Placeholder Image

字幕列表 影片播放

  • Dear Fellow Scholars, this is Two Minute Papers withroly Zsolnai-Fehér.

    親愛的學者夥伴們,這裡是 Károly Zsolnai-Fehér 的二分鐘報導

  • In 1997, the news took the world by storm - Garry Kasparov, world champion and grandmaster chess player

    在 1997 年,這個新聞席捲了全世界 - 世界西洋棋冠軍同時也是棋聖的 Garry Kasparov

  • was defeated by an artificial intelligence program by the name Deep Blue.

    被一個名叫 Deep Blue 的人工智慧程式擊敗

  • In 2011, IBM Watson won first place in the famous American Quiz Show, Jeopardy.

    在 2011 年,IBM Watson 贏得美國有名的智力測驗電視節目《危險邊緣》的第一名

  • In 2014, Google DeepMind created an algorithm that that mastered a number of Atari games by working on raw pixel input.

    在 2014 年,Google DeepMind 建立一套演算法藉由一列一列分析影像像素來學習許多 Atari 遊戲

  • This algorithm learned in a similar way as a human would.

    這套演算法和人類學習的模式相仿

  • This time around, Google DeepMind embarked on a journey to write an algorithm that plays Go.

    此時此刻,Google DeepMind 己經踏上寫出一個圍棋演算法的旅程

  • Go is an ancient chinese board game where the opposing players try to capture each other's stones on the board.

    圍棋是中國古代的棋盤遊戲,規則是對奕的雙方要試著圍住對方棋盤上的區域

  • Behind the veil of this deceptively simple ruleset, lies an enormous layer of depth and complexity.

    在這看似簡單的規則後面,有著極大量的精密計算和複雜度

  • As scientists like to say, the search space of this problem is significantly larger than that of chess.

    如同科學家們說的,這個問題的搜尋空間遠遠大過象棋

  • So large, that one often has to rely on human intuition to find a suitable next move,

    由於如此之大,以致於通常要仰賴下棋者的直覺找出適合的下一步

  • therefore it is not surprising that playing Go on a high level is, or maybe was widely believed to be intractable for machines.

    因此要把圍棋下好是一個或者說普遍相信對機器來說是極為困難的,這並不令人意外

  • This chart shows the skill level of previous artificial intelligence programs.

    這張圖表呈現了過去一些人工智慧程式的技術水平

  • The green bar is shows the skill level of a professional player used as a reference.

    這個綠色條狀圖代表人類專業棋士的技術水平,以此作為一個比較的基準

  • The red bars mean that these older techniques required a significant starting advantage to be able to contend with human opponents.

    紅色條狀圖表示這些較老舊的技術需要重大的改善才能和人類對手競爭

  • As you can see, DeepMind's new program's skill level is well beyond most professional players.

    如你所見,DeepMind 推出的新程式技術水平高過大部分的專業玩家。

  • An elite pro player and European champion Fan Hui was challenged to play AlphaGo, Google DeepMind's newest invention

    樊麾,一位曾獲歐洲冠軍的菁英職業棋士被 Google DeepMind 最新的發明 AlphaGo 挑戰

  • and got defeated in all five matches they played together.

    並在五盤對奕都敗下陣來

  • During these games, each turn it took approximately 2 seconds for the algorithm to come up with the next move.

    在這幾盤棋中,演算法每次會花大約 2 秒鐘計算出下一步

  • An interesting detail is that these strange black bars show confidence intervals,

    一個有趣的細節是這些看似奇怪的黑色條狀圖代表信賴區間,

  • which means that the smaller they are, the more confident one can be in the validity of the measurements.

    它的值愈小,表示我們可以對這個測量的結果更有信心

  • As one can see, these confidence intervals are much shorter for the artificial intelligence programs than the human player,

    如我們所看到的,這些人工智慧程式的信賴區間遠比人類棋士的要來得小

  • likely because one can fire up a machine and let it play a million games,

    可能是因為我們可以開著機器讓它下很多很多盤棋

  • and get a great estimation of its skill level, while the human player can only play a very limited number of matches.

    因此可以對它的技術水平得到很好的預測,但是人類棋士能下的次數極為有限

  • There is still a lot left to be excited for, in March, the algorithm will play a world champion.

    還有許多值得我們熱切期待的,今年三月,這個演算法將和一位世界冠軍對奕

  • The rate of improvement in artificial intelligence research is accelerating at a staggering pace.

    人工智慧研究的演進比例正以驚人的速度成長

  • The only question that remains is not if something is possible, but when it will become possible.

    現在剩下的問題不是某件目標是否可能達成,而是什麼時候它可以被達成

  • I wake up every day excited to read the newest breakthroughs in the field, and of course,

    我每天早上醒來興奮地讀著這個領域最新的突破

  • trying to add some leaves to the tree of knowledge with my own projects.

    並且理所當然地嘗試在我的專案中的知識樹增添一些新知

  • I feel privileged to be alive in such an amazing time.

    能活在這麼令人驚豔的時代讓我覺得非常感恩

  • As always, there's lots of references in the description box, make sure to check them out.

    一如往常,在敘述欄裡有許多的引用資料,參考看看吧

  • Thanks for watching and for your generous support, and I'll see you next time!

    感謝您的收看和您的大力支持,我們下次見

Dear Fellow Scholars, this is Two Minute Papers withroly Zsolnai-Fehér.

親愛的學者夥伴們,這裡是 Károly Zsolnai-Fehér 的二分鐘報導

字幕與單字

影片操作 你可以在這邊進行「影片」的調整,以及「字幕」的顯示

B1 中級 中文 美國腔 演算法 圍棋 技術 人工 水平 冠軍

兩分鐘論文--DeepMind如何用深度學習(AlphaGo)征服圍棋? (Two Minute Papers - How DeepMind Conquered Go With Deep Learning (AlphaGo))

  • 1973 64
    Vincent Liu 發佈於 2021 年 01 月 14 日
影片單字