每天推薦一個 GitHub 優質開源項目和一篇精選英文科技或編程文章原文，歡迎關注開源日報。交流QQ群：202790710；電報群 https://t.me/OpeningSourceOrg

今日推薦開源項目：《競答遊戲輔助工具TopSup》

推薦理由：最近 GitHub 上有一個很流行的開源項目是來自中國的一名程序員發起，競答遊戲輔助工具，相信在很多活動都可以都可以用得上。作者是 Skye

關於遊戲

沖頂大會：遊戲過程中由一名主持人出題，在線註冊觀眾答題，每期 12 道題，題目範圍將涵蓋科學、文化、綜藝、藝術等各個領域，有 10 秒作答時間。每天有兩次遊戲機會，通常為中午 1 點和晚上 9 點。全答對的人能分得獎金。

關於ADB

全名為 Android Debug Bridge ，直譯過來就是安卓調試橋，其實它的作用也就是搭起你的 android設備與 pc間的橋樑，讓你能用你的 pc操作管理你的 android設備或者模擬器。在這個項目中主要用來獲取截屏。

註：IOS使用的是 WDA ，詳情自行谷歌。

關於谷歌 Tesseract

開源 OCR 引擎，使用 c 和 c++ 編寫而成，主要用於識別圖像中的文本，目前已支持世界上大部分使用較廣的語言。當然，你也可以自己進行訓練。

OCR：Optical Character Recognition，光學字元識別，顧名思義，不加贅述。

Wiki: https://en.wikipedia.org/wiki/Tesseract_(software)

而該項目使用的是 Python-tesseract：封裝過的Tesseract

https://github.com/madmaze/pytesseract

關於百度 OCR

功能相仿，但使用它需要到百度平台上創建應用申請 API Key 和 Secret Key ，所以還是建議使用 Tesseract （=w=）。

工作原理

使用 ADB 截屏

代碼鏈接：https://github.com/Skyexu/TopSup/blob/master/common/screenshot.py

使用 OCR 識別文本

代碼鏈接：https://github.com/Skyexu/TopSup/blob/master/common/ocr.py

除了 Tesseract 的部分以外，還使用了灰度轉化的方法增加了識別準確率。

獲取結果，官方文檔對三種方式的介紹：

直接打開瀏覽器搜索問題
題目+每個選項都通過搜索引擎搜索，從網頁代碼中提取搜索結果計數
只用題目進行搜索，統計結果頁面代碼中包含選項的詞頻

代碼鏈接：https://github.com/Skyexu/TopSup/blob/master/common/methods.py

然後，因為沒法使用（某些特殊原因），所以沒有截圖，按照官方的說法，可能會識別錯誤導致你與勝利失之交臂，也有可能無法識別，總之，在有些時候，它可能還不如你使用語音搜索來的快。

今日推薦英文原文：《SETI: AI Helping Humanity Overcome Its Limitations》原作者：Tony Kontzer

原鏈接：https://blogs.nvidia.com/blog/2018/04/13/seti-using-ai/

推薦理由：搜尋地外文明計劃（SETI），是對所有在搜尋地外文明的團體的統稱，不是只代表一個組織。這其中較著名的有學術單位包括哈佛大學和柏克萊加州大學，非營利組織SETI協會。這些組織致力於用射電望遠鏡等先進設備接收從宇宙中傳來的電磁波，從中分析有規律的信號，希望藉此發現外星文明。而人工智慧跟 SETI 會碰撞出什麼火花呢？NVIDIA來告訴你。

SETI: AI Helping Humanity Overcome Its Limitations

Few organizations are as bullish on AI as the SETI Institute.

Best known for its ongoing search for extraterrestrial intelligence, the institute is engaged in broad range of complicated science. And for all of humanity』s natural intelligence, it』s the artificial sort that』s most likely to help us succeed long term.

「AI is the fastest way to move up to enlightenment,」 Graham Mackintosh, an AI consultant for space science applications at NASA-STC and SETI, told a roomful of attendees at last month』s GPU Technology Conference.

It』s also, Mackintosh said, critical to SETI』s mission.

Only 10 percent of the institute』s work involves the search for extraterrestrial intelligence. The other 90 percent of the time has SETI scientists busy doing everything from searching for new planets and monitoring the behavior of the sun to developing planetary instruments and studying how to create livable environments in the harshest conditions.

The thread that runs through these missions is our own limitations: We can』t possibly know what anomalies to look for that could interfere with our efforts. And while we』ve long been limited by our intelligence, AI is stretching the boundaries of our knowledge and understanding, a fact of which SETI is taking full advantage.

Regardless of the field in question — whether it』s planetary sciences, astrobiology, life sciences, cognitive sciences or any of several other sciences — SETI is applying AI to previously unattainable quests.

「AI, we believe, is going to transform every one of those,」 said Mackintosh.

In each case, SETI is training its models and doing inference on clusters of NVIDIA Tesla P100 GPUs in the IBM cloud. Some of the ways the institute is putting its AI system to use:

It』s crunching a vast amount of information generated by the Allen Telescope Array in Northern California. The telescope』s 42 receiving dishes, each 6 meters in diameter, crank out 4.5 TB of data each hour.
It』s modeling the shapes of asteroids in an attempt to predict where they will head over the coming decades, as even a slight variation in an asteroid』s shape can significantly alter its path through space.
It』s monitoring for 「long-period comets,」 which Mackintosh said are scary because they』re on such a slow cycle, with orbits that take eons. Prior to the use of AI, we』d never even seen them before.
It』s predicting the behavior of the sun, using HD images sent every 12 seconds from the Solar Dynamics Observatory that』s been observing the sun since 2010. It runs these through a neural network model to forecast what the sun will look like 24 hours into the future.
It』s simulating space missions and the conditions that humans, vehicles and scientific equipment will face. It does this by evaluating data collected on explorations of mountains that come closest to duplicating the surface of Mars, or on dives into the Arctic ice, where it develops and test tools used to explore icy planets.

More pressing is the directive NASA is under to send another team of astronauts to the moon — not to visit, but to stay permanently. To do so, SETI scientists are looking to AI to help detect small, crater-like caves that could house habitats safe from the dangerous radiation at the surface.

「It』s unbelievably hard to find those among all the craters, but a convolutional neural network will do a great job,」 said Mackintosh.

Which brings us back to the notion that we humans need help, and lots of it, if we』re going to answer some of our most burning questions about the universe, such as the search for extraterrestrial life. To find those answers, we have to be able to identify previously undetectable anomalies that could tip us off to the presence of life.

「Without AI, we』re really not going to be capable of joining the interstellar community,」 said Mackintosh. 「AI is the best tool to cast a wide net to look for anomalies that exist.」