8 ms·

One Giant Step for a Chess-Playing Machine

69 points by my_first_acct 8y ago

my_first_acct 8y ago
About the author (from the bottom of the article): > Steven Strogatz is professor of mathematics at Cornell and author of the forthcoming “Infinite Powers: How Calculus Reveals the Secrets of the Universe,” from which this essay is adapted.
ikeboy 8y ago
I don't like the framing. AlphaZero is just a very refined and efficient form of brute force. Precomputing weights obtained by brute force doesn't make the overall enterprise not brute force anymore.
- falcor84 8y ago
  In what way is it brute force? Obviously it's physically infeasible to store the best move for every position. AlphaZero statistically learns a way to recognize the gist of positions in ways that were previously not possible; I see this as a great achievement.
  - ikeboy 8y ago
    It's a great achievement but I don't think framing it as thinking akin to humans is a valid framing. >AlphaZero gives every appearance of having discovered some important principles about chess, but it can’t share that understanding with us. No, it doesn't have any new principles, it just has a very good system of weights. Imagine a perfect computer that plays the game by mapping out every single move. AlphaZero is a series of optimizations that allow for an efficient but lossy simulation of such a computer. Most of the research in this area is about making an oracle more practical/more accurate estimations of the theoretically correct response. I think it's fair to call it brute force with optimizations.
    - lern_too_spel 8y ago
      You could say the same about the way humans have learned to play chess. The main lines of the popular openings have been exhaustively searched. The way humans evaluate the middle game is also due to distilling features from brute force play and passing them on to other players who add on their own features and compute weights from additional brute force play. The difference between AlphaZero and StockFish is that StockFish only does the latter (compute weights for features given to it by others), while AlphaZero also does the former (distill features from game state).
      - ikeboy 8y ago
        The difference is humans have principles. A good analogy is intuition. If someone says AlphaZero has developed a strong intuition for chess, I have no issue with that, and to the extent human chess is based on intuition comparison is fair. But to portray humans as having chess principles, then say AlphaZero has new ones but just can't describe them seems off. To put it another way, humans have general intelligence and AlphaZero doesn't. At least some of the general intelligence is used for human chess play, so there is a dimension of that which AZ doesn't have.
        lern_too_spel 8y ago
        In the context of playing chess, humans use general intelligence to featurize the game state in terms of piece value and position value and weight the relative importance of those. The rest is memorization and simulation, which is where the intuition of advanced players comes from — they aren't actively thinking of piece value and position rules like beginmers. I don't see a huge difference here between humans and AlphaZero in those terms.
        ikeboy 8y ago
        I think using general intelligence to do that is importantly different from simulating a huge number of games and generating weights from that
- deleted 8y ago
  [deleted]
- soraki_soladead 8y ago
  In your comment here and below it sounds like you're saying: "if you take a brute force algorithm and make it more efficient than brute force by not brute forcing the solution then it's just a brute force algorithm!" If AlphaZero is brute force than any use of a non-exhaustive planning mechanism (pruned MCTS in this case) is brute force which is honestly ridiculous. Search and planning have a long history in both computer science _and_ neuropsychology because that is what we call the methods that are more efficient than brute force at the expense of some accuracy. There are some problems with the article but it isn't that AlphaZero is just some overhyped brute force algorithm.
  - ikeboy 8y ago
    I never suggested it's overhyped. I think the specific terms being used to compare it to humans are inaccurate. On a related note, AlphaZero used quite a lot of processing power - even with the optimizations, if it had to run on human hardware it would be pretty worthless.
    - lern_too_spel 8y ago
      > AlphaZero used quite a lot of processing power It ran on a single machine with four TPUs. In a few years with a few more optimizations, I can imagine an equal strength implementation on a handheld chess computer. If you're talking about training hardware, the correct comparison is against the processing time used by all serious human chess players through history because an apples to apples comparison would have both training from scratch (just the rules).
  - ikeboy 8y ago
    If we had a halting oracle and used it to output the best move for any situation, I would also call that brute force, even if it's actually only computing a handful of bits per move. Compressing a computation doesn't change the nature of what's being done meaningfully.
    - soraki_soladead 8y ago
      Your understanding of what brute force means is incorrect. It is an exhaustive algorithm. Any non-exhaustive algorithm is no longer brute force. An algorithm (like AlphaZero) that can locally generalize, even to small degrees, is also not brute force. Your argument is that a brute force algorithm is simply being compressed by a neural network but that isn't how AlphaZero works at all. You can't prune a brute force algorithm's branching and still call it brute force. In fact, if it was, then games like Go wouldn't be approachable by the same algorithm.
