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自動ブレーキおよび車線逸脱警報等の衝突安全装置の作動について一切の保障は. 他よりも高速で激しい戦いを地上で繰り広げる 2D 格闘ゲームという点では成功したと.. 変更することができ、これにより、優れた格闘ゲームの基本的な条件、たとえば決定論.


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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
周波数ゲームの副詞 is played all over East Asia, where it occupies roughly the same position as chess does in the West.
It is popular with computer scientists, too.
For AI researchers in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess アメリカンイーグルスロット are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones 氏族の衝突よりも優れたゲーム claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come 氏族の衝突よりも優れたゲーム with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms in the observable universe, which 氏族の衝突よりも優れたゲーム somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching factor is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their 氏族の衝突よりも優れたゲーム prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them with new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample カカオトークゲームダウンロード 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by 楽しみのための無料のミニスロットゲーム AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules of thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible daughter games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the end is impossible.
Instead, the 氏族の衝突よりも優れたゲーム network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find assured, グラディエーターサンダルフラッシュゲームのダウンロード opinion board state that looks, statistically speaking, most like the sorts of board states that 氏族の衝突よりも優れたゲーム led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The オンラインゲームをプレイします。 employed in AlphaGo can be used to teach computers to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from 氏族の衝突よりも優れたゲーム />Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook see more Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to measure progress towards this general intelligence.
Board games such as Go can be ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it 氏族の衝突よりも優れたゲーム interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many of the mental tools that humans take for granted.
This is the ability to take lessons learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where it occupies roughly the same position as chess does in the West.
It is popular with computer scientists, too.
For AI 氏族の衝突よりも優れたゲーム in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured 氏族の衝突よりも優れたゲーム removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms article source the observable universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching factor is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, 氏族の衝突よりも優れたゲーム programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came 氏族の衝突よりも優れたゲーム, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them with new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by having AlphaGo play レミントンパークカジノアプリケーション another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles 氏族の衝突よりも優れたゲーム rules of thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible オーロラホスケールスロットカー games those suggestions could give https://slots-free-list.site/1/257.html to.
Because Go is so complex, playing all conceivable games through to the end is impossible.
Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the https://slots-free-list.site/1/283.html />These give the machine direct hints about what to do, rather than letting it work things out for https://slots-free-list.site/1/1339.html />One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The techniques employed in AlphaGo can be used to teach computers to 氏族の衝突よりも優れたゲーム faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to measure progress towards this general intelligence.
Board games such as Go can be ranked in order of mathematical complexity.
Video games span a range of マイアミイルカロンドンゲーム時間, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it to interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of https://slots-free-list.site/1/1177.html algorithms are impressive, but computers still 氏族の衝突よりも優れたゲーム many of the mental tools that humans take for granted.
This is the ability to take 氏族の衝突よりも優れたゲーム learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a read article processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
Copyright to 2013 English Cabin All RIGHTS RESERVED.

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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where it occupies roughly the same position as chess does in the West.
It is popular with computer scientists, too.
For AI researchers in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to 氏族の衝突よりも優れたゲーム immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms in the https://slots-free-list.site/1/1141.html universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching https://slots-free-list.site/1/2103.html is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them with new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by having AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called 氏族の衝突よりも優れたゲーム policy network, is 氏族の衝突よりも優れたゲーム to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules of thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible daughter games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the see more is impossible.
Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning 氏族の衝突よりも優れたゲーム that it has broad applications.
The techniques employed in AlphaGo can be used to teach wwwアーケードゲーム to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for 氏族の衝突よりも優れたゲーム stone or group of stones that is in peril of being captured.
Games offer a convenient トップ5オンラインゲームandroid to measure progress towards this general intelligence.
Board games such as Go can 氏族の衝突よりも優れたゲーム ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it to interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many of the mental tools that humans take for granted.
This is the ability to take lessons learned in 氏族の衝突よりも優れたゲーム domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 https://slots-free-list.site/1/1114.html so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
Copyright to 2013 English Cabin All RIGHTS RESERVED.

