Another victory for Deep Mind: after chess and artificial intelligence have conquered StarCraft

In November 2017, that is a little more than a year ago, we wrotethat artificial intelligence is not yet able to beat professional players in StarCraft. But less than a year, as this barrier was taken. Last month in London, the team from the English divisions of the study of artificial intelligence DeepMind quietly laid a new cornerstone in the battle between people and computers. On Thursday she revealed that achievement in the three-hour stream on YouTube, in which humans and robots fought for life and death.

DeepMind beat people at StarCraft

Stream DeepMind revealed that her robot with artificial intelligence AlphaStar wins professional player in a complex real-time strategy (RTS) StarCraft II. Champion of mankind, the 25-year-old Grzegorz Coments from Poland flew with the score 5:0. It seems that the software for machine learning found the strategy unknown to professionals who compete for millions in prizes, which are issued annually in one of the most lucrative for the world eSports games.

“It was not like any StarCraft, which I played,” said Comins known professional under the name MaNa.

Feat DeepMind is the most complex in the long chain of events that computers impose on the best from the world of people in games and I won. Checkers fell in 1994, chess in 1997, in 2016 AlphaGo won the game. Robot StarCraft is the most powerful player of the world of artificial intelligence; and his arrival was awaited.

AlphaStar appeared about six years ago in the history of machine learning. Although the victory AlphaGo in 2016 has been overwhelming — the th experts believed that this moment would come at least ten years later — a victory AlphaStar seems more or less arrived on schedule. To date, it is clear that with enough data and computing power, machine learning can cope with a complex but specific problems.

Mark Riedl, associate Professor at Georgia Institute of Technology, found the news of Thursday’s thrilling, but not amazing. “We have already reached that point, so it was only a matter of time. In a sense, defeat people in games has become boring.”

Video games like StarCraft are mathematically more complicated than chess or go. The number of valid positions on the go Board is a unit with 170 zeros and the equivalent in StarCraft is estimated as 1 with 270 zeros, not less. The creation and management of military units in StarCraft requires players to select and perform many other actions and making decisions without the ability to see every move of the opponent.

DeepMind has predolin these steep barriers with powerful chips TPU, which Google invented to increase the power of machine learning. The company adapted the algorithmsdeveloped for text processing are under the task of determining actions on the battlefield which lead to victory. AlphaStar studied in StarCraft on the records of half a million games between people, then played with constantly-improving clones of yourself in a virtual League that is a kind of digital evolution. The best bots that appeared in this League, accumulated experience, equivalent to the gameplay 200 years.

AlphaStar beat MaNa, not omnipotent. At the moment the robot can only play one of three races available in StarCraft. In addition to the inhuman long experience of the game, DeepMind is also differently perceived this game. He sees everything that happens in the game, odnovremenno, whereas the MaNa needed to move around the map to see what’s going on. AlphaStar also has higher control precision and targeting of units than the person holding a computer mouse, while the response time of the computer and less than that of the professional gamer.

Despite these flaws, Riedl and other experts as a whole welcomed the work of DeepMind. “It was very impressive,” says Tang Jie, a researcher of the research Institute of AI OpenAI working on bots, play Dota 2, the most profitable for eSports game in the world. The trick with video games can have potentially positive side effects. The algorithms and code that OpenAI used for the development of Dota last year, with varying degrees of success have been adapted to make the hands more dexterous robots.

However, AlphaStar illustrates the limitations of modern specialized systems machine learning, says Julian Togelius, a Professor at new York University and author of the recently published book on games and artificial intelligence. Unlike his human opponent, the new champion DeepMind may not play at full strength on different maps or in different races of aliens in the game without lengthy additional training. Also he can’t play checkers, chess or earlier versions of StarCraft.

This inability to cope with even small surprises is a challenge for many expected applications of AI, such as Autonomous vehicles or adaptable robots, which the researchers called artificial General intelligence (AGI, AIS). More significant battle between man and machine can be a kind of decathlon, with Board games, video games and the finals in Dungeons and Dragons.

Limitations of the highly specialized artificial intelligence seemed to be apparent, when MaNa was playing in an exhibition game against AlphaStar, which was limited by the map view on the type of person, one square at a time. Data DeepMind revealed that it is almost as good as the one that beat MaNa in five games.

New bot quickly assembled an army powerful enough to crush your opponent-human, but MaNa used intelligent maneuvers and experience lesions to hold back the forces of AI. The delay gave him time to gather its troops and conquer.

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