- starbeast 8y ago
  >AlphaZero is just a very refined and efficient form of brute force. As is evolution.
  - mannykannot 8y ago
    You have a point, but evolution is regarded, on both sides of the divide, as being the antithesis of intelligent design.
    - starbeast 8y ago
      Maybe. Is very good at designing intelligence however.
thomble 8y ago
The prospect of curing diseases is wonderful. But for now, we're just using these algorithms to figure out how to maximize the amount of time people spend staring at their phones.
fjfaase 8y ago
Interesting how this is covered in the New York Times, while earlier, AlphaZero (and its predecessor AlphaGo) showed the similar kind of insight in playing go (a much more complicated game than chess) coming up with moves that humans would dismiss almost immediately. Since then, go playing professionals have started to imitate this style of playing. I guess the same will happen with professional chess players in the coming years: there will be a less strong focus on material and more on positioning. Because AlphaZero cares less about pieces, but more about their position and abilities to attach and/or defend.
- cepth 8y ago
  IMO, at the highest levels of chess, there has always been a focus on "positioning" (positional chess) over material. World Champions like Capablanca, Botvinnik, Karpov, and Kramnik all play/ed in a style that was postionally sound, and at times boa-constrictor like. If you want to be a grandmaster today, you have to be able to understand/execute concepts like giving up material to establish a fortress ( https://en.wikipedia.org/wiki/Fortress_(chess) https://en.wikipedia.org/wiki/Fortress_(chess) ). The World Champion that played in the most sacrificial/attacking style, Mikhail Tal, was famed for giving up pieces to generate attacking momentum. Contemporary analyses of his play have found that some of these sacrifices were unsound, and some were actually the "best move" in a given position. I don't think it's feasible to expect human players to be able to calculate at the ply/depth that AlphaZero (or other chess engines) is able to. See this example from the latest World Championship (https://www.chess.com/news/view/world-chess-championship-game-6-caruana-misses-nearly-impossible-win https://www.chess.com/news/view/world-chess-championship-gam...). A "forced" win in 30 moves was available on the board, but it would've required that Caruana make moves that cut against the "principles" regarding piece placement ("positioning") that are drilled into chess players. I think a simple reality is that the search depths that AlphaZero (and to a lesser extent other chess engines) are dealing with are simply beyond human capability. A human player trying to execute the sacrifices that AlphaZero did (https://chess24.com/en/read/news/alphazero-really-is-that-good https://chess24.com/en/read/news/alphazero-really-is-that-go...) would be taking a stab in the dark. In most positions, they wouldn't really be able to calculate all the variations, or foresee how the endgame would play out.
Waterluvian 8y ago
I know very little about AI, but something that excites me, maybe just romantically, is the idea of an AI taking everything it learned from one game and applying it to another. Seems like we are happy to give them billions of games of practice. But what happens when exercise becomes a constraint? You've played a ton of chess. Now here's the rules to Go. Now play one game of it.
- Lkjhmnbv 8y ago
  There's a lot of research into this. If you're curious you can Google around for transfer learning and one shot learning.
  - Waterluvian 8y ago
    Thanks for the terms to search.
mindgam3 8y ago
Is it just my sonar beeping off the charts, or does anyone else hear the unmistakable signs of a submarine article? (1) Apologies in advance for what may be perceived as a rant. I have a very low tolerance for clickbait-y BS like this as it pertains to my own passions as a lifelong chess devotee and former professional player. First, the author of the article has no professional credibility in either chess or machine learning. He's a professor of math and a writer. No disrespect to either math or writing, I love and value both very highly, but they have very little to do with chess and machine learning per se. The problems is he tries to present AlphaZero as "humankind’s first glimpse of an awesome new kind of intelligence," which is really a bit of a stretch unless you add the disclaimer that technically all AlphaZero does is play 3 types of perfect-information games quite well. This is undoubtedly a great accomplishment, particularly in the field of Go which many domain experts felt intuitively would not crack to our AI overlords before another 5-10 years of computing power/hardware advances at least. (As someone who had the unfortunate label of "prodigy" applied in my youth due to earning the title of chess master at age 10, I consider myself somewhat of a domain expert in chess, and I was one of those people who got it wrong. I barely know the rules of Go, but intuitively I could comprehend that it was several orders of magnitude more complex than chess, and I was really hoping that the Go gurus would fend off the machines for longer. They didn’t. Hats off to DeepMind.) But. With all due respect to DeepMind engineers for an