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人機大戰,巔峰競技 - Game IV:李世石扳回一城
2016/03/13 21:44:32瀏覽934|回應9|推薦28

人機大戰,巔峰競技 - Game IV:李世扳回一城

AlphaGo wrapped up victory for Google in the DeepMind Challenge Match by winning its third straight game against Go champion Lee Se-dol yesterday, but the 33-year-old South Korean has got at least some level of revenge — he's just defeated AlphaGo, the AI program developed by Google's DeepMind unit, in the fourth game of a five-game match in Seoul.

AlphaGo is now 3-1 up in the series with a professional record, if you can call it that, of 9-1 including the 5-0 win against European champion Fan Hui last year. Lee's first win came after an engrossing game where AlphaGo played some baffling moves, prompting commentators to wonder whether they were mistakes or — as we've often seen this week — just unusual strategies that would come good in the end despite the inscrutable approach. (To humans, at least.)

According to tweets from DeepMind founder Demis Hassabis, however, this time AlphaGo really did make mistakes. The AI "thought it was doing well, but got confused on move 87," Hassabis said, later clarifying that it made a mistake on move 79 but only realized its error by 87. AlphaGo adjusts its playing style based on its evaluation of how the game is progressing.

Lee entered the post-game press conference to rapturous applause, remarking "I've never been congratulated so much just because I won one game!" Lee referred back to his post-match prediction that he would win the series 5-0 or 4-1, saying that this one win feels even more valuable after losing the first three games.

"Lee Sedol is an incredible player and he was too strong for AlphaGo today," said Hassabis, adding that the defeat would help DeepMind test the limits of its AI. "For us this loss is very valuable. We're not sure what happened yet."

DeepMind's AlphaGo program has beaten 18-time world champion Lee three times so far with its advanced system based on deep neural networks and machine learning. The series is the first time a computer program has taken on a professional 9-dan player of Go, the ancient Chinese board game long considered impossible for computers to play at a world-class level due to the high level of intuition required to master its intricate strategies. Lee was competing for a $1 million prize put up by Google, but DeepMind's victory means the sum will be donated to charity. 

 

References:

Go champion Lee Se-dol strikes back to beat Google's DeepMind AI for first time

Intuition beats ingenuity at last

By Sam Byford on March 13, 2016 04:44 am

http://www.theverge.com/2016/3/13/11184328/alphago-deepmind-go-match-4-result

 AlphaGo-Lee Sedol Match: Game 4 News Coverage

Mar 13, 2016 06:04 pm | Chris Garlock

Lee Sedol defeats AlphaGo in masterful comeback – Game 4
Go Game Guru

After Three Losses, Master Go Player Scores A Win Against Computer
NPR

Go Grandmaster Lee Sedol Grabs Consolation Win Against Google’s AI
Wired

Go champion Lee Se-dol strikes back to beat Google’s DeepMind AI for first time
The Verge

Go Champion Beats AlphaGo Software on Fourth Try
Wall Street Journal

Go champion notches first victory against Google computer
Financial Times

In the Age of Google DeepMind, Do the Young Go Prodigies of Asia Have a Future?
The New Yorker

Is Artificial Intelligence Being Oversold?
Scientific American


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雁~《詩詞習作輯錄》
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2016/03/15 15:47

