编辑“︁AlphaGo”︁（章节）

== 反應 ==
AlphaGo被誉为人工智能研究的一项标志性进展，在此之前，围棋一直是机器学习领域的难题，甚至被认为是当代技术力所不及的范畴。<ref>{{cite news |title=A computer has beaten a professional at the world's most complex board game |url=http://www.independent.co.uk/life-style/gadgets-and-tech/news/google-alphago-computer-beats-professional-at-worlds-most-complex-board-game-go-a6837506.html |newspaper=[[The Independent]] |access-date=2016-01-28 |date=2016-01-27 |last=Connor |first=Steve |archive-url=https://web.archive.org/web/20160309232950/http://www.independent.co.uk/life-style/gadgets-and-tech/news/google-alphago-computer-beats-professional-at-worlds-most-complex-board-game-go-a6837506.html |archive-date=2016-03-09 |dead-url=no }}</ref><ref>{{cite news |title=Google's AI beats human champion at Go |url=http://www.cbc.ca/news/technology/alphago-ai-1.3422347 |work=[[CBC News]] |access-date=2016-01-28 |date=2016-01-27 |archive-url=https://web.archive.org/web/20160310181352/http://www.cbc.ca/news/technology/alphago-ai-1.3422347 |archive-date=2016-03-10 |dead-url=no }}</ref>樊麾战的棋局裁判托比·曼宁和[[国际围棋联盟]]的秘书长{{le|李夏辰|Lee Ha-jin}}都认为将来围棋棋手会借助电脑来提升棋艺，从错误中学习。<ref>{{cite journal |url=http://www.nature.com/news/go-players-react-to-computer-defeat-1.19255 |title=Go players react to computer defeat |first=Elizabeth |last=Gibney |journal=Nature |year=2016 |doi=10.1038/nature.2016.19255 |author= |access-date=2016-03-11 |archive-url=https://web.archive.org/web/20160130123457/http://www.nature.com/news/go-players-react-to-computer-defeat-1.19255 |archive-date=2016-01-30 |dead-url=no }}</ref>

[[台灣大學]]電機系教授-{于}-天立認為，Google能夠成功結合深度神經網路、加強式學習和蒙地卡羅樹狀搜尋三種演算法，其成果值得喝采。他認為這種技術應該適用於一般連續性決策問題。因為AlphaGo可以在眾多可行的決策中，適當分配運算資源來探索此一決策所帶來的好處及壞處，並且可從探索中回饋修正錯誤。不過-{于}-也提到，即使AlphaGo所使用的學習模型比較具有一般性，它離真正完全通用的學習模型仍有一段距離。<ref>{{Cite web|url=http://pansci.asia/archives/95263|title=AlphaGo成為「棋靈王」是有多厲害？人工智慧未來又要怎麼走？|accessdate=2016-03-15|author=-{于}-天立|date=2016-03-14|coauthors=|work=[[PanSci 泛科學]]|publisher=}}</ref>