_are_professional_poker_players_currently/" rel="nofollow">https://www.reddit.com/r/IAmA/comments/5qi3i9/we_are_profess...</a>
"Each night after the play ended, the Pittsburgh Supercomputing Centre added computations to sharpen the AI's strategy."<p>This sounds more like an "advanced chess" setup, where a human teams up with an AI to play. The title of the article should really be "amateur poker players + AI defeat professional poker players". The real test would be if the AI self-corrected over the length of the tournament, without human intervention.
Once this is out, it's going to be have a big impact on online poker games.<p>They way things are going with AI. You have a good algorithm, you can get rich very quickly by being a one man business with hardware rented in AWS.<p>Libratus AI player is modelling it's human counterparts and predicting how they think to outsmart them.<p>When Google started, they got the page rank algorithm and distributed algorithms good enough to run on shitty unreliable cheap computers. They are well on the way to become the world's largest company overtaking Apple someday.<p>Their ad algorithms already know that I am applying for a house loan and are blasting me with ads every fucking page I visit on the Internet.<p>I can totally see Google and Facebook personalizing ads per person and taking advantage of the person's vulnerablaties. Like psychologically modelling them to make them click ads and buy random shit. I can see the start of ultimate God algorithms for marketing.<p>The ability for AI to create drug like experiences for us that we can't stop craving.
Do a search for "long term" in online poker and you'll find that the suggestion for players to determine their level of play based on that "long term" is something like one million hands. That's running 4 tables for full days of poker over a long period of time. For sure it's playing more than 4 players.<p>The probability space of poker is such that 4 competitors isn't going to tell you much.<p>And defining "top" is difficult because "top" may be more celebrity than anything. Everyone has their different objectives. If you are "top" then you sure as <i></i><i></i> didn't get there by building a case history against AI poker bots. Give these guys a chance to adjust, and give them a chance to figure out why the effort to adjust might be worth bothering with.
Can someone clarify why exactly is this being presented as more impressive than beating Chess masters or even Go masters?<p>People talk a lot about number of states in poker, but the hand and visible cards can be easily (to a statitician) reduced to a scalar "probability of having the best hand".<p>At least in terms of crunching the possibility tree this should be a far less computationally intensive challenge.
Did I read the article incorrectly, or did it say that the bots creators were feeding it additional data each night before the next day's tournament? If so that defeats the entire purpose, doesn't it?
This article <a href="http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/ai-learns-from-mistakes-to-defeat-human-poker-players" rel="nofollow">http://spectrum.ieee.org/automaton/robotics/artificial-intel...</a>
says 120000 hands were played over the course of more than two weeks. How much of a factor was sheer boredom?
Hmm, why is it surprising that AI is good at poker? The way I see the game is that a bad poker player will just hold a model of his hand in mind. A slightly better one will also hold a model of his opponent's hand. Even better one will also model his opponent's model of himself... and so on recursively. And who's really good at recursion? Computers.
The University of Alberta bot has been beating top pros at limit hold'em for over 10 years.<p>no-limit and tournament play were deliberately placed outside the scope of their poker-bot projects--at least during the period of time i was following it which was approx. 2004 - 2010.<p>anyone know if the CMU team trained their rig on these variants?
EDIT: Read below, I am wrong. I clearly didn't know what I was talking about.<p>This is a great achievement in AI, don't get me wrong, but the headline should read, "AI beats the best four poker players we could find who were willing to play for a mere $200K".<p>All the actual best players play for millions and have a reputation to uphold. They would never agree to do this.<p>They four guys they got are pretty good, and could certainly destroy me, but they aren't the best of the best.<p>I'd love to see the bot play in the World Series of Poker for a few million.
Does anyone know the technical details of how to train and AI bot for a game like poker?<p>I imagine it's just reinforcement learning where the inputs are the actions of the individual players (hold/fold/raise, timing etc) and the statistical probabilities in terms of expected cards. Train a neural net to predict probability of the opponent's hands and act accordingly.<p>Is it just that the professionals all act similarly enough that the bot can learn based on other players?
Sounds like the end of online poker is very near. This version required a supercomputer and took a long time to decide its actions but those type of things tend to be quickly improved given enough motivation.<p>Even if poker sites could somehow perfectly detect automated players(which they can't of course), highly skilled poker is profitable enough that some people would be willing to manually execute the actions themselves as directed by the AI.
This article is related to no-limit heads-up, which requires a very different style of play than no-limit full ring. Full ring would be significantly more difficult to beat by AI. So the title leaves out some very important information. I am not surprised they beat heads-up; I would have expected it sooner.
There is a game humans can still beat machines at with ease, Diplomacy(1). When a machine wins a Diplomacy tournament I know we are finished as anything except pets.<p>1. <a href="https://en.wikipedia.org/wiki/Diplomacy_(game)" rel="nofollow">https://en.wikipedia.org/wiki/Diplomacy_(game)</a>
I'd really be interested in whether intentionally showing your cards instead of mucking them was taken into account by the AI. (e.g. showing your poor hand after a successful bluff)
Does the computer-ness of the AI give it advantage in poker w.r.t. counting cards, at which humans are imperfect? Or are humans perfect enough these days?