係咪得我一個覺得柯潔其實好可憐

當你係某個領域取得一定成就
驕傲有時係令你進步 唔輸得既一個動力
如果作為世界第一
佢仲冷係謙卑咁
絕對係驚輸 留一後路俾自己
就好似歌手唱歌話自己病左
考試話自己未溫哂書咁
亦都係對自己一個肯定
就算理性上面覺得自己勝算低
感性上都應該覺得自己贏硬
先有小小贏面
好似定目標定100分
衰極都會令到60，50
你定60分做目標
最多可能去到30

原來仲有人覺得lol可以同圍棋比
與其話傾圍棋就等於五毛，連登小學雞仲多

連登仔一時又話柯潔冇棋品所以低屌
一時又話串完柒所以抵屌
即係你地屌緊邊樣

拎lol黎比你拎其他運動其他競技都話幫到你口打機世界第一點幫啊

有連登仔人類跑輸保特係咪又冇曬尊嚴
但我想講，人類因為生理上既不足，唔好話機械，連動物都會跑輸
但智慧唔同，人類一向以智慧自居，同時智慧亦都係人類唯一一樣引領全球既野，而圍棋正正就可以體現到人類智慧無限既潛能
如果圍棋都輸撚埋咁人類就唔再係世界第一，到時就真係冇撚曬尊嚴呀明唔明呀 成班衝出黎柒做乜鳩

拎lol黎比你拎其他運動其他競技都話幫到你口打機世界第一點幫啊

幫到架幫到架

有連登仔保特跑輸跑車係咪又冇曬尊嚴
但我想講，人類因為生理上既不足，唔好話機械，連動物都會跑輸
但智慧唔同，人類一向以智慧自居，同時智慧亦都係人類唯一一樣引領全球既野，而圍棋正正就可以體現到人類智慧無限既潛能
如果圍棋都輸撚埋咁人類就唔再係世界第一，到時就真係冇撚曬尊嚴呀明唔明呀 成班衝出黎柒做乜鳩

sor腦抽筋

有連登仔人類跑輸保特係咪又冇曬尊嚴
但我想講，人類因為生理上既不足，唔好話機械，連動物都會跑輸
但智慧唔同，人類一向以智慧自居，同時智慧亦都係人類唯一一樣引領全球既野，而圍棋正正就可以體現到人類智慧無限既潛能
如果圍棋都輸撚埋咁人類就唔再係世界第一，到時就真係冇撚曬尊嚴呀明唔明呀 成班衝出黎柒做乜鳩

完全正解
不過唔使驚訝
連登不嬲咁多智障

笑人用打機比,不如笑埋Deepmind

https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/

At the start of a game of StarCraft, players choose one of three races, each with distinct unit abilities and gameplay approaches. Players’ actions are governed by the in-game economy; minerals and gas must be gathered in order to produce new buildings and units. The opposing player builds up their base at the same time, but each player can only see parts of the map within range of their own units. Thus, players must send units to scout unseen areas in order to gain information about their opponent, and then remember that information over a long period of time. This makes for an even more complex challenge as the environment becomes partially observable - an interesting contrast to perfect information games such as Chess or Go. And this is a real-time strategy game - both players are playing simultaneously, so every decision needs to be computed quickly and efficiently.

An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information. Computers are capable of extremely fast control, but that doesn’t necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of “Actions Per Minute”. StarCraft’s high-dimensional action space is quite different from those previously investigated in reinforcement learning research; to execute something as simple as “expand your base to some location”, one must coordinate mouse clicks, camera, and available resources. This makes actions and planning hierarchical, which is a challenging aspect of Reinforcement Learning.

笑人用打機比,不如笑埋Deepmind

https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/

At the start of a game of StarCraft, players choose one of three races, each with distinct unit abilities and gameplay approaches. Players’ actions are governed by the in-game economy; minerals and gas must be gathered in order to produce new buildings and units. The opposing player builds up their base at the same time, but each player can only see parts of the map within range of their own units. Thus, players must send units to scout unseen areas in order to gain information about their opponent, and then remember that information over a long period of time. This makes for an even more complex challenge as the environment becomes partially observable - an interesting contrast to perfect information games such as Chess or Go. And this is a real-time strategy game - both players are playing simultaneously, so every decision needs to be computed quickly and efficiently.

An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information. Computers are capable of extremely fast control, but that doesn’t necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of “Actions Per Minute”. StarCraft’s high-dimensional action space is quite different from those previously investigated in reinforcement learning research; to execute something as simple as “expand your base to some location”, one must coordinate mouse clicks, camera, and available resources. This makes actions and planning hierarchical, which is a challenging aspect of Reinforcement Learning.

