Poker Ai Neural Network
2021年11月22日Register here: http://gg.gg/wza7v
For all of its power and promise, artificial intelligence has some big drawbacks — its carbon footprint is one of them.
*AI Neural Network Learns When It Should Not Be Trusted. November, 23, 2020 - 17:04. “Neural networks are really good at knowing the right answer 99 percent of the time”.
*The power of quantum neural networks. ∙ by Amira Abbas, et al. Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability.
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images. Convolutional neural networks are widely used in computer vision and have become the state of the art for many visual applications such as image classification, and have also found success in natural language processing for text classification.
*The mathematical breakthrough helps AI applications like speech recognition, gesture recognition, and ECG classification to become a hundred to a thousand times more energy efficient
For all of its power and promise, artificial intelligence has some big drawbacks — its massive carbon footprint is one of them.
Training a ‘regular’ AI using a single high-performance graphics card produces the same amount of carbon as a flight across the United States, according to MIT Technology Review. That’s because AI requires so much data. All of it must be captured, stored, analyzed, and sent out, and this requires vast amounts of processing power. Data centers require more servers, larger footprints, and cooling.
As the tech industry continues to advance in applications of AI, and consumers and enterprise take for granted the results in better products and services, there is growing pressure to address and tackle AI’s environmental impact.READ NEXTGrowing tech markets: From chips to data to energyEnergy-efficient AI
In the hopes of reducing that damage, researchers at the Centrum Wiskunde & Informatica (CWI), the Dutch national research center for mathematics and computer science, and IMEC/Holst Research Center from Eindhoven in the Netherlands, have successfully developed a learning algorithm for spiking neural networks (SNNs).
The mathematical breakthrough published in a paper catchily entitled ‘Effective and Efficient Computation with Multiple-Timescale Spiking Recurrent Neural Network’, helps AI applications like speech recognition, gesture recognition, and ECG classification to become up to a thousand times more energy efficient.
These breakthroughs, said researcher and professor of cognitive neurobiology, Sander Bohté, make AI algorithms “a thousand times more energy efficient in comparison with standard neural networks, and a factor hundred more energy efficient than current state-of-the-art neural networks.”Brain-inspired AI
SNNs are artificial neural networks that more closely mimic natural neural networks or the way the human brain processes information. In the past, computers have imitated the brain’s neuronal networks to produce applications like image recognition, speech recognition, automatic translation, to medical diagnoses. But, so far, they have lacked in efficiency, and have required up to a million times more energy than the human brain.
SNNs require a lot less frequency in communication and involve minimum calculations for performing a task.
“The communication between neurons in classical neural networks is continuous and easy to handle from a mathematical perspective. Spiking neurons look more like the human brain and communicate only sparingly and with short pulses. This, however, means that the signals are discontinuous and much more difficult to handle mathematically,” added Bohté.YOU MIGHT LIKE
INNOVATIONHow tech companies are moving into the energy sector
As of today, Bohté’s methods are capable of training spiking neural networks comprised of up to a few thousand neurons. Typically less than classical neural networks, the new method is still sufficient for many applications like speech recognition, ECG classification, and the recognition of gestures. The next challenge facing Bohté and his team of researchers will be to further expand the application possibilities and scale up these networks to one hundred thousand or a million neurons.
The underlying mathematical algorithms have been made available open-source, while prototypes for the new types of chips necessary to run spiking neural networks are already in development.
The environmental impact of AI and data is no secret. The latest breakthrough by researchers looks set to bring about more efficient tools readily available and primed for further development. At the same time, this alternative could enable the running of applications on small AI devices like chips or smartwatches with less need for cloud intervention.
15 December 2020
14 December 2020
14 December 2020
Expert-Level Artificial Intelligence in Heads-Up No-Limit PokerLinks
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Downloads & Videos | Media Contact
DeepStack bridges the gap between AI techniques for games of perfect information—like checkers, chess and Go—with ones for imperfect information games–like poker–to reason while it plays using “intuition” honed through deep learning to reassess its strategy with each decision.
With a study completed in December 2016 and published in Science in March 2017, DeepStack became the first AI capable of beating professional poker players at heads-up no-limit Texas hold’em poker.
DeepStack computes a strategy based on the current state of the game for only the remainder of the hand, not maintaining one for the full game, which leads to lower overall exploitability.
DeepStack avoids reasoning about the full remaining game by substituting computation beyond a certain depth with a fast-approximate estimate. Automatically trained with deep learning, DeepStack’s “intuition” gives a gut feeling of the value of holding any cards in any situation.
DeepStack considers a reduced number of actions, allowing it to play at conventional human speeds. The system re-solves games in under five seconds using a simple gaming laptop with an Nvidia GPU.Neural Network SoftwareThe first computer program to outplay human professionals at heads-up no-limit Hold’em poker
In a study completed December 2016 and involving 44,000 hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical significance. Over all games played, DeepStack won 49 big blinds/100 (always folding would only lose 75 bb/100), over four standard deviations from zero, making it the first computer program to beat professional poker players in heads-up no-limit Texas hold’em poker.Games are serious business
Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact. AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn’t have (your cards).
Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to play. However, to make this approach feasible in heads-up no-limit Texas hold’em—a game with vastly more unique situations than there are atoms in the universe—a simplified abstraction of the game is often needed.A fundamentally different approach
DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games.
At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction.
During re-solving, DeepStack doesn’t need to reason about the entire remainder of the game because it substitutes computation beyond a certain depth with a fast approximate estimate, DeepStack’s ’intuition’ – a gut feeling of the value of holding any possible private cards in any possible poker situation.
Finally, DeepStack’s intuition, much like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations.
DeepStack is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up no-limit poker with statistical significance.DownloadPaper & SupplementsHand HistoriesMembers (Front-back)Neural Networks Pdf
Rooty hill rsl apl poker. Michael Bowling, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Viliam Lisý, Martin Schmid, Matej Moravčík, Neil Burch Online gambling paypal accepted.low-variance Evaluation
The performance of DeepStack and its opponents was evaluated using AIVAT, a provably unbiased low-variance technique based on carefully constructed control variates. Thanks to this technique, which gives an unbiased performance estimate with 85% reduction in standard deviation, we can show statistical significance in matches with as few as 3,000 games.Abstraction-based Approaches
Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play. While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation.Professional Matches
We evaluated DeepStack by playing it against a pool of professional poker players recruited by the International Federation of Poker. 44,852 games were played by 33 players from 17 countries. Eleven players completed the requested 3,000 games with DeepStack beating all but one by a statistically-significant margin. Over all games played, DeepStack outperformed players by over four standard deviations from zero.
Heuristic SearchDoom Neural Ai
At a conceptual level, DeepStack’s continual re-solving, “intuitive” local search and sparse lookahead trees describe heuristic search, which is responsible for many AI successes in perfect information games. Poker odds calculator holdem. Until DeepStack, no theoretically sound application of heuristic search was known in imperfect information games.’,’resolveObject’:’,’resolvedBy’:’manual’,’resolved’:true}’>Ai Neural Network Story Writer’,’resolvedBy’:’manual’,’resolved’:true}’>’,’resolveObject’:’,’resolvedBy’:’manual’,’resolved’:true}’>
Register here: http://gg.gg/wza7v
https://diarynote.indered.space
For all of its power and promise, artificial intelligence has some big drawbacks — its carbon footprint is one of them.
*AI Neural Network Learns When It Should Not Be Trusted. November, 23, 2020 - 17:04. “Neural networks are really good at knowing the right answer 99 percent of the time”.
*The power of quantum neural networks. ∙ by Amira Abbas, et al. Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability.
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images. Convolutional neural networks are widely used in computer vision and have become the state of the art for many visual applications such as image classification, and have also found success in natural language processing for text classification.
*The mathematical breakthrough helps AI applications like speech recognition, gesture recognition, and ECG classification to become a hundred to a thousand times more energy efficient
For all of its power and promise, artificial intelligence has some big drawbacks — its massive carbon footprint is one of them.
Training a ‘regular’ AI using a single high-performance graphics card produces the same amount of carbon as a flight across the United States, according to MIT Technology Review. That’s because AI requires so much data. All of it must be captured, stored, analyzed, and sent out, and this requires vast amounts of processing power. Data centers require more servers, larger footprints, and cooling.
As the tech industry continues to advance in applications of AI, and consumers and enterprise take for granted the results in better products and services, there is growing pressure to address and tackle AI’s environmental impact.READ NEXTGrowing tech markets: From chips to data to energyEnergy-efficient AI
In the hopes of reducing that damage, researchers at the Centrum Wiskunde & Informatica (CWI), the Dutch national research center for mathematics and computer science, and IMEC/Holst Research Center from Eindhoven in the Netherlands, have successfully developed a learning algorithm for spiking neural networks (SNNs).
The mathematical breakthrough published in a paper catchily entitled ‘Effective and Efficient Computation with Multiple-Timescale Spiking Recurrent Neural Network’, helps AI applications like speech recognition, gesture recognition, and ECG classification to become up to a thousand times more energy efficient.
These breakthroughs, said researcher and professor of cognitive neurobiology, Sander Bohté, make AI algorithms “a thousand times more energy efficient in comparison with standard neural networks, and a factor hundred more energy efficient than current state-of-the-art neural networks.”Brain-inspired AI
SNNs are artificial neural networks that more closely mimic natural neural networks or the way the human brain processes information. In the past, computers have imitated the brain’s neuronal networks to produce applications like image recognition, speech recognition, automatic translation, to medical diagnoses. But, so far, they have lacked in efficiency, and have required up to a million times more energy than the human brain.
SNNs require a lot less frequency in communication and involve minimum calculations for performing a task.
