用 Machine Learning 改善 Streaming 品質的服務與論文

Hacker News 上看到「Puffer」這個服務,裡面利用了 machine learning algorithm 動態調整 bitrate,以提昇傳輸品質。

測試得到的數據後來被整理起來一起放進論文:「Continual learning improves Internet video streaming」。

在開頭介紹了 Fugu 這個演算法:

We describe Fugu, a continual learning algorithm for bitrate selection in streaming video.

而 Puffer 就是實驗網站:

We evaluate Fugu with Puffer, a public website we built that streams live TV using Fugu and existing algorithms. Over a nine-day period in January 2019, Puffer streamed 8,131 hours of video to 3,719 unique users.

這個站台提供了許多真實的頻道進行測試:

Stream live TV in your browser. There's no charge. You can watch U.S. TV stations affiliated with the NBC, CBS, ABC, PBS, FOX, and Univision networks.

可以看到 Fugu 的結果很好,比起其他提出的方案是全面性的改善:

這邊是用 WebSocket 測試,並且配合了不同的 TCP congestion algorithm,沒有太考慮額外的計算成本...

AI 版的星海爭霸二將直接透過歐洲區的 Battle.net 匿名與人類對戰

前幾天 Blizzard 公佈的消息,DeepMind 的星海爭霸二 AI (AlphaStar) 將會透過 Blizzard 的 Battle.net 歐洲區伺服器跟人類對戰:「DeepMind Research on Ladder」。

Experimental versions of DeepMind’s StarCraft II agent, AlphaStar, will soon play a small number of games on the competitive ladder in Europe as part of ongoing research into AI.

預設是不會對到的,需要選擇參與:

If you would like the chance to help DeepMind with its research by matching against AlphaStar, you can opt in by clicking the “opt-in” button on the in-game popup window. You can alter your opt-in selection at any time by using the “DeepMind opt-in” button on the 1v1 Versus menu.

但你仍然不會知道對手是人還是 AI,而且如同一般對戰情況,這會影響到你的戰績:

For scientific test purposes, DeepMind will be benchmarking AlphaStar’s performance by playing anonymously during a series of blind trial matches. This means the StarCraft community will not know which matches AlphaStar is playing, to help ensure that all games are played under the same conditions. AlphaStar plays with built-in restrictions that the DeepMind team has defined in consultation with pro players. A win or a loss against AlphaStar will affect your MMR as normal.

okay,這樣大概知道為什麼只開放歐洲區了...

加州從今年七月開始,禁止 AI 偽裝成人類 (前幾天也有一些新聞在報導):「A California law now means chatbots have to disclose they’re not human」,對應的法條在「Bill Text - SB-1001 Bots: disclosure」這邊可以看到:

17941. (a) It shall be unlawful for any person to use a bot to communicate or interact with another person in California online, with the intent to mislead the other person about its artificial identity for the purpose of knowingly deceiving the person about the content of the communication in order to incentivize a purchase or sale of goods or services in a commercial transaction or to influence a vote in an election. A person using a bot shall not be liable under this section if the person discloses that it is a bot.

(b) The disclosure required by this section shall be clear, conspicuous, and reasonably designed to inform persons with whom the bot communicates or interacts that it is a bot.

而加州是 Blizzard Entertainment 的總部...

法條上面對「online platform」有設計排除條款,不過如果只算星海二的人數,有可能不到這個豁免限制... 所以得避開而改用歐洲區來測試?

(c) “Online platform” means any public-facing Internet Web site, Web application, or digital application, including a social network or publication, that has 10,000,000 or more unique monthly United States visitors or users for a majority of months during the preceding 12 months.

(c) This chapter does not impose a duty on service providers of online platforms, including, but not limited to, Web hosting and Internet service providers.

美國軍方應該是超級關注這個議題,相較於 AlphaGo 或是 AlphaZero 是資訊完全透明的遊戲,這次要踏入非對稱資訊的遊戲。

如果在這個領域上有成果的話,可以預期未來的戰爭 (yeah 實體戰爭) 會開始大量採用 AI 了...

Instagram 改善影片上架速度的方式

不是什麼魔法,其實是改產品面上的規格 (但是發表到 Instagram Engineering 上):「Video Upload Latency Improvements at Instagram」。

最原始的版本是所有的格式都轉完後才可以上架:

然後把規格改成最高畫質的版本轉完後就可以先上架:

The idea is, instead of blocking until all video versions are available, we can publish the video once the highest-quality video version is available.

然後是把影片切段上傳,所以傳一半就可以先處理一半,變成 pipeline 的概念,但增加程式的複雜度,以及被迫要調整影片品質的參數:

Segmented uploads reduce upload latency in many cases but come with a few tradeoffs. For instance, segmented uploads increase the complexity of the pipeline. There are some quality metrics that are only available per segment at transcode time, such as SSIM. These metrics are not helpful to us on a per segment basis. Therefore, we need to do a duration weighted average of the SSIM of all segments to come up with the SSIM of the whole video. Similarly, handling exceptions is more complex since there are more cases to handle.

另外有一種特例是上傳的影片本身就已經符合伺服器的規格,這樣的話可以直接放行 (不過這樣不會有 security concern 嗎...):

Another performance optimization we use to improve the upload latency and save CPU utilization is something we call a “passthrough” upload. In some cases, the media that is uploaded is already ready for playback on most devices.

都是想的出來而且會帶有 tradeoff 的方法,而不是完全正面的改善 :o

Cloudflare 改善了同時下載同一個檔案的效率...