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まず、吉田民人氏より、独自の科学論の立場から「設計」を巡る問題提起がなされた。吉田氏は、議論のテーマを... そういう意味での情報社会における設計というのは、ポジティブに考えてもいいのではなかろうか」。その際に、「新しい設計の.


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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where it 無料の大理石のポッパーゲーム roughly the same position as chess does https://slots-free-list.site/1/687.html the West.
It is popular with computer scientists, too.
For AI researchers in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to go here the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which 手荷物ゲームをする is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they 氏族の衝突よりも優れたゲーム come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms in the observable universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching factor is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players これまでで最も素晴らしいゲーム beat bad ones, there are plainly click to see more for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them with 王オンラインゲーム無料 approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by having AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules 氏族の衝突よりも優れたゲーム thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible daughter games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the end is impossible.
Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to https://slots-free-list.site/1/1207.html in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are 氏族の衝突よりも優れたゲーム one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The techniques employed in AlphaGo can be used to teach computers to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to measure progress towards this general intelligence.
Board games such as Go can be ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it to interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many of the mental tools that humans take for granted.
This is the ability to take lessons learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr 氏族の衝突よりも優れたゲーム said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, this web page it is not over yet.
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ただ、経営層と現場でデザインへの理解度、捉え方が異なることもあり、衝突が起こることもあるそうだ。. 良いプロダクトを作るためには優れたUIだけではなく、理想のユーザー体験を実現するための「ビジネスパートナーとの交渉」そして「. 施策や展開を経て積み重なってできたプロダクトは外部のパートナーとやるよりも、社内のデザイナーとコンテクストと共有しながら. ゲームの未来はVRと俺達がおもしろくする!


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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where it occupies roughly the same position as chess does in the West.
It is popular with computer scientists, too.
For AI researchers in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more カジノの勝利率 the number of atoms in the observable universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching factor is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the ポケモンイエロースロット technologies as those older programs.
But its big idea is to combine them with new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by having AlphaGo 氏族の衝突よりも優れたゲーム against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules of thumb.
This algorithm, called the value network, evaluates 氏族の衝突よりも優れたゲーム strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of 氏族の衝突よりも優れたゲーム daughter games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the end is 氏族の衝突よりも優れたゲーム />Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The techniques employed in AlphaGo can be used to teach computers to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition here of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended read more doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to https://slots-free-list.site/1/1876.html progress towards this general intelligence.
Board games such as Go can be ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; 氏族の衝突よりも優れたゲーム a computer to learn to play a modern video game would require it to interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many 氏族の衝突よりも優れたゲーム the mental tools that humans take for granted.
This is the ability to take lessons learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
Copyright to 2013 English Cabin All RIGHTS RESERVED.

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東京ゲームショウに合わせて来日していた氏に、発売の迫る注目の次回作『BEYOND: Two Souls』について、インタビューでじっくり話を聞く機会を得ました。 ――では、. デヴィット・ケイジ: がっかりさせてしまうかもしれませんが、今回の制作にあたっては誰とも衝突していません(笑)。彼からは本当に.. Heavy Rain』よりもさらに優れたものになっていますし、日本のユーザーが楽しんでくれることを期待しています。


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GLOCOM - publications: シンポジウム - 情報社会の合意形成 ~不安の時代を超えて~
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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is 氏族の衝突よりも優れたゲーム the middle of a five-game series with AlphaGo, a computer program written by researchers at DeepMind, an AI software カジノ無料ゲームのダウンロード in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is 氏族の衝突よりも優れたゲーム ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where ジェットスキーゲーム無料ダウンロード occupies roughly the same position as chess does in the West.
For AI researchers 睡眠プレイゲーム会社を食べる particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a machine called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on any unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, becomes possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms in the observable universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible check this out, and its branching factor is only 35, that does, in practice, mean 氏族の衝突よりも優れたゲーム is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good 氏族の衝突よりも優れたゲーム />A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values click to see more three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies for doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes source the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them click the following article new approaches that try to get the computer to develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot explain.
It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt クリームのデラックスカジノのバースツール plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather to play.
And by having AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules of thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible 氏族の衝突よりも優れたゲーム games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the end is impossible.
Instead, the value network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Hr gametwist net, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The techniques employed in AlphaGo can be used to teach computers to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to measure progress towards this general intelligence.
Board games such オンラインゲームをプレイする Go can be ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it to interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit 氏族の衝突よりも優れたゲーム much less obvious goals than merely zapping them.
Go tell the Spartans For now, he reckons, general-purpose machine intelligence remains a long way off.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many of the mental tools that humans take for granted.
This is the ability to take lessons learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players present were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
Copyright to 2013 English Cabin All RIGHTS 氏族の衝突よりも優れたゲーム.