“The communication between neurons in classical neural networks is continuous and easy to handle from a mathematical perspective. Spiking neurons look more like the human brain and communicate only sparingly and with short pulses. This, however, means that the signals are discontinuous and much more difficult to handle mathematically,” added Bohté.YOU MIGHT LIKE
INNOVATIONHow tech companies are moving into the energy sector
As of today, Bohté’s methods are capable of training spiking neural networks comprised of up to a few thousand neurons. Typically less than classical neural networks, the new method is still sufficient for many applications like speech recognition, ECG classification, and the recognition of gestures. The next challenge facing Bohté and his team of researchers will be to further expand the application possibilities and scale up these networks to one hundred thousand or a million neurons.
The underlying mathematical algorithms have been made available open-source, while prototypes for the new types of chips necessary to run spiking neural networks are already in development.
The environmental impact of AI and data is no secret. The latest breakthrough by researchers looks set to bring about more efficient tools readily available and primed for further development. At the same time, this alternative could enable the running of applications on small AI devices like chips or smartwatches with less need for cloud intervention.
15 December 2020
14 December 2020
14 December 2020
Expert-Level Artificial Intelligence in Heads-Up No-Limit PokerLinks
Twitch | YouTube | Twitter
Downloads & Videos | Media Contact
DeepStack bridges the gap between AI techniques for games of perfect information—like checkers, chess and Go—with ones for imperfect information games–like poker–to reason while it plays using “intuition” honed through deep learning to reassess its strategy with each decision.
With a study completed in December 2016 and published in Science in March 2017, DeepStack became the first AI capable of beating professional poker players at heads-up no-limit Texas hold’em poker.
DeepStack computes a strategy based on the current state of the game for only the remainder of the hand, not maintaining one for the full game, which leads to lower overall exploitability.
DeepStack avoids reasoning about the full remaining game by substituting computation beyond a certain depth with a fast-approximate estimate. Automatically trained with deep learning, DeepStack’s “intuition” gives a gut feeling of the value of holding any cards in any situation.
DeepStack considers a reduced number of actions, allowing it to play at conventional human speeds. The system re-solves games in under five seconds using a simple gaming laptop with an Nvidia GPU.Neural Network SoftwareThe first computer program to outplay human professionals at heads-up no-limit Hold’em poker
In a study completed December 2016 and involving 44,000 hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical significance. Over all games played, DeepStack won 49 big blinds/100 (always folding would only lose 75 bb/100), over four standard deviations from zero, making it the first computer program to beat professional poker players in heads-up no-limit Texas hold’em poker.Games are serious business
Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact. AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn’t have (your cards).
Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to play. However, to make this approach feasible in heads-up no-limit Texas hold’em—a game with vastly more unique situations than there are atoms in the universe—a simplified abstraction of the game is often needed.A fundamentally different approach
DeepStack is the first theoretically sound application of heuristic search methods—which have been famously successful in games like checkers, chess, and Go—to imperfect information games.
At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction.
During re-solving, DeepStack doesn’t need to reason about the entire remainder of the game because it substitutes computation beyond a certain depth with a fast approximate estimate, DeepStack’s ’intuition’ – a gut feeling of the value of holding any possible private cards in any possible poker situation.
Finally, DeepStack’s intuition, much like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations.
DeepStack is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up no-limit poker with statistical significance.DownloadPaper & SupplementsHand HistoriesMembers (Front-back)Neural Networks Pdf
Rooty hill rsl apl poker. Michael Bowling, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Viliam Lisý, Martin Schmid, Matej Moravčík, Neil Burch Online gambling paypal accepted.low-variance Evaluation
The performance of DeepStack and its opponents was evaluated using AIVAT, a provably unbiased low-variance technique based on carefully constructed control variates. Thanks to this technique, which gives an unbiased performance estimate with 85% reduction in standard deviation, we can show statistical significance in matches with as few as 3,000 games.Abstraction-based Approaches
Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play. While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation.Professional Matches
We evaluated DeepStack by playing it against a pool of professional poker players recruited by the International Federation of Poker. 44,852 games were played by 33 players from 17 countries. Eleven players completed the requested 3,000 games with DeepStack beating all but one by a statistically-significant margin. Over all games played, DeepStack outperformed players by over four standard deviations from zero.
Heuristic SearchDoom Neural Ai
At a conceptual level, DeepStack’s continual re-solving, “intuitive” local search and sparse lookahead trees describe heuristic search, which is responsible for many AI successes in perfect information games. Poker odds calculator holdem. Until DeepStack, no theoretically sound application of heuristic search was known in imperfect information games.’,’resolveObject’:’,’resolvedBy’:’manual’,’resolved’:true}’>Ai Neural Network Story Writer’,’resolvedBy’:’manual’,’resolved’:true}’>’,’resolveObject’:’,’resolvedBy’:’manual’,’resolved’:true}’>
Register here: http://gg.gg/wza7v
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