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I consider AlphaGo - The Movie [1] to be a timeless classic that will never feel outdated. In my opinion, it surpasses even Hollywood productions, despite being based on true
Www replay poker com. I consider AlphaGo - The Movie [1] to be a timeless classic that will never feel outdated.
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In my opinion, it surpasses even Hollywood productions, despite being based on true events and filmed live with real people. I'm ranking it as #2, though, because I still believe Steve Jobs' 2007 iPhone presentation [2] is the greatest live tech event ever captured on film. Hearing the crowd screaming when seeing some tricks and techniques ( eg. slide to unlock , pinch to zoom and scrolling up ) on how to use the phone does really triggers some haptic feedback in my heart because we are now so used to these tricks that were pure magic back then. There are a lot of parallels between rule-based games like Go and rule-based formal systems like ZFC. It’s interesting that the same techniques used for AlphaGo have not worked nearly as well for finding proofs of famous open problems that we suspect are both 1) decidable within ZFC and 2) have a “reasonable” minimal proof length. What aspect of efficiently exploring the combinatorial explosion in possibilities of iterated rule-based systems is the human brain still currently doing much better than machines? Well, Bridge remains unconquered, although it is unclear whether it is because of disinterest or incapability. As I have highlighted before, the day a computer false-cards will be the day. (False-carding - playing a certain card with the primary intention of deceiving the opponents and forcing an error) Does Bridge have card draw from a randomized deck? Because that's most likely the issue. I'm facing similar problems when trying to build something that plays Magic The Gathering like games reasonably competent. The combinatorics explosion and dealing with bluffing/hidden knowledge is really a tough nut to crack. My current guess is that you need something like monte carlo reinforcement learning to do it. Forcing an Error is an especially hard case because in machine vs machine matches both sides would be aware that something could force an error and would therefore not fall for it. (To be fair, re (card) games: I'm also only interested in seeing Cyborg-on-Cyborg action. Lee vs a-G almost qualified
I need to ask, when playing against AI players in poker games, are they fair (= work on the same sets of cards, are not aware of your hand) or do they get to cheat? (I played a MTG game years ago and it was not fair, the opponent's deck was not shuffled but they always had cards that provided a certain experience) They are indeed fair. The strongest poker bots are not AI in the way it is commonly defined. From my understanding they calculate the nash-equilibrium for a simplified game and extrapolate that to the full game. Coincidentally, I just watched the hour long documentary that DeepMind made about the match [1]. It talks a lot about the two moves - though not really in detail. To a non-go player like myself, both moves 37 and 78 seemed completely arbitrary. I mean, much of the video talks about how it's impossible to calculate all the future moves like in chess, yet move 37 of a possible ~300 move game is called out as genius, and move 78 is a God Hand. For the layman like myself, it seemed a bit inconsistent. The thing that made me smile was how history repeated itself. Sedol predicted a 5-0 win against the program. Kasparov was pretty cocky as well in the 1990s. You'd think someone would have warned him! Hey Sedol. Cool your jets, these guys wouldn't be spending so much money just to embarrass themselves." DeepMind was definitely way more polite than IBM, so that was good to see. The Deep Blue team were sorta jerks to Gary. > I mean, much of the video talks about how it's impossible to calculate all the future moves like in chess, yet move 37 of a possible ~300 move game is called out as genius, and move 78 is a God Hand. Every move is a choice of ~300 possibilities, and you need to calculate far ahead to know if it's a good move or not, so the number of choices you have to explore is much greater than what it seems. Interesting that Lee Sedol losing at Go was the big opening act in the modern AI wave, but it ended up coming from a completely different technology that has effectively faded into the background. They used deep neural networks, reinforcement learning, and Monte Carlo tree search. All except the MCTS are critical components of modern LLMs. MCTS is a form of planning which you can argue has parallels to "reasoning" models, although that's pretty tenuous I admit. If by that you mean reinforcement learning, that's not the case, e.g. see https://arxiv.org/abs/2501.12948. modern post-training uses RL and immense amounts of synthetic data to iteratively bootstrap better performance.