〈抱歉,貼錯地方~重貼於此文回應。〉

職業一段也有可能贏職業九段啦~似乎不可能,除非讓子。

我估計AlphaGo的棋力至少有職業十段~目前可能性不大。

〈職業十段一般是尊稱職業九段棋士,戰績已臻顛峰的榮譽職。另有十段賽則例外。〉

若AlphaGo的棋力有職業十段,那麼李世乭第78手理論上不會造成AlphaGo太大困擾。

再說人工智能機器若達到某高段階,同段階〈職業七段至職業九段〉的職業高手常輸在持久穩定性。

前譜黑棋只要不理78手,中上方腹地正確補一手即幾乎囊括。後譜中上方白棋已有兩眼,翻轉成功。

左上角的B15可能更重要~也是黑不及補強的重要關鍵手。

不過,勝負〈決勝〉手〈K4〉侵蝕了下方黑空,逼使頑抗的AlphaGo因程式計算實空明顯落後,黯然棄子投降。

金大俠(chin8673) 於 2016-03-16 11:19 回覆:
謝謝雁兄多次來交流圍棋

UDN中懂棋者實在不多矣

這AlphaGo有自學能力,學到啥境界,髙不見頂,它的棋力深不可測,無人可知!大俠保守估計,目前的AlphaGo或有11段。觀五盤棋,AlphaGo的佈局中盤戰鬥攻防打劫棄子手拔收官等等,所有紋枰技藝,全面而強大,間有些誤著、緩手、昏招、慢棋,不妨害它的實力堅強。或曰:AlphaGo太強了,盤面上贏太多時,則會下緩手慢棋,等等對手,盤面上落後時,則會急起力追,總之,伴在對水前面不遠處,若果真如此,AlphaGo就太有人性了!

金大俠
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2016/03/15 05:28

A brilliant move of 78 by Lee. Mistake was on move 79 by AlphaGo and AlphaGo only came to that realization on around move 87.  AlphaGo’s loss shows that there’s still some glimmer of hope for humanity.誰理你


步調緩慢的波多黎各(世界日報家園版)

〈出作業〉
電影之路(十)——〈拍片囉——殺青日〉
電影之路(九)——〈拍片囉——處女秀〉
金大俠(chin8673) 於 2016-03-15 11:16 回覆:

Michael Redmond on AlphaGo, Lee Sedol and Honinbo Shuwa

Mar 14, 2016 04:52 am | Chris Garlock

by Chris Garlock

During a long walk around Seoul on Monday — the day off before the Google DeepMind Challenge final game Tuesday
between Lee Sedol 9P and AlphaGo — Michael Redmond 9P was still thinking about the game from the previous day, in which Lee had finally snatched victory from the jaws of defeat. In reviewing the game carefully, he was convinced that Lee’s “brilliant” move 78 — which had won the game — didn’t actually work. Somehow, though, it had prompted a fatal mistake by AlphaGo, which top members of the DeepMind team were still trying to understand, and had reviewed key points with Redmond after the match and then again at breakfast Monday morning. While Redmond was fascinated with the move’s many complicated variations and trying to understand what had happened to AlphaGo, he was also thinking about Honinbo Shuwa, the 19th century Japanese professional go player admired by modern professionals for his light, flexible play, and mastery of “amashi,” taking territory early and then invading or reducing the opponent’s resulting area of influence. Which is exactly the strategy employed by Lee Sedol against AlphaGo in the fourth game on Sunday. “Shuwa would just jump into huge moyos and lay waste to them,” Redmond said as we walked past the Changgyeonggung Palace. “He’d just be kind of floating around there and still taking territory while being attacked. It was just sort of impossible to kill Shuwa’s stones.” Lee Sedol was doing things a little differently, Redmond noted. “He was taking profit and taking profit and then invading at the last minute. He’s been trying this strategy since Game 2 and it hasn’t been working but it finally did in Game 4.” In the final game, in which Lee will take black, “I think that Lee has the idea that he can use the amashi style, which is usually used when playing white, to take territory, allow AlphaGo to build a big moyo and then jump in.”
Garlock is the Managing Editor of the American Go E-Journal. photo: Redmond (left) with DeepMind team members David Silver (next to Redmond), Chris Maddison (second from right) and Thore Graepel (far right), revieiwng Game 4 Sunday night.
Click here for Redmond’s Match 3 Game Highlights and here for the Match 4 Livestream commentary by Michael Redmond 9P with Chris Garlock. Click here for complete commentaries on games 1-4, as well as brief game highlights for each round.
The fifth and final game in the 5-game Lee Sedol-AlphaGo match will be Tuesday, March 15, 1P KST (Monday night 9p PST, midnight EST). The match will be livestreamed on DeepMind’s YouTube channel with commentary by Redmond and Garlock. And catch Myungwan Kim 9P’s commentary with Andrew Jackson starting at 10P PST on the AGA’s YouTube Channel. 