impressive result in chess and go, it's a bit too early to start thinking of AlphaXXX as an "oracle" where all we can do is "sit at its feet and listen intently" while we would "not understand why the oracle was always right" and eventually be left "gaping in wonder and confusion." (As an aside, the amount of pseudo-religious worship language in the piece is truly off the charts. I realize it stokes the passions, but it would be great if we could talk about AI’s true strengths and limitations without resorting to such histrionics. But I digress.) Why is it too early to start bowing down to a new god? Well, for starters, they basically just brute forced the game of Go a bunch of years earlier than predicted, but this wasn't just a pure software win, this was also heavily connected to massive increases in computing power aka GPUs and ginormous cloud-based render farms. Secondly, the author tries to make the leap from AlphaZero [good at 3 perfect-information games: chess, go and shogi] to what he calls "a more general problem-solving algorithm; call it AlphaInfinity". Note how he invokes the holy grail of AGI (Artificial General Intelligence) without actually using this term, which would set off alarm bells in, well, anyone who knew anything about AI who wasn't employed by DeepMind/Google. Notice further how this massive leap from "machine that can play 3 games well" to "machine that can, you know, actually think about stuff like a human can, including these pesky 'edge cases' and un-trained-for scenarios that always confuse our algorithms despite their otherwise inhuman level of perfection". One great example of such a case that may cause one to question these glorious predictions is a research paper titled “Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects” which shows how ML models consistently mistake a school-bus for a snowplow under the right (snowy) conditions (2). Far be it from me to dare bursting the bubble/reality distortion field of certain ML leaders and visionaries, but c’mon - a human child, once they truly learned how to recognize a schoolbus, would never mistake it for a snowplow, even if it was upside down. This flaw doesn’t mean that we can’t update training data to handle these types of rotations, but it does mean that we have a lot of work to do before we can say that these ML models have in some way grasped the “essence” of “school-bus” or [insert-other-object] here in a deep symbolic way, and by "deep symbolic way" I mean "any way that a human child learns how to do reasonably quickly before moving on to other, exponentially harder tasks". I could go on, but I won’t. Just in case my overall point isn’t clear: 1. AlphaZero is an unbelievably impressive accomplishment within the limited subset of life that is [chess, go, shogi] 2. ML approaches, even in computer vision, have a long way to go before anything remotely resembling child-level human intelligence 3. Therefore, can we please please stop the marketing masquerading as news articles about DeepMind’s latest result. And if anyone at DeepMind is listening: your product is pretty sweet! It would be better strategically to simply let it speak for itself, without trying to frame it as AGI. 1. http://www.paulgraham.com/submarine.html http://www.paulgraham.com/submarine.html 2. https://arxiv.org/pdf/1811.11553.pdf https://arxiv.org/pdf/1811.11553.pdf
Animats 8y ago
Has someone applied this to online poker yet? Make some real money.
- Lkjhmnbv 8y ago
  Heads up Texas holdem is "solved". https://www.scientificamerican.com/article/time-to-fold-humans-poker-playing-ai-beats-pros-at-texas-hold-rsquo-em https://www.scientificamerican.com/article/time-to-fold-huma...
  - cepth 8y ago
    The vast majority of "action" available online is "6-max" (6 player) or "full-ring" (9 player). On a PokerStars, WSOP.com, or Party Poker, you're going to find that there are maybe 1/10th or 1/20th the number of headsup tables as higher capacity tables. The development of "GTO" (game theory optimal) play in Texas Hold 'Em is certainly a first step in the direction of computers playing poker. However, there's still quite a long way to go. Poker Snowie, one of the cutting edge "GTO" programs, is based off of NNs (https://www.pokersnowie.com/about/technology-training.html https://www.pokersnowie.com/about/technology-training.html). At the same time, there are some glaring weaknesses in the software, namely that it can only offer suggestions at specific pot size bets (0.25, 0.5, 1, 2). The authors themselves concede some other weaknesses (https://www.pokersnowie.com/about/weaknesses.html https://www.pokersnowie.com/about/weaknesses.html). Worth mentioning that Amaya, the owner of PokerStars, has posted job openings for "AI researchers" (http://www.starsgroup.com/careers/job/Poker-AI-Research-Engineer-oyBn7fwn http://www.starsgroup.com/careers/job/Poker-AI-Research-Engi... & https://www.pokernews.com/news/2017/10/pokerstars-to-hire-artificial-intelligence-researchers-29136.htm https://www.pokernews.com/news/2017/10/pokerstars-to-hire-ar...). Some people think that the position may be to help PokerStars detect/combat bot use, but others think that there may be a (arguably bleak) future where players have the option to compete against Amaya-created bots online.
  - bobbiechen 8y ago
    Here's the paper about Libratus (mentioned in the article). In January 2017 Libratus beat a team of four top-10 heads- up no-limit specialist professionals in a 120,000-hand Brains vs. AI challenge match over 20 days. (PDF) https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf