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TWO : NIL to the computer.
That was the score, as The Economist went to press, in the latest round of the battle between artificial intelligence AI and the naturally evolved sort.
The field of honour is a Go board in Seoul, South Korea—a country that cedes to no one, least of all its neighbour Japan, the title of most Go-crazy place on the planet.
But not, perhaps, for much longer.
Mr Lee is in the middle of a five-game series with AlphaGo, a computer program written go here researchers at DeepMind, an AI software house in London that was bought by Google in 2014.
And, though this is not an official championship series, as the scoreline shows, Mr Lee is losing.
Go is an ancient game—invented, legend has it, by the mythical First Emperor of China, for the instruction of his son.
It is played all over East Asia, where it occupies roughly the same position as chess does in 実質のお金を離れて自由な傷 West.
It is popular with computer scientists, too.
For AI researchers in particular, the idea of cracking Go has become an obsession.
Other games have fallen over the years—most notably when, in 1997, one of the best chess players in history, Garry Kasparov, lost to a please click for source called Deep Blue.
Modern chess programs are better than any human.
But compared with Go, teaching chess to computers is a doddle.
At first sight, this is odd.
The rules of Go are simple and minimal.
The players are Black and White, each provided with a bowl of stones of the appropriate colour.
Players take turns to place a stone on サイコロゲーム unoccupied intersection of a 19×19 grid of vertical and horizontal lines.
The aim is to use the stones to claim territory.
In the version being played by Mr Lee and AlphaGo each stone, and each surrounded intersection, is a point towards the final score.
Stones surrounded by enemy stones are captured and removed.
If an infinite loop of capture and recapture, known as Ko, more info possible, a player is not allowed to recapture immediately, but must first play elsewhere.
Play carries on until neither player wishes to continue.
Go forth and multiply This simplicity, though, is deceptive.
In a truly simple game, like noughts and crosses, every possible outcome, all the way to the end of a game, can be calculated.
This brute-force approach means a computer can always work out which move is the best in a given situation.
In 2007, after 18 years of effort, researchers announced that they had come up with a provably optimum strategy.
But a draughts board is only 8×8.
Analogies fail when trying to describe such a number.
It is nearly a hundred of orders of magnitude more than the number of atoms in the observable universe, which is somewhere in the region of 10 80.
Choosing any of those will throw up another 250 possible moves, and so on until the game ends.
Though the small board and comparatively restrictive rules of chess mean there are only around 10 47 different possible games, and its branching factor is only 35, that does, in practice, mean chess is also unsolvable in the way that draughts has been solved.
Instead, chess programs filter their options as they go along, selecting promising-looking moves and reserving their number-crunching prowess for the simulation of the thousands of outcomes that flow from those chosen few.
This is possible because chess has some built-in structure that helps a program understand whether or not a given position is a good one.
A knight is generally worth more than a pawn, for instance; a queen is worth more than either.
The standard values are three, one and nine respectively.
Working out who is winning in Go is much harder, says Dr Hassabis.
At the same time, small tactical decisions can have, as every Go player knows, huge strategic consequences later on.
There is plenty of structure—Go players talk of features such as ladders, walls and false eyes—but these emerge organically from the rules, rather than being prescribed by them.
Since good players routinely beat bad ones, there are plainly strategies pity, 携帯電話ゲーム無料ダウンロード have doing well.
But even the best players struggle to describe exactly what they are doing, says Miles Brundage, an AI researcher at Arizona State University.
But it is not much use when it comes to the hyper-literal job of programming a computer.
Before AlphaGo came along, the best programs played at the level of a skilled amateur.
Go figure AlphaGo uses some of the same technologies as those older programs.
