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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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B1 中級

2分間の論文 - DeepMindがディープラーニングで囲碁を克服した方法 (AlphaGo) (Two Minute Papers - How DeepMind Conquered Go With Deep Learning (AlphaGo))

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    Vincent Liu に公開 2021 年 01 月 14 日