係笑人用LOL比呀
又以偏概全，連登小學雞除左識呢樣同去m記食野仲識啲咩呀

講清楚，我唔係可憐佢輸左，而係可憐作為人類都唔撐人類，走去撐部機械
當然你地又可以話我煽動情緒，懶誇張又乜又物

alpha go 咪又係人類產物
樓上用保特同汽車比 我覺得幾適合
唔通人類發明一樣超越自身能力嘅野
唔係反映緊人類嘅智慧咩

柯潔話佢之所以講個句嬴硬係因為佢覺得自己身為圍棋界最後希望唔可以退縮，同時想用出位少少嘅言論製造話題，希望令更多人留意圍棋。而事實上的確令到更多人開始學圍棋，中國棋院個到有反映到呢排多左人去學。 不過連登仔好難放低偏見理性討論

Exactly呀
之前睇新聞經過同李世石打個場之後
中國多左20%人玩圍棋

算啦佢地唔會理架

有幾慘
佢被世人寸/笑係因為佢之前蔑視對手，而且仲係科技產物，可以話係大言不慚

AlphaGo贏係必然，甚至係必須嘅
換算力比人類低 咁我整部機出來做乜？

柯潔贏，係佢一個人嘅勝利
AlphaGo贏，係人類嘅勝利

不敢苟同

你睇到嘅科技贏左人類
我睇到嘅係科技嘅進步，相輔相成嘅未來

我都唔苟同你。

有巴打都講落，從一開始大家睇野嘅觀點就已經唔同，做有咩野好講?
此post已完。

啱呀
有人睇到人類最後防線咁煽情
我都冇咩好評論
正如有人worship主可以喊一樣

想知俾正評嘅巴絲係咪都咁諗

其實真係幾唏噓
如果有樣野比我做到世界第一
之後轉個頭樣野已經比AI完全取代...

講清楚，我唔係可憐佢輸左，而係可憐作為人類都唔撐人類，走去撐部機械
當然你地又可以話我煽動情緒，懶誇張又乜又物

alpha go 咪又係人類產物
樓上用保特同汽車比 我覺得幾適合
唔通人類發明一樣超越自身能力嘅野
唔係反映緊人類嘅智慧咩

其實真係幾唏噓
如果有樣野比我做到世界第一
之後轉個頭樣野已經比AI完全取代...

佢地唔會理架，總之你臭串就係唔啱

柯潔話佢之所以講個句嬴硬係因為佢覺得自己身為圍棋界最後希望唔可以退縮，同時想用出位少少嘅言論製造話題，希望令更多人留意圍棋。而事實上的確令到更多人開始學圍棋，中國棋院個到有反映到呢排多左人去學。 不過連登仔好難放低偏見理性討論

Exactly呀
之前睇新聞經過同李世石打個場之後
中國多左20%人玩圍棋

話明係經過李世石打個場之後
可能係因為AlphaGo橫空出世引起全球關注
可能係因為李世石神之一手引發少年捍衛人類嘅中二心
點計個功勞都唔會係落係柯潔出位言論身上啦下話

笑人用打機比,不如笑埋Deepmind

https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/

At the start of a game of StarCraft, players choose one of three races, each with distinct unit abilities and gameplay approaches. Players’ actions are governed by the in-game economy; minerals and gas must be gathered in order to produce new buildings and units. The opposing player builds up their base at the same time, but each player can only see parts of the map within range of their own units. Thus, players must send units to scout unseen areas in order to gain information about their opponent, and then remember that information over a long period of time. This makes for an even more complex challenge as the environment becomes partially observable - an interesting contrast to perfect information games such as Chess or Go. And this is a real-time strategy game - both players are playing simultaneously, so every decision needs to be computed quickly and efficiently.

An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information. Computers are capable of extremely fast control, but that doesn’t necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of “Actions Per Minute”. StarCraft’s high-dimensional action space is quite different from those previously investigated in reinforcement learning research; to execute something as simple as “expand your base to some location”, one must coordinate mouse clicks, camera, and available resources. This makes actions and planning hierarchical, which is a challenging aspect of Reinforcement Learning.

係笑人用LOL比呀
又以偏概全，連登小學雞除左識呢樣同去m記食野仲識啲咩呀

上面係冇人笑人用打機比

笑人用打機比,不如笑埋Deepmind

https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/

At the start of a game of StarCraft, players choose one of three races, each with distinct unit abilities and gameplay approaches. Players’ actions are governed by the in-game economy; minerals and gas must be gathered in order to produce new buildings and units. The opposing player builds up their base at the same time, but each player can only see parts of the map within range of their own units. Thus, players must send units to scout unseen areas in order to gain information about their opponent, and then remember that information over a long period of time. This makes for an even more complex challenge as the environment becomes partially observable - an interesting contrast to perfect information games such as Chess or Go. And this is a real-time strategy game - both players are playing simultaneously, so every decision needs to be computed quickly and efficiently.

An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information. Computers are capable of extremely fast control, but that doesn’t necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of “Actions Per Minute”. StarCraft’s high-dimensional action space is quite different from those previously investigated in reinforcement learning research; to execute something as simple as “expand your base to some location”, one must coordinate mouse clicks, camera, and available resources. This makes actions and planning hierarchical, which is a challenging aspect of Reinforcement Learning.