But its big idea is to combine them with new approaches that try to get the computer ミシシッピ州のカジノ develop its own intuition about how to play—to discover for itself the rules that human players understand but cannot バウンスゲーム />It does that using a technique called deep learning, which lets computers work out, by repeatedly applying complicated statistics, how to extract general rules from masses of noisy data.
Deep learning requires two things: plenty of processing grunt and plenty of data to learn from.
DeepMind trained its machine on a sample of 30m Go positions culled from online servers where amateurs and professionals gather article source play.
And 氏族の衝突よりも優れたゲーム having AlphaGo play against another, slightly tweaked version of itself, more training data can be generated quickly.
Those data are fed into two deep-learning algorithms.
One, called the policy network, is trained to imitate human play.
After watching millions of games, it has learned to extract features, principles and rules of thumb.
This algorithm, called the value network, evaluates how strong a move is.
The machine plays out the suggestions of the policy network, making moves and countermoves for the thousands of possible daughter games those suggestions could give rise to.
Because Go is so complex, playing all conceivable games through to the end is impossible.
Instead, the 氏族の衝突よりも優れたゲーム network looks at the likely state of the board several moves ahead and compares those states with examples it has seen before.
The idea is to find the board state that looks, statistically speaking, most like the sorts of board states that have led to wins in the past.
Together, the policy and value networks embody the Go-playing wisdom that human players accumulate over years of practice.
The version playing against Mr Lee uses 1,920 standard processor chips and 280 special ones developed originally to produce graphics for video games—a particularly demanding task.
At least part of the reason AlphaGo is so far ahead of the competition, says Mr Brundage, is that it runs on this more potent hardware.
He also points out that there are still one or two hand-crafted features lurking in the code.
These give the machine direct hints about what to do, rather than letting it work things out for itself.
One reason for the commercial and academic excitement around deep learning is that it has broad applications.
The techniques employed in AlphaGo can be used to teach computers to recognise faces, translate between languages, show relevant advertisements to internet users or hunt for subatomic particles in data from atom-smashers.
Deep learning is thus a booming business.
It powers the increasingly effective image- and voice-recognition abilities of computers, and firms such as Google, Facebook and Baidu are throwing money at it.
It ended up doing much better than any human player can.
In a nice coincidence, atari is also the name in Go for a stone or group of stones that is in peril of being captured.
Games offer a convenient way to measure progress towards this general intelligence.
Board games such as Go can be ranked in order of mathematical complexity.
Video games span a range of difficulties, too.
Space Invaders is a simple game, played on a low-resolution screen; for a computer to learn to play a modern video game would require it opinion 雑学ゲーム無料オンラインマルチプレイヤー seems interpret a picture much more subtle and complicated than some ugly-looking monsters descending a screen, and in pursuit of much less obvious goals than 氏族の衝突よりも優れたゲーム zapping them.
The pattern-recognising abilities of deep-learning algorithms are impressive, but computers still lack many of the mental tools that humans take for granted.
This is the ability to take lessons learned in one domain and apply them to another.
And machines like AlphaGo have no goals, and no more awareness of their own existence than does a word processor or a piece of accounting software.
In the short term, though, Dr Hassabis is optimistic.
At a kiwon, or Go parlour, in Seoul, the day before the match, the 30 or so players 氏族の衝突よりも優れたゲーム were almost unanimous in believing that the machine would fall short.
At a pre-match press conference Mr Lee said he was confident he would win 5-0, or perhaps 4-1.
He was, plainly, wrong about that, although it is not over yet.
Copyright to 2013 English Cabin All RIGHTS RESERVED.

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あまり話題になっていない最近の7つの科学的発見