金大俠(chin8673) 於 2016-03-15 11:21 回覆:

 

Lee Sedol Notches Win Against AlphaGo in DeepMind Challenge Game 4

Mar 13, 2016 10:10 am | Chris Garlock

Lee Sedol 9P made a comeback Sunday after three consecutive losses, to beat AlphaGo in the fourth game of the Google DeepMind Challenge. Playing as white, Lee won by resignation after 180 moves. AlphaGo held a strong position for the first half of the game, but commentators noted that Lee Sedol played a brilliant move 78, followed by a mistake by AlphaGo at move 79. “Today’s game was another example of AlphaGo playing a very interesting, good game,” said English commentator Michael Redmond 9P. “However, move 78 by Lee Sedol was really brilliant — and enabled him to win.“ Song Taegon 9P, the Korean commentator, said that “It seems Lee Sedol can now read AlphaGo better and has a better understanding of how AlphaGo moves. For the 5th match, it will be a far closer battle than before since we know each better.Professional go players said that they became more interested in playing go after witnessing AlphaGo’s innovative moves. People started to rethink about moves that were previously regarded as undesirable or bad moves. AlphaGo can help us think outside of the box.“ As in the previous games in this match, Lee used up all of his time and two periods of byō-yomi overtime, playing nearly two hours on his last period. With the match score 3-1, AlphaGo has already secured victory in the Google DeepMind Challenge Match, but Sunday’s loss heightens the drama going into the final game, Game Five, which will be played on Tuesday, March 15 at 1pm KST.
photo (left): AlphaGo’s Demis Hassabis and David Silver review Game 4 with Michael Redmond 9P; photo by Chris Garlock. photo (right): Lee Sedol, courtesy Geordie Wood for Wired.
Click here for Michael Redmond’s Match 3 Game Highlights and here for the Match 4 Livestream commentary by Michel Redmond 9P with Chris Garlock. Click here for complete commentaries on games 1-4, as well as brief game highlights for each round.
The fifth and final game in the 5-game Lee Sedol-AlphaGo match will be Tuesday, March 15, 1P KST (Monday night 9p PST, midnight EST). The match will be livestreamed on DeepMind’s YouTube channel with commentary by Redmond and Garlock. And catch Myungwan Kim 9P’s commentary with Andrew Jackson starting at 10P PST on the AGA’s YouTube Channel. 


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《奇招打亂AlphaGo。南韓棋王逆轉勝》
2016/03/15 01:02

《奇招打亂AlphaGo。南韓棋王逆轉勝》~聯合新聞網

其實,李世石第78手並非最強應手,但已足以讓AlphaGo陷入迷惑,誤判對手亂了陣腳。

留意到第二圖示〈如下〉,比照前圖黑棋幾乎囊括上空,78手後白棋成功侵入上空,證明78手誘敵犧牲得太有價值。

也證明了AlphaGo〈據估棋力可能相當於職業七段〉有盲點~即其程式仍存在Bug。AlphaGo雖輸了這一局,收穫卻更多。

至於為何職業七段會贏職業九段?〈名人或本因坊亦非罕見〉

主因是人工智慧之穩定性佳勝過人類,整體而言,李世石雖敗猶榮。〈一比四or二比三?〉

勝負〈決勝〉手〈網路新聞分享〉


 

金大俠(chin8673) 於 2016-03-15 10:52 回覆:
職業一段也有可能贏職業九段啦

我估計AlphaGo的棋力至少有職業十段

(職業九段棋士下棋過程中也會出現盲點、昏招呀)

下棋過程中的穩定性,會影響勝率,勝率會影響棋力,棋力反應在棋手的段數

李世石雖敗猶榮,因為AlphaGo太強了

若AlphaGo 5:0完勝李世石

DeepMind就不會讓AlphaGo再與人類挑戰了(還用挑戰嗎?)

