Expert system (ES, 專家系統)is a somewhat archaic term that describes a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. Artificial intelligence (AI, 人工智慧)systems build on expert systems by employing technologies such as machine learning, natural language processing, speech recognition and machine vision, all of which are central to an AI system. AlphaGo does have amazing self-learning capability (Supervised Learning and Reinforcement Learning) so that even DeepMind’s development team does not know how strong or how smart AlphaGo is at current stage.  Also notice that AlphaGo is less than “3 year old.”  

和人工智慧下棋，贏棋的關鍵在於：完全不按牌理出牌、亂下，讓它無法預測你出棋的邏輯。這是和電腦琢磨過棋藝的人都知道的事。

越是笨蛋式、爆笑式下法，電腦越是反而幫此人贏棋，越是高手式下法，反而越受制肘、越易中計，宛如自宮。因為越是專家、越是熟稔棋藝，腦子就越是格式化過度嚴重。高手過招，往往是較量彼此的格式化程度，但問題是，人類再怎麼格式化自己腦袋，也絕不會比得上電腦完全格式化。所以贏棋的關鍵，是拋卻慣於格式化的僵化大腦認知模式，像初生嬰兒或開悟者那樣，腦子認知水汪汪的一片、如同布丁，也就是一般聰明者易產生歧視或誤認是笨蛋可欺、頭腦像有問題的那種人的存在狀態。也等於老祖宗說的「大智若愚」、「圓樸樸、渾拙拙」，未定義一切。

我相信棋王只要有機會反覆和同一人工智慧對戰，就可抓到它的棋路與出棋邏輯，並且反得人工智慧助力，輕易贏棋。

以過去和電腦對弈的經驗而論，我猜測，反而越是笨蛋式自殺式的出棋，越容易得到人工智慧主動化解危機，給予保送；越想贏棋，就越容易害自己輸；反而越不想贏棋，就越容易成為贏家。放鬆的心態、不受過去制約、沒有爭贏的慾望企圖及壓力，才是致勝關鍵。真的就只是「徹底的玩、有趣的玩」而已。