In a chess game of the best neural network for chess vs 100 GMs at one move a day, I would pick an A.I. like Alpha Zero to win.
It is the respect for computing power that brings me to side with a neural network such as Alpha Zero. At one move per day Alpha Zero would have something like Fibonacci Series improvement and upgrades until it exhausted all of the possible moves in chess. It could approach infinity in a sense- a Cantorian trans-finite set paradigm- for learning and structuring chess moves making the human grandmasters about as competitive as 100 monkeys typing Shakespeare vs Shakespeare, in writing another Shakespearian play in 1611.
Alpha Zero’s neural network required just four hours to learn to play chess and beat SF 8? Given a 30 move game, and 30 days to compound its own interest, Alpha Zero’s improvement would be greater than that of Magnus Carlsen vs Gioachino Greco over the centuries. Humans don’t learn remotely as fast, nor have the power for simultaneous equations as a state-of-the-art neural network. Formulaic models written by 100 GMs in a day would be comparatively primitive to those invented by the best and brightest neural network.
Computers presently are up to 122.3 petaflops (IBM Summit June 2018). Neural networks including Alpha Zero I would think, may exploiting any number of computers, including super-computers. The power applied to a single chess game or match would be absurd.
One hundred GMs with about any sort of non-electronic approach calculating board positional vectors would have as little of a chance as 100 of Emperor Gao Gui Xiang Gong best suanpan (abacus) wielders versus a supercomputer at calculating numbers of rice grains in theoretical inventories of all possible Universes minus 1.
image credit: https://applied-data.science/static/main/res/alpha_go_zero_cheat_sheet.png
For GMs to beat an AI neural network, logically they would require comparable processing capability in critical areas. One might anticipate that human reasoning algorithms regarding chess might be modeled by program designers, yet not vice versa.
Simple Alpha Zero
Fritz beat World Champ Vlad Kramnik in 2006 running on a Xeon dual-core 5160 chip at 3 mgz. The chip sold for just $860. It was a very tiny chip in comparison to today’s chips used for desktops such as the 32 core AMD Threadripper chips.
Neural network programming for artificial intelligence has advanced quite a lot since the 1996–97 Deep Blue-Kasparov games. Apparently the A.I. programming was good old brute force rather than sophisticated algorithm. The A.I. designer for Deep Blue discusses that project here… https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/
I took a programming and systems analysis course in 1980, though I worked in other fields, so I understand the philosophy behind designing computer algorithms to process data reasonably well. Computers today are orders of magnitude more powerful than those that beat Kasparov and Kramnik. There is a field of study called Big Data that is remarkable to those that know nothing of it. Big Data sweep with neural networks can get all of the information there is on-line about chess rather easily
A.I. neural network designers today can use everything ever put on-line concerning chess and program a network to sort through it and process it in numerous ways to ultimately let it select the best move. Capablanca, Morphy, Pillsbury, Steinitz; every game ever played and noted online would be in its live database. Deep Blue had very limited capacity for that, and it had just two GMs help its design. With the way data is now, the question to me is could the A.I. read and convert into meaningful information every chess book in any language that it could input. It could have the help of thousands of GMs in a sense providing data and it might build chess ‘wisdom’. Those are just programming challenges that probably were already solved. Some have said that IBM may have been using Deep Blue just to test its A.I. Maybe Google and Alpha Zero were doing that too. It was a learning test.
AlphaGo Zero Explained In One Diagram – Applied Data Science – Medium
!) Deep Blue’s lead designer was an AI specialist named Murray Campbell, who spoke about his assignment by IBM to develop Deep Blue after having made Deep Thought- the first artificial intelligence project to defeat a GM. He still works in IBM’s A.I. Foundation within the Cognitive Computing Group. Deep Blue was an attention getting development project of the people developing artificial intelligence. 20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess
2-Apparently the late Charles Krauthammer shared your point of view about Deep Blue. A Forbes article comment;“ To Krauthammer, Deep Blue’s win in the 1996 match was due to “brute force” calculation, which is not artificial intelligence, he says, just faster calculation of a much wider range of possible tactical moves.”
The Brute Force Of IBM Deep Blue And Google DeepMind
Gil Press-the author of the Forbes article writes that; “Deep Blue was an example of so-called “artificial intelligence” achieved through “brute force,” the super-human calculating speed that has been the hallmark of digital computers since they were invented in the 1940s. Deep Blue was a specialized, purpose-built computer, the fastest to face a chess world champion, capable of examining 200 million moves per second, or 50 billion positions, in the three minutes allocated for a single move in a chess game.”