69 points by my_first_acct 8y ago

About the author (from the bottom of the article): > Steven Strogatz is professor of mathematics at Cornell and author of the forthcoming “Infinite Powers: How Calculus Reveals the Secrets of the Universe,” from which this essay is adapted.

ikeboy 8y ago

I don't like the framing. AlphaZero is just a very refined and efficient form of brute force. Precomputing weights obtained by brute force doesn't make the overall enterprise not brute force anymore.

falcor84 8y ago
In what way is it brute force? Obviously it's physically infeasible to store the best move for every position. AlphaZero statistically learns a way to recognize the gist of positions in ways that were previously not possible; I see this as a great achievement.
- ikeboy 8y ago
  It's a great achievement but I don't think framing it as thinking akin to humans is a valid framing. >AlphaZero gives every appearance of having discovered some important principles about chess, but it can’t share that understanding with us. No, it doesn't have any new principles, it just has a very good system of weights. Imagine a perfect computer that plays the game by mapping out every single move. AlphaZero is a series of optimizations that allow for an efficient but lossy simulation of such a computer. Most of the research in this area is about making an oracle more practical/more accurate estimations of the theoretically correct response. I think it's fair to call it brute force with optimizations.
  - lern_too_spel 8y ago
    You could say the same about the way humans have learned to play chess. The main lines of the popular openings have been exhaustively searched. The way humans evaluate the middle game is also due to distilling features from brute force play and passing them on to other players who add on their own features and compute weights from additional brute force play. The difference between AlphaZero and StockFish is that StockFish only does the latter (compute weights for features given to it by others), while AlphaZero also does the former (distill features from game state).
    - ikeboy 8y ago
      The difference is humans have principles. A good analogy is intuition. If someone says AlphaZero has developed a strong intuition for chess, I have no issue with that, and to the extent human chess is based on intuition comparison is fair. But to portray humans as having chess principles, then say AlphaZero has new ones but just can't describe them seems off. To put it another way, humans have general intelligence and AlphaZero doesn't. At least some of the general intelligence is used for human chess play, so there is a dimension of that which AZ doesn't have.
      - lern_too_spel 8y ago
        In the context of playing chess, humans use general intelligence to featurize the game state in terms of piece value and position value and weight the relative importance of those. The rest is memorization and simulation, which is where the intuition of advanced players comes from — they aren't actively thinking of piece value and position rules like beginmers. I don't see a huge difference here between humans and AlphaZero in those terms.
        ikeboy 8y ago
        I think using general intelligence to do that is importantly different from simulating a huge number of games and generating weights from that
deleted 8y ago
[deleted]
soraki_soladead 8y ago
In your comment here and below it sounds like you're saying: "if you take a brute force algorithm and make it more efficient than brute force by not brute forcing the solution then it's just a brute force algorithm!" If AlphaZero is brute force than any use of a non-exhaustive planning mechanism (pruned MCTS in this case) is brute force which is honestly ridiculous. Search and planning have a long history in both computer science _and_ neuropsychology because that is what we call the methods that are more efficient than brute force at the expense of some accuracy. There are some problems with the article but it isn't that AlphaZero is just some overhyped brute force algorithm.
- ikeboy 8y ago
  I never suggested it's overhyped. I think the specific terms being used to compare it to humans are inaccurate. On a related note, AlphaZero used quite a lot of processing power - even with the optimizations, if it had to run on human hardware it would be pretty worthless.
  - lern_too_spel 8y ago
    > AlphaZero used quite a lot of processing power It ran on a single machine with four TPUs. In a few years with a few more optimizations, I can imagine an equal strength implementation on a handheld chess computer. If you're talking about training hardware, the correct comparison is against the processing time used by all serious human chess players through history because an apples to apples comparison would have both training from scratch (just the rules).
- ikeboy 8y ago
  If we had a halting oracle and used it to output the best move for any situation, I would also call that brute force, even if it's actually only computing a handful of bits per move. Compressing a computation doesn't change the nature of what's being done meaningfully.
  - soraki_soladead 8y ago
    Your understanding of what brute force means is incorrect. It is an exhaustive algorithm. Any non-exhaustive algorithm is no longer brute force. An algorithm (like AlphaZero) that can locally generalize, even to small degrees, is also not brute force. Your argument is that a brute force algorithm is simply being compressed by a neural network but that isn't how AlphaZero works at all. You can't prune a brute force algorithm's branching and still call it brute force. In fact, if it was, then games like Go wouldn't be approachable by the same algorithm.
starbeast 8y ago
>AlphaZero is just a very refined and efficient form of brute force. As is evolution.
- mannykannot 8y ago
  You have a point, but evolution is regarded, on both sides of the divide, as being the antithesis of intelligent design.
  - starbeast 8y ago
    Maybe. Is very good at designing intelligence however.

thomble 8y ago

The prospect of curing diseases is wonderful. But for now, we're just using these algorithms to figure out how to maximize the amount of time people spend staring at their phones.

fjfaase 8y ago

Interesting how this is covered in the New York Times, while earlier, AlphaZero (and its predecessor AlphaGo) showed the similar kind of insight in playing go (a much more complicated game than chess) coming up with moves that humans would dismiss almost immediately. Since then, go playing professionals have started to imitate this style of playing. I guess the same will happen with professional chess players in the coming years: there will be a less strong focus on material and more on positioning. Because AlphaZero cares less about pieces, but more about their position and abilities to attach and/or defend.

