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今日推荐开源项目：《竞答游戏辅助工具TopSup》

推荐理由：最近 GitHub 上有一个很流行的开源项目是来自中国的一名程序员发起，竞答游戏辅助工具，相信在很多活动都可以都可以用得上。作者是 Skye

关于游戏

冲顶大会：游戏过程中由一名主持人出题，在线注册观众答题，每期 12 道题，题目范围将涵盖科学、文化、综艺、艺术等各个领域，有 10 秒作答时间。每天有两次游戏机会，通常为中午 1 点和晚上 9 点。全答对的人能分得奖金。

关于ADB

全名为 Android Debug Bridge ，直译过来就是安卓调试桥，其实它的作用也就是搭起你的 android设备与 pc间的桥梁，让你能用你的 pc操作管理你的 android设备或者模拟器。在这个项目中主要用来获取截屏。

注：IOS使用的是 WDA ，详情自行谷歌。

关于谷歌 Tesseract

开源 OCR 引擎，使用 c 和 c++ 编写而成，主要用于识别图像中的文本，目前已支持世界上大部分使用较广的语言。当然，你也可以自己进行训练。

OCR：Optical Character Recognition，光学字符识别，顾名思义，不加赘述。

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

而该项目使用的是 Python-tesseract：封装过的Tesseract

https://github.com/madmaze/pytesseract

关于百度 OCR

功能相仿，但使用它需要到百度平台上创建应用申请 API Key 和 Secret Key ，所以还是建议使用 Tesseract （=w=）。

工作原理

使用 ADB 截屏

代码链接：https://github.com/Skyexu/TopSup/blob/master/common/screenshot.py

使用 OCR 识别文本

代码链接：https://github.com/Skyexu/TopSup/blob/master/common/ocr.py

除了 Tesseract 的部分以外，还使用了灰度转化的方法增加了识别准确率。

获取结果，官方文档对三种方式的介绍：

直接打开浏览器搜索问题
题目+每个选项都通过搜索引擎搜索，从网页代码中提取搜索结果计数
只用题目进行搜索，统计结果页面代码中包含选项的词频

代码链接：https://github.com/Skyexu/TopSup/blob/master/common/methods.py

然后，因为没法使用（某些特殊原因），所以没有截图，按照官方的说法，可能会识别错误导致你与胜利失之交臂，也有可能无法识别，总之，在有些时候，它可能还不如你使用语音搜索来的快。

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

原链接：https://blogs.nvidia.com/blog/2018/04/13/seti-using-ai/

推荐理由：搜寻地外文明计划（SETI），是对所有在搜寻地外文明的团体的统称，不是只代表一个组织。这其中较著名的有学术单位包括哈佛大学和柏克莱加州大学，非营利组织SETI协会。这些组织致力于用射电望远镜等先进设备接收从宇宙中传来的电磁波，从中分析有规律的信号，希望借此发现外星文明。而人工智能跟 SETI 会碰撞出什么火花呢？NVIDIA来告诉你。

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.”