Rather than brute force, Alpha Zero has enhanced programming compared to that of Deep Blue. A.I. is a matter of programming code written by humans (presently) to generate calculating responses that can find their own answers to challenges. In defeating the world Go champion Alpha Go aka (Zero) trained using reinforcing techniques playing against itself.
First it was given 30 million moves that humans use in Go to develop its own game. Then it played against itself calculating moves in advance that were optimal. After that those moves formed a database that it again played against itself finding moves that human players hadn’t. An article on the Alpha Zero program that people tend to call Artificial Intelligence- that is a matter of the ability of the program and computer support to find ‘winning’ replies to specific configurations, notes that on move 37 of the game Alpha Zero calculated the odds of a human playing the move at 1 in 10,000. The higher refined data base provided very elite moves. Inside the Epic Go Tournament Where Google’s AI Came to Life
Gil Press wrote in his article about the technology that support Alpha Go. It is a lot of processing power and dwarfs the calculating power of 100 GMs laboring like a commune to find the best chess move once per day.. “AlphaGo used 1,920 Central Processing Units (CPU) and 280 Graphics Processing Units (GPU), according to The Economist, and possibly additional proprietary google Tensor Processing Units, for a lot of hardware power, plus brute force statistical analysis software (processing and analyzing lots and lots of data) known as Deep Neural Networks, or more popularly as Deep Learning.” - The Brute Force Of IBM Deep Blue And Google DeepMind
Cade Metz writes is his article on AlphaGo that “AlphaGo’s relentless superiority shows us that machines can now mimic—and indeed exceed—the kind of human intuition that drives the world’s best Go players.” Elsewhere Deep Blue was described as exploiting Kasparov with psychology, such as playing an instant move after a deep think by Kasparov to lead him to believe he “had fallen into a trap”. It is not that computers have learned to think like humans with some sort of self-awareness or cognizance that someone could believe is required for artificial intelligence. It is the case that computer programs can find better, faster answers than human to particular set criteria such as chess and Go- much better and faster even that communes or collectives of human players, when they have a certain level of expert system programming and hardware for processing power.
Human thought on certain kinds of challenges that can be modeled, can be processed faster with enhanced computer programs that were at some point written by humans. It is the equivalent of automachines beating humans in a race around a mile oval track….just the way it is.
https://www.anandtech.com/show/13124/the-amd-threadripper-2990wx-and-2950x-review
It is the respect for computing power that brings me to side with a neural network such as Alpha Zero. At one move per day Alpha Zero would have something like Fibonacci Series improvement and upgrades until it exhausted all of the possible moves in chess. It could approach infinity in a sense- a Cantorian trans-finite set paradigm- for learning and structuring chess moves making the human grandmasters about as competitive as 100 monkeys typing Shakespeare vs Shakespeare, in writing another Shakespearian play in 1611.
Alpha Zero’s neural network required just four hours to learn to play chess and beat SF 8? Given a 30 move game, and 30 days to compound its own interest, Alpha Zero’s improvement would be greater than that of Magnus Carlsen vs Gioachino Greco over the centuries. Humans don’t learn remotely as fast, nor have the power for simultaneous equations as a state-of-the-art neural network. Formulaic models written by 100 GMs in a day would be comparatively primitive to those invented by the best and brightest neural network.
Computers presently are up to 122.3 petaflops (IBM Summit June 2018). Neural networks including Alpha Zero I would think, may exploiting any number of computers, including super-computers. The power applied to a single chess game or match would be absurd.
One hundred GMs with about any sort of non-electronic approach calculating board positional vectors would have as little of a chance as 100 of Emperor Gao Gui Xiang Gong best suanpan (abacus) wielders versus a supercomputer at calculating numbers of rice grains in theoretical inventories of all possible Universes minus 1.
image credit: https://applied-data.science/static/main/res/alpha_go_zero_cheat_sheet.png
For GMs to beat an AI neural network, logically they would require comparable processing capability in critical areas. One might anticipate that human reasoning algorithms regarding chess might be modeled by program designers, yet not vice versa.
Simple Alpha Zero
Fritz beat World Champ Vlad Kramnik in 2006 running on a Xeon dual-core 5160 chip at 3 mgz. The chip sold for just $860. It was a very tiny chip in comparison to today’s chips used for desktops such as the 32 core AMD Threadripper chips.