cepth 8y ago
IMO, at the highest levels of chess, there has always been a focus on "positioning" (positional chess) over material. World Champions like Capablanca, Botvinnik, Karpov, and Kramnik all play/ed in a style that was postionally sound, and at times boa-constrictor like. If you want to be a grandmaster today, you have to be able to understand/execute concepts like giving up material to establish a fortress ( https://en.wikipedia.org/wiki/Fortress_(chess) https://en.wikipedia.org/wiki/Fortress_(chess) ). The World Champion that played in the most sacrificial/attacking style, Mikhail Tal, was famed for giving up pieces to generate attacking momentum. Contemporary analyses of his play have found that some of these sacrifices were unsound, and some were actually the "best move" in a given position. I don't think it's feasible to expect human players to be able to calculate at the ply/depth that AlphaZero (or other chess engines) is able to. See this example from the latest World Championship (https://www.chess.com/news/view/world-chess-championship-game-6-caruana-misses-nearly-impossible-win https://www.chess.com/news/view/world-chess-championship-gam...). A "forced" win in 30 moves was available on the board, but it would've required that Caruana make moves that cut against the "principles" regarding piece placement ("positioning") that are drilled into chess players. I think a simple reality is that the search depths that AlphaZero (and to a lesser extent other chess engines) are dealing with are simply beyond human capability. A human player trying to execute the sacrifices that AlphaZero did (https://chess24.com/en/read/news/alphazero-really-is-that-good https://chess24.com/en/read/news/alphazero-really-is-that-go...) would be taking a stab in the dark. In most positions, they wouldn't really be able to calculate all the variations, or foresee how the endgame would play out.

Waterluvian 8y ago

I know very little about AI, but something that excites me, maybe just romantically, is the idea of an AI taking everything it learned from one game and applying it to another. Seems like we are happy to give them billions of games of practice. But what happens when exercise becomes a constraint? You've played a ton of chess. Now here's the rules to Go. Now play one game of it.

Lkjhmnbv 8y ago
There's a lot of research into this. If you're curious you can Google around for transfer learning and one shot learning.
- Waterluvian 8y ago
  Thanks for the terms to search.

mindgam3 8y ago

Is it just my sonar beeping off the charts, or does anyone else hear the unmistakable signs of a submarine article? (1) Apologies in advance for what may be perceived as a rant. I have a very low tolerance for clickbait-y BS like this as it pertains to my own passions as a lifelong chess devotee and former professional player. First, the author of the article has no professional credibility in either chess or machine learning. He's a professor of math and a writer. No disrespect to either math or writing, I love and value both very highly, but they have very little to do with chess and machine learning per se. The problems is he tries to present AlphaZero as "humankind’s first glimpse of an awesome new kind of intelligence," which is really a bit of a stretch unless you add the disclaimer that technically all AlphaZero does is play 3 types of perfect-information games quite well. This is undoubtedly a great accomplishment, particularly in the field of Go which many domain experts felt intuitively would not crack to our AI overlords before another 5-10 years of computing power/hardware advances at least. (As someone who had the unfortunate label of "prodigy" applied in my youth due to earning the title of chess master at age 10, I consider