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Fortnite 看起來沒上 Auto Scaling?(或是沒正確設好?)

Fortnite 遊戲的伺服器放在 AWS 上,看起來這波 Meltdown 的安全更新 (KPTI) 造成非常大的 overhead:

不過看起來出了問題:

We wanted to provide a bit more context for the most recent login issues and service instability. All of our cloud services are affected by updates required to mitigate the Meltdown vulnerability. We heavily rely on cloud services to run our back-end and we may experience further service issues due to ongoing updates.

最有可能的是把 AWS 當作一般的 VPS 在用,另外一種可能是有部份內部服務沒有 scale,造成上了 KPTI 後 overhead 增加,就卡住了...

在 TeX 上輸出圍棋棋譜的套件 psgo_emitter

忘記是在哪邊看到 avysk/psgo_emitter 這個套件,提供 TeX 語法輸出成圍棋棋盤的圖示,不過說明裡說只支援 Windows 平台:

psgo_emitter is a (Windows) console utility to create go diagrams for go life-and-death problems (tsumego).

可以只輸出角部,像是這段語法:

    \begin{psgopartialboard}{(1,1)(8,6)}
            \stone{black}{b}{3}
            \stone{black}{d}{3}
            \stone{black}{b}{4}
            \stone{white}{d}{5}
            \stone{white}{g}{2}
            \stone{black}{d}{2}
            \stone{white}{b}{5}
            \stone{white}{c}{4}
            \stone{white}{e}{4}
            \stone{white}{e}{3}
            \stone{white}{e}{2}
            \stone{black}{e}{1}
    \end{psgopartialboard}

會輸出這樣的圖:

另外也可以把手順放進去:

    \begin{psgopartialboard}{(1,1)(8,6)}
            \stone{black}{b}{3}
            \stone[\marklb{1}]{black}{a}{2}
            \stone{black}{d}{3}
            \stone{black}{b}{4}
            \stone[\marklb{8}]{white}{f}{1}
            \stone[\marklb{6}]{white}{d}{1}
            \stone{white}{e}{2}
            \stone{white}{g}{2}
            \stone{black}{d}{2}
            \stone{white}{b}{5}
            \stone[\marklb{7}]{black}{b}{2}
            \stone[\marklb{9}]{black}{a}{1}
            \stone{white}{c}{4}
            \stone[\marklb{4}]{white}{c}{2}
            \stone{white}{e}{4}
            \stone[\marklb{5}]{black}{c}{3}
            \stone{white}{e}{3}
            \stone[\marklb{2}]{white}{b}{1}
            \stone{white}{d}{5}
            \stone[\marklb{3}]{black}{a}{4}
            \stone{black}{e}{1}
    \end{psgopartialboard}

就會輸出:

套件還很新,不知道之後會發展成什麼樣子...

各種道路設計對於流量的影響?

在「The rates of traffic flow on different kinds of 4-way intersections」這邊看到有趣的東西,利用遊戲 Cities: Skylines 模擬各種道路設計對流量的影響:

This is an animation of traffic flows simulated on 30 different kinds of four-way junctions, from two roads intersecting with no traffic lights or signs to complex stacked interchanges that feature very few interactions between individual cars. It was recorded in a game called Cities: Skylines, a more realistic take on SimCity.

影片在這:

記得這是模擬,實際情況會有其他考量,所以裡面的結果參考就好...

然後裡面有看到很多常見的設計,還有一些沒看過的神奇設計 XD 另外有些設計超級複雜,第一次開的人真的會知道怎麼開嗎 XDDD

AlphaGo 的開局庫分析

Facebook 上看到 Aja Huang 的訊息,介紹了 DeepMind 放出的新資料,由 AlphaGo 分析人類開局的各種勝率 (不是先前發表出來更凶的 AlphaZero,但不曉得是 AlphaGo Zero 還是 AlphaGo Master...)。

網站在 AlphaGo Teach: Discover new and creative ways of playing Go,盤面上的數字都是指黑棋勝率。

This tool provides analysis of 6,000 of the most popular opening sequences from the recent history of Go, using data from 231,000 human games and 75 games AlphaGo played against human players.

Explore the board and learn how AlphaGo's moves compare to those of professional and amateur players.

由於這是暴力法算出超大量的開局資料,而且是完整公佈在網路上,可以預料接下來的職業比賽,開局應該會參考這邊的資料進行修正...