Neural network programming for artificial intelligence has advanced quite a lot since the 1996–97 Deep Blue-Kasparov games. Apparently the A.I. programming was good old brute force rather than sophisticated algorithm. The A.I. designer for Deep Blue discusses that project here… https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/
I took a programming and systems analysis course in 1980, though I worked in other fields, so I understand the philosophy behind designing computer algorithms to process data reasonably well. Computers today are orders of magnitude more powerful than those that beat Kasparov and Kramnik. There is a field of study called Big Data that is remarkable to those that know nothing of it. Big Data sweep with neural networks can get all of the information there is on-line about chess rather easily
A.I. neural network designers today can use everything ever put on-line concerning chess and program a network to sort through it and process it in numerous ways to ultimately let it select the best move. Capablanca, Morphy, Pillsbury, Steinitz; every game ever played and noted online would be in its live database. Deep Blue had very limited capacity for that, and it had just two GMs help its design. With the way data is now, the question to me is could the A.I. read and convert into meaningful information every chess book in any language that it could input. It could have the help of thousands of GMs in a sense providing data and it might build chess ‘wisdom’. Those are just programming challenges that probably were already solved. Some have said that IBM may have been using Deep Blue just to test its A.I. Maybe Google and Alpha Zero were doing that too. It was a learning test.
AlphaGo Zero Explained In One Diagram – Applied Data Science – Medium
!) Deep Blue’s lead designer was an AI specialist named Murray Campbell, who spoke about his assignment by IBM to develop Deep Blue after having made Deep Thought- the first artificial intelligence project to defeat a GM. He still works in IBM’s A.I. Foundation within the Cognitive Computing Group. Deep Blue was an attention getting development project of the people developing artificial intelligence. 20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess
2-Apparently the late Charles Krauthammer shared your point of view about Deep Blue. A Forbes article comment;“ To Krauthammer, Deep Blue’s win in the 1996 match was due to “brute force” calculation, which is not artificial intelligence, he says, just faster calculation of a much wider range of possible tactical moves.”
The Brute Force Of IBM Deep Blue And Google DeepMind
Gil Press-the author of the Forbes article writes that; “Deep Blue was an example of so-called “artificial intelligence” achieved through “brute force,” the super-human calculating speed that has been the hallmark of digital computers since they were invented in the 1940s. Deep Blue was a specialized, purpose-built computer, the fastest to face a chess world champion, capable of examining 200 million moves per second, or 50 billion positions, in the three minutes allocated for a single move in a chess game.”
Rather than brute force, Alpha Zero has enhanced programming compared to that of Deep Blue. A.I. is a matter of programming code written by humans (presently) to generate calculating responses that can find their own answers to challenges. In defeating the world Go champion Alpha Go aka (Zero) trained using reinforcing techniques playing against itself.
First it was given 30 million moves that humans use in Go to develop its own game. Then it played against itself calculating moves in advance that were optimal. After that those moves formed a database that it again played against itself finding moves that human players hadn’t. An article on the Alpha Zero program that people tend to call Artificial Intelligence- that is a matter of the ability of the program and computer support to find ‘winning’ replies to specific configurations, notes that on move 37 of the game Alpha Zero calculated the odds of a human playing the move at 1 in 10,000. The higher refined data base provided very elite moves. Inside the Epic Go Tournament Where Google’s AI Came to Life
Gil Press wrote in his article about the technology that support Alpha Go. It is a lot of processing power and dwarfs the calculating power of 100 GMs laboring like a commune to find the best chess move once per day.. “AlphaGo used 1,920 Central Processing Units (CPU) and 280 Graphics Processing Units (GPU), according to The Economist, and possibly additional proprietary google Tensor Processing Units, for a lot of hardware power, plus brute force statistical analysis software (processing and analyzing lots and lots of data) known as Deep Neural Networks, or more popularly as Deep Learning.” - The Brute Force Of IBM Deep Blue And Google DeepMind
Cade Metz writes is his article on AlphaGo that “AlphaGo’s relentless superiority shows us that machines can now mimic—and indeed exceed—the kind of human intuition that drives the world’s best Go players.” Elsewhere Deep Blue was described as exploiting Kasparov with psychology, such as playing an instant move after a deep think by Kasparov to lead him to believe he “had fallen into a trap”. It is not that computers have learned to think like humans with some sort of self-awareness or cognizance that someone could believe is required for artificial intelligence. It is the case that computer programs can find better, faster answers than human to particular set criteria such as chess and Go- much better and faster even that communes or collectives of human players, when they have a certain level of expert system programming and hardware for processing power.
Human thought on certain kinds of challenges that can be modeled, can be processed faster with enhanced computer programs that were at some point written by humans. It is the equivalent of automachines beating humans in a race around a mile oval track….just the way it is.
https://www.anandtech.com/show/13124/the-amd-threadripper-2990wx-and-2950x-review
No comments:
Post a Comment