myself somewhat of a domain expert in chess, and I was one of those people who got it wrong. I barely know the rules of Go, but intuitively I could comprehend that it was several orders of magnitude more complex than chess, and I was really hoping that the Go gurus would fend off the machines for longer. They didn’t. Hats off to DeepMind.) But. With all due respect to DeepMind engineers for an impressive result in chess and go, it's a bit too early to start thinking of AlphaXXX as an "oracle" where all we can do is "sit at its feet and listen intently" while we would "not understand why the oracle was always right" and eventually be left "gaping in wonder and confusion." (As an aside, the amount of pseudo-religious worship language in the piece is truly off the charts. I realize it stokes the passions, but it would be great if we could talk about AI’s true strengths and limitations without resorting to such histrionics. But I digress.) Why is it too early to start bowing down to a new god? Well, for starters, they basically just brute forced the game of Go a bunch of years earlier than predicted, but this wasn't just a pure software win, this was also heavily connected to massive increases in computing power aka GPUs and ginormous cloud-based render farms. Secondly, the author tries to make the leap from AlphaZero [good at 3 perfect-information games: chess, go and shogi] to what he calls "a more general problem-solving algorithm; call it AlphaInfinity". Note how he invokes the holy grail of AGI (Artificial General Intelligence) without actually using this term, which would set off alarm bells in, well, anyone who knew anything about AI who wasn't employed by DeepMind/Google. Notice further how this massive leap from "machine that can play 3 games well" to "machine that can, you know, actually think about stuff like a human can, including these pesky 'edge cases' and un-trained-for scenarios that always confuse our algorithms despite their otherwise inhuman level of perfection". One great example of such a case that may cause one to question these glorious predictions is a research paper titled “Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects” which shows how ML models consistently mistake a school-bus for a snowplow under the right (snowy) conditions (2). Far be it from me to dare bursting the bubble/reality distortion field of certain ML leaders and visionaries, but c’mon - a human child, once they truly learned how to recognize a schoolbus, would never mistake it for a snowplow, even if it was upside down. This flaw doesn’t mean that we can’t update training data to handle these types of rotations, but it does mean that we have a lot of work to do before we can say that these ML models have in some way grasped the “essence” of “school-bus” or [insert-other-object] here in a deep symbolic way, and by "deep symbolic way" I mean "any way that a human child learns how to do reasonably quickly before moving on to other, exponentially harder tasks". I could go on, but I won’t. Just in case my overall point isn’t clear: 1. AlphaZero is an unbelievably impressive accomplishment within the limited subset of life that is [chess, go, shogi] 2. ML approaches, even in computer vision, have a long way to go before anything remotely resembling child-level human intelligence 3. Therefore, can we please please stop the marketing masquerading as news articles about DeepMind’s latest result. And if anyone at DeepMind is listening: your product is pretty sweet! It would be better strategically to simply let it speak for itself, without trying to frame it as AGI. 1. http://www.paulgraham.com/submarine.html http://www.paulgraham.com/submarine.html 2. https://arxiv.org/pdf/1811.11553.pdf https://arxiv.org/pdf/1811.11553.pdf