因為曾經輸了一場

可能幾個月後,再挑戰更年輕、棋力更強的棋手
金大俠(chin8673) 於 2016-03-15 10:57 回覆:
這所謂的勝負〈決勝〉手〈K4〉

左上角的B15可能更重要

多硯坊 (休)
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2016/03/14 14:01
神來之筆
破了不敗之身
不知是否會載入 Alpha Go 的記憶體
從而啟動自我升級 
金大俠(chin8673) 於 2016-03-15 10:37 回覆:
AlphaGo能自我學習

但面對圍棋盤上的浩瀚變化

沒人確知它的吸收能力、應用能力

是多麼強大、還是很緩慢

我猜AlphaGo會贏第五場大笑

戈 筆 揚
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2016/03/14 10:32

I think, this game, Lee is simply lucky.

Alpha-Go can be improved even more in the future.

金大俠(chin8673) 於 2016-03-14 12:16 回覆:
第四盤李世石讀秒了(用完了2小時),AlphaGo仍有1小時7分鐘又4秒

時間上對李世石非常不利

且中上完全是黑地(AlphaGo)

李世石死纏窮繞,絕境乞生,

竟創造出AlphaGo漏算之處(令AlphaGo昏招)

李世石也算極幸運

他那神奇鬼手78,還是有較好的應對之手

只是AlphaGo「暫時昏迷」了大笑

pearlz (民進黨抹黑霸凌WHO )
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9dan
2016/03/14 07:06

9 dan 是什麼意思?表示級別?中文怎麼說?

Google 很坦誠.


金大俠(chin8673) 於 2016-03-14 12:05 回覆:
9 dan 是九段,9P是職業九段

圍棋最高段就是九段

再來是八段、七段•••

pearlz (民進黨抹黑霸凌WHO )
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好消息
2016/03/14 04:49

3 比 1 了,這下子很有看頭。

電腦不可以輸的,輸了,就是程式設計有誤,有沒有預想到的失誤。

付給棋手的巨金是值得的。


金大俠(chin8673) 於 2016-03-14 12:01 回覆:
「輸了,就是程式設計有誤,」

此說,對,也不太對。

那神奇鬼手78,將盤面弄得(太過)複雜!

以致AlphaGo誤算誤判,這是AlphaGo的缺失

Google的巨金沒白花開心

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2016/03/13 23:57

神之一手01〈網路新聞分享〉

金大俠(chin8673) 於 2016-03-14 11:52 回覆:
謝謝雁兄分享

這神之一手78,夾縫中塞擠,縫隙中求生,狀似切割斷打右四黑子,實乃攻擊左方二黑子(G13,G14),中央突破,解救黑營中受困的散兵白子,可稱為經典的神奇鬼手!愛你喲!

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《李世石神之一手令人工智能誤判》
2016/03/13 23:53

《李世石神之一手令人工智能誤判》 :Yahoo 新聞

[節錄]~

 AlphaGo創始人哈薩比斯賽後指出,李世石78手,令AlphaGo誤以為勝率達到七成,直到第87手才反應過來。

 AlphaGo的其中一名創作者席爾瓦指,李世石的神之一手,點中電腦此前不為人知的BUG。衷心祝賀李世石贏得漂亮,並希望能促進AlphaGo的進步。

 專家指出,AlphaGo的弱點,可能是在極為複雜的局面,計算存在誤區及盲點。當遇到無法想像的問題時,可能會按照人工智能的本能處理,做出奇奇怪怪的舉動。

按:李世石〈乭〉78手並非此盤棋最強受手或勝負手,但卻是導致AlphaGo誤判形勢,使李世石〈乭〉得以成功逆轉勝之誘敵妙手。

  以兵法言,其誘敵妙手接近《孫子兵法。九變》之術。

《李世乭「神之一手」擊敗 AlphaGo》:風傳媒

 http://www.storm.mg/article/87340

《人機第四戰,李世乭神乎其技逆轉 AlphaGo 奪人類首勝》:ETtoday

 http://www.ettoday.net/news/20160313/662103.htm