在「Live video just got more live: Introducing Concurrent Streaming Acceleration」這邊 Cloudflare 說明他們改善了同時下載同一個 cache-miss 檔案時的效率。

本來的架構在 cache-miss 時,CDN 這端會先取得 exclusive lock,然後到 origin server 抓檔案。如果這時候有其他人也要抓同一個檔案,就會先卡住,避免同時間對 origin server 產生大量連線:

這個架構在一般的情況下其實還 ok,就算是 Windows Update 這種等級的量,畢竟就是一次性的情況而已。但對於現代愈來愈普及的 HTTP(S) streaming 技術來說,因為檔案一直產生,這就會是常常遇到的問題了。

由於 lock 機制增加了不少延遲,所以在使用者端就需要多抓一些緩衝時間才能確保品質,這增加了直播的互動延遲,所以 Cloudflare 改善了這個部分,讓所有人都可以同時下載,而非等到發起的使用者下載完才能下載:

沒有太多意外的,從 Cloudflare 內部數字可以看出來這讓 lock 時間大幅下降,對於使用者來說也大幅降低了 TTFB (time to first byte):

不確定其他家的情況...

Pornhub 想買 Tumblr?

看到 Pornhub 想要買 Tumblr 的新聞:「Pornhub wants to buy Tumblr and restore site to former porn-filled glory」。

如果是其他家買可能還沒感覺,但如果是 Pornhub 買的話真的有機會恢復往日榮光的感覺?當然本來已經被迫離開的那些人應該是不會回來...

詩篇的作者抱怨不知道自己詩篇考題的答案...

2017 年的文章,最近不知道為什麼冒出來,但還蠻有趣的...

看到「I Can’t Answer These Texas Standardized Test Questions About My Own Poems」這篇,Sara Holbrook 收到信件跑來問問題 (節錄前面的部份):

Hello Mrs. Holbrook. My name is Sean, and I’m an 8th grade English teacher in Texas. I’m attempting to decipher the number of stanzas in your poem, ‘Midnight’. This isn’t clear from the formatting in our most recent benchmark. The assessment asks the following question:

作者最後的抱怨也很有趣:

My final reflection is this: any test that questions the motivations of the author without asking the author is a big baloney sandwich. Mostly test makers do this to dead people who can’t protest. But I’m not dead.

I protest.

這邊其實也是在偷戳「作者之死」現象... 另外一則也有類似的情況,發生的早一點的台灣 (2016) XDDD

文學的過度解讀現象 XDDD

日本圍棋界使用 AWS 分析棋局的情況

看到「圍棋AI與AWS」這篇譯文,原文是「囲碁AIブームに乗って、若手棋士の間で「AWS」が大流行 その理由とは?」。

沒有太意外是使用 Leela Zero + Lizzle,畢竟這是 open source project,在軟體與資料的取得上相當方便,而且在好的硬體上已經可以超越人類頂尖棋手。

由於在 Lizzle 的介面上可以看到勝率,以及 Leela Zero 考慮的下一手 (通常會有多個選點),而且當游標移到這些選點上以後,還會有可能的變化圖可以看,所以對於棋手在熟悉操作介面後,可以很快的擺個變化圖,然後讓 Leela Zero 分析後續的發展,而棋手就可以快速判斷出「喔喔原來是這樣啊」。

網路上也有類似的自戰解說,可以看到棋手對 Lizzle 的操作與分析 (大約從 50:50 開始才是 Lizzle 的操作):

不過話說回來,幹壞事果然是進步最大的原動力... 讓一群對 AWS 沒什麼經驗的圍棋棋手用起 AWS,而且還透過 AMI 與 spot instance 省錢... XD

用 NN 演算法重製 Full HD 版的 Star Trek: DS9

看到「Remastering Star Trek: Deep Space Nine With Machine Learning」這篇,裡面用了類神經網路演算法,將本來只有 480p (SD) 的 Star Trek: DS9 升到 1080p (Full HD) 的版本,而且看起來效果還不錯...

意外的看到有人拿 Star Trek 的材料來玩... 依照作者的說明,DS9 一直沒有 Full HD 版的其中一個原因反而是因為「數位化」了。使用類比膠卷的母帶可以透過更高規格的重新掃描而得到高畫質版本,但 DS9 的母帶似乎已經是數位版了,所以反而造成無法透過重新掃描的方式取得 Full HD 版本:

While you can rescan analog film at a higher resolution, video is digital and can't be rescanned. This makes it much costlier to remaster this TV show, which is one of the reasons why it hasn't happened.

現有的 upscale 技術主要都還是以圖片為主,所以作者本來以為對於動態畫面的處理會遇到問題,但蠻意外的超出預期,從影片可以看出來:

看起來之後的 remaster 版本有可能可以靠這個方法先做初步,然後再讓人進去修?

AWS 推出了 Live 時全自動上字幕的功能

AWS 推出了在直播時就自動上字幕的功能:「Introducing Live Streaming with Automated Multi-Language Subtitling」,其實就是把現有的服務兜出來:「Live Streaming with Automated Multi-Language Subtitling」。

The solution deploys Live Streaming on AWS which includes AWS Elemental MediaLive, MediaPackage, Amazon CloudFront. The solution also deploys AWS Lambda, Amazon Simple Storage Service, Amazon Transcribe, and Amazon Translate.

對於比較沒那麼要求翻譯品質的情況也許可以玩看看...?