Www replay poker com
Artículo sobre www replay poker com:
I consider AlphaGo - The Movie [1] to be a timeless classic that will never feel outdated. In my opinion, it surpasses even Hollywood productions, despite being based on true
Www replay poker com. I consider AlphaGo - The Movie [1] to be a timeless classic that will never feel outdated.
Haga clic aquí para Www replay poker com
In my opinion, it surpasses even Hollywood productions, despite being based on true events and filmed live with real people. I'm ranking it as #2, though, because I still believe Steve Jobs' 2007 iPhone presentation [2] is the greatest live tech event ever captured on film. Hearing the crowd screaming when seeing some tricks and techniques ( eg. slide to unlock , pinch to zoom and scrolling up ) on how to use the phone does really triggers some haptic feedback in my heart because we are now so used to these tricks that were pure magic back then. There are a lot of parallels between rule-based games like Go and rule-based formal systems like ZFC. It’s interesting that the same techniques used for AlphaGo have not worked nearly as well for finding proofs of famous open problems that we suspect are both 1) decidable within ZFC and 2) have a “reasonable” minimal proof length. What aspect of efficiently exploring the combinatorial explosion in possibilities of iterated rule-based systems is the human brain still currently doing much better than machines? Well, Bridge remains unconquered, although it is unclear whether it is because of disinterest or incapability. As I have highlighted before, the day a computer false-cards will be the day. (False-carding - playing a certain card with the primary intention of deceiving the opponents and forcing an error) Does Bridge have card draw from a randomized deck? Because that's most likely the issue. I'm facing similar problems when trying to build something that plays Magic The Gathering like games reasonably competent. The combinatorics explosion and dealing with bluffing/hidden knowledge is really a tough nut to crack. My current guess is that you need something like monte carlo reinforcement learning to do it. Forcing an Error is an especially hard case because in machine vs machine matches both sides would be aware that something could force an error and would therefore not fall for it. (To be fair, re (card) games: I'm also only interested in seeing Cyborg-on-Cyborg action. Lee vs a-G almost qualified
I need to ask, when playing against AI players in poker games, are they fair (= work on the same sets of cards, are not aware of your hand) or do they get to cheat? (I played a MTG game years ago and it was not fair, the opponent's deck was not shuffled but they always had cards that provided a certain experience) They are indeed fair. The strongest poker bots are not AI in the way it is commonly defined. From my understanding they calculate the nash-equilibrium for a simplified game and extrapolate that to the full game. Coincidentally, I just watched the hour long documentary that DeepMind made about the match [1]. It talks a lot about the two moves - though not really in detail. To a non-go player like myself, both moves 37 and 78 seemed completely arbitrary. I mean, much of the video talks about how it's impossible to calculate all the future moves like in chess, yet move 37 of a possible ~300 move game is called out as genius, and move 78 is a God Hand. For the layman like myself, it seemed a bit inconsistent. The thing that made me smile was how history repeated itself. Sedol predicted a 5-0 win against the program. Kasparov was pretty cocky as well in the 1990s. You'd think someone would have warned him! Hey Sedol. Cool your jets, these guys wouldn't be spending so much money just to embarrass themselves." DeepMind was definitely way more polite than IBM, so that was good to see. The Deep Blue team were sorta jerks to Gary. > I mean, much of the video talks about how it's impossible to calculate all the future moves like in chess, yet move 37 of a possible ~300 move game is called out as genius, and move 78 is a God Hand. Every move is a choice of ~300 possibilities, and you need to calculate far ahead to know if it's a good move or not, so the number of choices you have to explore is much greater than what it seems. Interesting that Lee Sedol losing at Go was the big opening act in the modern AI wave, but it ended up coming from a completely different technology that has effectively faded into the background. They used deep neural networks, reinforcement learning, and Monte Carlo tree search. All except the MCTS are critical components of modern LLMs. MCTS is a form of planning which you can argue has parallels to "reasoning" models, although that's pretty tenuous I admit. If by that you mean reinforcement learning, that's not the case, e.g. see https://arxiv.org/abs/2501.12948. modern post-training uses RL and immense amounts of synthetic data to iteratively bootstrap better performance. Www replay poker com