係笑人用LOL比呀
又以偏概全，連登小學雞除左識呢樣同去m記食野仲識啲咩呀

上面係冇人笑人用打機比

唔好撚柒啦
圍棋變化from wiki:
Some numbers:
9×9 board: ~1.039 × 10^38
13×13 board: ~3.724 × 10^79
17×17 board: ~1.908 × 10^137
19×19 board: ~2.082 × 10^170 (i.e., a 2 followed by 170 zeroes)

圍棋一步棋係講緊過百可能，要比較每步價值要考慮棋盤上所有棋子，仲要推算下一步再一步⋯⋯價值，所以計到仆街

打機變化俾盡可能都有廿十個好未，但大部分情況只需要只需要單獨optimized每一個player ，計算就易好撚多

再講要鬥反應快，電腦一早完勝人類

好明顯係廣義講打機啦
無知到仆街

有連登仔人類跑輸保特係咪又冇曬尊嚴
但我想講，人類因為生理上既不足，唔好話機械，連動物都會跑輸
但智慧唔同，人類一向以智慧自居，同時智慧亦都係人類唯一一樣引領全球既野，而圍棋正正就可以體現到人類智慧無限既潛能
如果圍棋都輸撚埋咁人類就唔再係世界第一，到時就真係冇撚曬尊嚴呀明唔明呀 成班衝出黎柒做乜鳩

on9到震
呢個世界第一都係人類嘅產物
乜撚野冇曬尊嚴
同埋你覺得人類永遠都係呢個宇宙嘅霸主

逢中必反真係戇尻尻

笑人用打機比,不如笑埋Deepmind

https://deepmind.com/blog/deepmind-and-blizzard-release-starcraft-ii-ai-research-environment/

At the start of a game of StarCraft, players choose one of three races, each with distinct unit abilities and gameplay approaches. Players’ actions are governed by the in-game economy; minerals and gas must be gathered in order to produce new buildings and units. The opposing player builds up their base at the same time, but each player can only see parts of the map within range of their own units. Thus, players must send units to scout unseen areas in order to gain information about their opponent, and then remember that information over a long period of time. This makes for an even more complex challenge as the environment becomes partially observable - an interesting contrast to perfect information games such as Chess or Go. And this is a real-time strategy game - both players are playing simultaneously, so every decision needs to be computed quickly and efficiently.

An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information. Computers are capable of extremely fast control, but that doesn’t necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of “Actions Per Minute”. StarCraft’s high-dimensional action space is quite different from those previously investigated in reinforcement learning research; to execute something as simple as “expand your base to some location”, one must coordinate mouse clicks, camera, and available resources. This makes actions and planning hierarchical, which is a challenging aspect of Reinforcement Learning.

係笑人用LOL比呀
又以偏概全，連登小學雞除左識呢樣同去m記食野仲識啲咩呀

上面係冇人笑人用打機比

唔好撚柒啦
圍棋變化from wiki:
Some numbers:
9×9 board: ~1.039 × 10^38
13×13 board: ~3.724 × 10^79
17×17 board: ~1.908 × 10^137
19×19 board: ~2.082 × 10^170 (i.e., a 2 followed by 170 zeroes)

圍棋一步棋係講緊過百可能，要比較每步價值要考慮棋盤上所有棋子，仲要推算下一步再一步⋯⋯價值，所以計到仆街

打機變化俾盡可能都有廿十個好未，但大部分情況只需要只需要單獨optimized每一個player ，計算就易好撚多

再講要鬥反應快，電腦一早完勝人類

好明顯係廣義講打機啦
無知到仆街

打機世界第一 點幫啊[/red]

呢度又點計呀 快啲屌鳩Deepmind白癡啦

上面個例子

連登多低質會員就常識啦
Human Dignity 又唔在乎
自己能力低過人搵唔到位串人
就係人 外表 性別 種族 國籍入手串人
但跟本連圍棋都唔識
評論嘅資格都無嘅社會垃圾點都要串下人平衡自己的自卑感

有連登仔人類跑輸保特係咪又冇曬尊嚴
但我想講，人類因為生理上既不足，唔好話機械，連動物都會跑輸
但智慧唔同，人類一向以智慧自居，同時智慧亦都係人類唯一一樣引領全球既野，而圍棋正正就可以體現到人類智慧無限既潛能
如果圍棋都輸撚埋咁人類就唔再係世界第一，到時就真係冇撚曬尊嚴呀明唔明呀 成班衝出黎柒做乜鳩

on9到震
呢個世界第一都係人類嘅產物
乜撚野冇曬尊嚴
同埋你覺得人類永遠都係呢個宇宙嘅霸主

如果人類唔係呢個世界既霸主，咁我會好撚驚囉
退一步唔講人類冇曬尊嚴，講棋手冇曬尊嚴得啦掛？
研究左幾千年既野比個新興AI一野收皮，係咪仲要讚個AI好撚勁？