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Reading Comprehension 高三英语 李弘扬 AlphaGo Zero Google recently announced that the latest version of its AlphaGo program is now capable of mastering the game without algorithms any help from humans! This version, called AlphaGo Zero, only had knowledge of the rules for playing the Chinese game of Go. Within three days, AlphaGo Zero became good enough to beat previous versions by 100 games to 0. In 1997, IBM’s Deep Blue chess playing machine beat a reigning world champion Gray Kasparov. Programs such as Deep Blue would examine all possible choices and its outcome, in order to predict the next move. This does not work for Go because it is much more complicated than chess and has a huge number of possibilities. For example, after the first two moves. This does not work for Go because it is much more complicated than chess and has a huge number of possibilities. For example, after the first two moves in chess, there remains about 400 possible next moves. But in Go, there remains about 130.000 possible next moves. A researcher at Google described the search space of Go to be more than the number of atoms in the universe! “Search space” is the computer science term for the number of possibilities that have to be examined or searched through. AlphaGo was designed to predict the next move based on data from millions of moves by human experts. Next, AlphaGo was improved to allow it to learn new strategies for itself. It used complex and neural network technology to make the best decision AlphaGo became the first computer program to beat human experts in Go---most recently, the world’s best Go player, Ke Jie from China, in May 2017. for these competitions, AlphaGo still required huge amounts of data from previous games as well as computing power. With AlphaGo Zero, the researchers at Google decided to only teach the program the rues of the game, and let it learn its strategies by playing against itself millions of times. 1. What breakthr