Steam 停止使用 Bitcoin 購買遊戲

Steam 宣佈停止使用 Bitcoin 購買遊戲:「Steam is no longer supporting Bitcoin」。

官方提到的原因是因為交易費用太高 (雖然是讓使用者付):

In the past few months we've seen an increase in the volatility in the value of Bitcoin and a significant increase in the fees to process transactions on the Bitcoin network. For example, transaction fees that are charged to the customer by the Bitcoin network have skyrocketed this year, topping out at close to $20 a transaction last week (compared to roughly $0.20 when we initially enabled Bitcoin).

另外一個原因是波動問題:

Historically, the value of Bitcoin has been volatile, but the degree of volatility has become extreme in the last few months, losing as much as 25% in value over a period of days.

所以這樣推測,Steam 不是直接換成法幣?我記得他們合作的交易所 (BitPay) 可以馬上換成法幣...

AlphaGo Zero 演算法的 Open Source 實做

Leela 的作者 Gian-Carlo Pascutto 依照 paper 的描述寫完了,放在 GitHub 上的 gcp/leela-zero

不過他在 Twitter 上也提到了,open source 實做不是真正的困難,真正的困難在於訓練完的資料,那個部份需要大量的成本才有辦法作到:

另外他推估 AlphaGo Zero 的計算量是 1700 年 (以 1080 Ti 來計算):「[Computer-go] Zero performance」。

另外 Leela 0.11.0 也推出了,還是先維持 policy + value 的方式,但引入了不少新演算法加強。另外一個蠻特別的地方是 Windows 版改用 clang 而變快不少:

Windows version is now compiled with Clang/LLVM 5.0 instead of MSVC2017. This makes the Monte Carlo evaluations about 15% faster.

雖然 DeepMind 說要收手,但還是留下不少方向讓大家走...

AlphaGo Zero 的計算量

AlphaGo Zero 論文裡有提到,用同樣的硬體 (4 TPU) 可以用 89:11 碾壓 AlphaGo Master (今年年初與柯潔下的那個版本),主要是得力於更高品質的 neural network 以及更強的選擇能力 (後面這塊應該是將兩個 nerual network 簡化為一後的好處):

This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration.

那麼對應的問題就會冒出來了,究竟 DeepMind 花了多少時間才能訓練出這個新的 nerual network?結果吳毅成教授在 Facebook 上先估算出來了:

這邊的 TPU 對 GPU 的推估應該是基於當時 Google 在說明 TPU 的部份「An in-depth look at Google’s first Tensor Processing Unit (TPU)」:

In short, we found that the TPU delivered 15–30X higher performance and 30–80X higher performance-per-watt than contemporary CPUs and GPUs.

用 GPU 大約是 12K 顆,反推回 TPU 大約也是千顆這個數量左右。而這個數量以目前已經將 TPU 商用化的 Google 來看應該是很輕鬆,只能說有錢真好 XD:

1. 從另外一個角度看, DeepMind 僅40天就可以把 40-block 版本練起來, 換算一下, DeepMind 等於用了約12000顆 1080 Ti.

Windows 將引入 TruePlay,推出作弊偵測的 API

在「Windows now includes gaming cheat detection at the system level」這邊看到微軟將會引入 TruePlay (然後跟 Sonostrueplay 衝名 XDDD) 作弊偵測機制。

很明顯的會有隱私問題,而也很明顯的微軟說不會有隱私問題:

As Microsoft notes, "to protect customer privacy, no data is shared or transmitted until permission is granted," and no information is sent until "processing has determined cheating is likely to have occurred."

這不是把人當傻子嗎,遊戲一開始就會要求你同意才能玩啊,所以資料一定會送出的啊... 而且 TruePlay 變成作業系統的標準配備後,作弊程式就會找 workaround 才會推出 :o

Facebook 也參戰參與 StarCraft 的 AI 測試,只是成績不太好...

Facebook 也參與了 StarCraft 的 AI 測試,不過成績不太好:「Facebook Quietly Enters StarCraft War for AI Bots, and Loses」。

不過跟 DeepMind 投入的項目不太一樣,Facebook 投入的是 StarCraft,DeepMind 投入的是 StarCraft II...

比賽有二十八組,Facebook 拿下第六名,但前三名都只是獨立愛好者:

Final results released Sunday indicate Facebook still has a way to go: CherryPi finished sixth in a field of 28; the top three bots were all made by lone, hobbyist coders.

不太像認真要玩...

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