He added that AlphaGo was a "wonderful player" and "completely different" from a year ago.
According to Demis Hassabis, Founder & CEO of DeepMind, the Google-owned company which created AlphaGo, Ke Jie started the game using the same strategies AlphaGo had recently used in some of its games.
"I think it was a really wonderful game - Huge respect to Ke Jie for playing such a great game and pushing AlphaGo to its limits. Compared to humans, I can not feel its passion and longing for the game of Go", he wrote on Weibo, the Chinese equivalent to Twitter, on Monday night.
"Ahead of today, I studied and prepared quite a lot", said Ji. Presumably other Go-playing AIs from China will be at the summit as well, not just AlphaGo, but details are slim right now. It involves two players alternately placing black and white stones on a checkerboard-like grid of 19 lines by 19 lines. Instead, it's moves which will most likely lead to victory. Google experts and prominent local academics will exchange notes and host discussions but the centerpiece will be the 2,500-year-old strategy board game between DeepMind's so-far undefeated AlphaGo system and local champion Ke Jie. It then defeated Korean Go legend Lee Se-dol in four of five games a year ago. After the final count, Ke Jie learned that he had lost by half of one point - the smallest possible margin.
Go, an ancient Chinese board game, is favored by AI researchers because of the large number of outcomes compared to other games such as western chess.
Even while reluctant to agree to a head-to-console match with AlphaGo, Ke, never known to be lacking in confidence, talked a big game, posting on social media, "It may defeat Lee Sedol, but not me". Their time is over though because both Facebook and Google have managed to make tin boxes that can play it better. AlphaGo takes on and teams up with some of the world's best players. Google said a broadcast of Lee's 2016 match with AlphaGo was watched by an estimated 280 million people. Another Deepmind employee added during the post-match press conference that AlphaGo was configured primarily to win, rather than maximise the size of its victory, and it played moves to minimise any risk of a comeback. The aim of the forum is to discuss how machine-learning methods behind AlphaGo can be useful in grappling with real-world issues, such as energy consumption.