Animats 8y ago

Has someone applied this to online poker yet? Make some real money.

Lkjhmnbv 8y ago
Heads up Texas holdem is "solved". https://www.scientificamerican.com/article/time-to-fold-humans-poker-playing-ai-beats-pros-at-texas-hold-rsquo-em https://www.scientificamerican.com/article/time-to-fold-huma...
- cepth 8y ago
  The vast majority of "action" available online is "6-max" (6 player) or "full-ring" (9 player). On a PokerStars, WSOP.com, or Party Poker, you're going to find that there are maybe 1/10th or 1/20th the number of headsup tables as higher capacity tables. The development of "GTO" (game theory optimal) play in Texas Hold 'Em is certainly a first step in the direction of computers playing poker. However, there's still quite a long way to go. Poker Snowie, one of the cutting edge "GTO" programs, is based off of NNs (https://www.pokersnowie.com/about/technology-training.html https://www.pokersnowie.com/about/technology-training.html). At the same time, there are some glaring weaknesses in the software, namely that it can only offer suggestions at specific pot size bets (0.25, 0.5, 1, 2). The authors themselves concede some other weaknesses (https://www.pokersnowie.com/about/weaknesses.html https://www.pokersnowie.com/about/weaknesses.html). Worth mentioning that Amaya, the owner of PokerStars, has posted job openings for "AI researchers" (http://www.starsgroup.com/careers/job/Poker-AI-Research-Engineer-oyBn7fwn http://www.starsgroup.com/careers/job/Poker-AI-Research-Engi... & https://www.pokernews.com/news/2017/10/pokerstars-to-hire-artificial-intelligence-researchers-29136.htm https://www.pokernews.com/news/2017/10/pokerstars-to-hire-ar...). Some people think that the position may be to help PokerStars detect/combat bot use, but others think that there may be a (arguably bleak) future where players have the option to compete against Amaya-created bots online.
- bobbiechen 8y ago
  Here's the paper about Libratus (mentioned in the article). In January 2017 Libratus beat a team of four top-10 heads- up no-limit specialist professionals in a 120,000-hand Brains vs. AI challenge match over 20 days. (PDF) https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf https://www.cs.cmu.edu/~noamb/papers/17-IJCAI-Libratus.pdf