Sweeping the top Go game masters in China, Japan and Korea, taking 60 consecutive victories, the mysterious Master has already clarified identity, it is AlphaGo. This kind of result is surprising and unsurprising.

The reason why it is not surprising is that before the Master clarified his identity, it has been widely speculated that it is AlphaGo. Surprisingly, in less than a year, compared to AlphaGo, Master's performance is more eye-catching, progressing faster, and playing chess is fast.

Where does this change come from, and what new advances the Master has made compared to the previous AlphaGo. DeepMind said in an email reply on January 5th: "We have been improving AlphaGo, including algorithmic innovations, learning through left and right training. We are doing these informal fast games this time later this year. In preparation for the official competition of the standard time. In fact, many other smart Go systems will also be tested on the Internet with vests. We did not expect such a big attention, but since so many players are very interested Then, we want to come out and admit it."

After the Master announced his identity, DeepMind founder CEO Demis Hassabis also expressed his excitement on his personal Twitter account. The founder, who is known as the "genius" and "workaholic", has a strong interest in Go. At the age of 13, he won the title of chess master. On January 3, the official blog post published by DeepMind revealed that in addition to Go, the company is still working with local medical institutions in the UK to provide better medical services. At the same time, the company is also considering challenging the StarCraft 2 game.

In such a short period of time, I have made such amazing achievements. What kind of magic is this artificial intelligence company founded in London in the beginning of 2010?

Where is Master strong?

To know how strong the Master has become, start with AlphaGo's algorithm.

The Economist has published an explanation of AlphaGo's algorithm. He believes that its pride lies in the use of new methods to try to get the computer to develop the intuition of how to play chess – the principle that human players can understand but cannot explain. It uses deep learning techniques to allow computers to extract common principles from vast, useless data through repetitive and complex statistics.

Deep learning requires two things: enough processing units and enough data to learn. DeepMind used 30 million game samples to train the machines. These games come from online servers where amateurs and professional players gather to play chess. In addition, AlphaGo also fine-tunes by playing against itself, so that more training data can be generated quickly.

These data need to be processed by two algorithms that are deeply learned. The first is the so-called strategy network, which is used to train and imitate human behavior. After seeing millions of games, it has learned to extract features, principles, and rules of thumb. Its job in the game is to observe the state of the board and produce some more promising steps for the second algorithm.

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