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[NetEase Smart News Dec. 13] In the early hours of the morning, AlphaGo's “Spokesperson/Humanity Arm†and Google’s DeepMind scientist Dr. Aja Huang announced on Facebook in the early morning that he will formally leave the AlphaGo team and move to DeepMind. Go to other project studies.
(Huang Shijie posted on Facebook)
It is understood that, as early as this year in May after the world's first person to defeat Go, Ke Jie, DeepMind had announced that from now on the AlphaGo project will not continue to develop, AlphaGo will no longer participate in competitions. At the same time, DeepMind also initially disclosed the next step of the AlphaGo team's plan to develop a wider range of algorithms for applications, including finding new ways to treat diseases, significantly reducing energy consumption, and inventing revolutionary new materials.
On November 27, Huang Shijie forwarded a message on Facebook about DeepMind's AI research with some of the top research institutions investigating breast cancer. DeepMind will use the latest machine learning technology, hoping to quickly and accurately detect the signs of cancer, help doctors to find cancer as soon as possible, so that effective treatment can be earlier, so he is very likely to join the medical research.
For the famous AlphaGo, man-machine wars, and Ke Jie, everyone may be very familiar with it, but Huang Shijie who may not be familiar with.
(Dr. Huang Shijie acts as his son)
Dr. Huang Shijie was a Taiwanese engineer and behind the scenes of AlphaGo. Huang Shijie loved Go since he was a child. When he was studying at Taiwan Normal University, he founded the school’s Go club and also won the Goal Cup Go Championship. He is an amateur six-section Go player. Huang Shijie, who has achieved brilliant academic results, was poached by a Canadian university as a computer go program fellow after an oral exam. He was recruited by DeepMind, a British company specializing in artificial intelligence, 1 year later. In 2014, DeepMind was acquired by Google. Huang Shijie is already one of the two chief engineers in the company.
(From left to right, Hasabis, Ke Jie, Silva)
Huang Shijie started DeepMind career. One day, Silva went to Huang Shijie and said: "Aja, we are ready to start a Go project. At the beginning, you and I are the only ones." In February 2014, the AlphaGo project was officially launched. The team consists of three people: Hasabis, Silva, and Huang Shijie. Hasabis is the owner of the entire company. Silva is the manager of Huang Shijie. Therefore, only Huang Shijie is the only one who really works in this team. The AlphaGo project wants to come up with a powerful Go program and explore along deep learning and reinforcement learning.
Two years later, from March 9th to 15th, 2016, they took AlphaGo to the world and they were invincible.
We look forward to Huang Shijie's new journey. He is not just a human arm. The big screen of artificial intelligence era has only just begun!
So, the DeepMind team has a new mission and released a new reinforcement learning algorithm, AlphaZero.
It is a new algorithm that can start from scratch and through self-enhanced reinforcement learning can reach beyond the human level in various tasks. It can be called "general chess AI".
It is understood that the AlphaZero algorithm can beat the Li Shishi version of AlphaGo in 8 hours of training; then 4 hours of training to defeat the world's top chess program Stockfish; then 2 hours of training to defeat the world's top chess program Elmo. This is another new algorithm brought to us by the DeepMind team following the study of AlphaGo Zero. It is a "more generic version."
In addition, we have seen several differences between AlphaZero and AlphaGo Zero.
First, AlphaGo Zero estimates and optimizes the probability of winning, assuming the outcome is a win/loss binary. AlphaZero takes into account the draw or other potential results and estimates and optimizes the results.
Second, AlphaGo and AlphaGo Zero will change the board position for data enhancement, while AlphaZero will not.
Third, AlphaZero only maintains a single neural network. This neural network is constantly updated rather than waiting for iterations.
Fourth, AlphaZero reuses the same hyper-parameters for all games, so there is no need to adjust for a particular game.
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