谷歌推出史上最强人脸识别系统

“I never forget a face,” some people like to boast. It’s a claim that looks quainter by the day as artificial intelligence research continues to advance. Some computers, it turns out, never forget 260 million faces.
有些人总喜欢夸口说：“我从来不会忘记别人的长相。”在人工智能研究突飞猛进的今天，还要这么夸口就有点奇怪了。事实上，现在有些电脑能记住2.6亿张脸。

Last week, a trio of Google GOOG -0.66% researchers published a paper on a new artificial intelligence system dubbed FaceNet that it claims represents the most-accurate approach yet to recognizing human faces. FaceNet achieved nearly 100-percent accuracy on a popular facial-recognition dataset called Labeled Faces in the Wild, which includes more than 13,000 pictures of faces from across the web. Trained on a massive 260-million-image dataset, FaceNet performed with better than 86 percent accuracy.
上周，谷歌公司的三位研究人员发表了一篇有关全新人工智能系统的研究论文。这一系统名为FaceNet，谷歌号称它是迄今为止最精确的人脸识别技术。面对一个名为“人面数据库”（Labeled Faces in the Wild）的常用人脸识别数据库时，FaceNet识别的准确率近乎百分之百。这个数据库容纳了网上搜集的一万三千多张人脸照片。而在面对一个含有2.6亿张人脸照片的庞大数据库时，这个系统的准确率也超过86%。

Researchers benchmarking their facial-recognition systems against Labeled Faces in the Wild are testing for what they call “verification.” Essentially, they’re measuring how good the algorithms are at determining whether two images are of the same person.
研究人员声称，面对“人面数据库”时，他们主要测试该系统的“确认能力”。就本质而言，他们衡量的是这套算法在判断两张照片是否同属一人时到底有多准确。

In December, a team of Chinese researchers also claimed better than 99 percent accuracy on the dataset. Last year, Facebook researchers published a paper boasting better than 97 percent accuracy. The Facebook FB 1.66% paper points to researchers claiming that humans analyzing images in the Labeled Faces dataset only achieve 97.5 percent accuracy.
去年12月，一个中国研究团队也声称，对这套数据库的识别准确率超过99%。去年，Facebook公司的研究人员发表论文称，他们也能做到超过97%的准确率。根据这篇论文援引的一些研究人员的说法，人类对该数据库的识别准确率仅有97.5%。

However, the approach Google’s researchers took goes beyond simply verifying whether two faces are the same. Its system can also put a name to a face—classic facial recognition—and even present collections of faces that look the most similar or the most distinct.
不过，谷歌研究人员采用的方法绝不只是确认两张脸是否一样这么简单。这套系统还能将人名和脸匹配——经典的人脸识别技术，甚至能把看起来最像或最不像的脸归集在一起。

This is all just research, but it points to a near future where the types of crime-fighting, or surveillance-enhancing, computers we often see on network television and blockbuster movies will be much more attainable. Or perhaps a world where online dating is even simpler (and shallower) than swiping left or right on Tinder.
目前这还仅仅是研究而已，但它预示着，在不远的将来，我们经常在网上视频或大片里看到的那种能惩治犯罪、加强监控的电脑将更加触手可及。比起在交友应用Tinder上划来划去，它可能会使网上交友更加简单（也更停留于表面）。

Have a thing for Brad Pitt circa 1998? Here are the 500 profiles that look the most like him.
很喜欢1998年左右时的布拉德o皮特？这个数据库里有500张看起来很像他的脸。

At first we’ll see systems like Google’s FaceNet and Facebook’s aforementioned system (dubbed “DeepFace”) make their way onto those company’s web platforms. They will make it easier, or more automatic, for users to tag photos and search for people, because the algorithms will know who’s in a picture even when they’re not labeled. These types of systems will also make it easier for web companies to analyze their users’ social networks and to assess global trends and celebrity popularity based on who’s appearing in pictures.
一开始，我们会看到谷歌的FaceNet及Facebook的DeepFace系统在各自的网络平台上运行。它们会让用户更加方便地（或者说更加自动化地）给照片贴上标签，找到要找的人，因为这些算法知道照片中的这个人是谁，即使这些照片并没有姓名标记。此外，这类系统还能让网络公司更加方便地基于照片人物的身份，来分析它们的用户社交网络，评判全球流行趋势及名人的受欢迎程度。

Though Google and Facebook’s advances in facial recognition are relatively new, computer systems like this can be found all around us today. They incorporate an artificial intelligence technique called deep learning, which has proven remarkably effective at so-called machine perception tasks such as recognizing objects (by some metrics, machines are now better at this than are people), recognizing voices, and understanding the content of written text.
尽管谷歌和Facebook在人脸识别技术上最近才取得这类进步，但与之类似的电脑系统早就无处不在。它们都含有一种名为“深度学习”的人工智能技术。事实证明，这种技术能够极其有效地完成识别物体（按照某些标准来看，机器在这方面已经比人类要强了）、识别语音及理解书面文字等机器辨别任务。

Aside from Google and Facebook, companies including Microsoft MSFT 0.32% , Baidu, and Yahoo YHOO 0.63% are also investing heavily in deep learning research. The algorithms already power everyday features such as voice control on smartphones, Skype Translate, predictive text-messaging applications, and advanced image-searching. (If you have images uploaded to a Google+ account, go ahead and search them for specific objects.) Spotify and Netflix NFLX -0.82% are investigating deep learning to power smarter media recommendations. PayPal EBAY -0.13% is using it to fight fraud.
除了谷歌和Facebook外，微软、百度和雅虎也在“深度学习”研究上投入重金。这种算法已经应用在一些我们常用的功能上了，比如智能手机语音控制、Skype实时翻译、短信预测输入法及先进的图像搜索等（如果你已经将一些图片上传至Google+账户里，你就可以试试用它们来搜索特定目标）。Spotify和Netflix公司正在研究如何利用深度学习技术更智能地推荐视频。贝宝公司则将其用于打击欺诈。

There are also several technology startups using deep learning to analyze medical images in real time, and to provide capabilities such as text analysis, computer vision, and voice recognition as cloud computing services. Twitter, Pinterest, Dropbox, Yahoo, and Google have all acquired deep learning startups in recent years. And IBM IBM -0.08% just bought a Denver-based startup called AlchemyAPI to help make its Watson system smarter and bolster its new Bluemix cloud platform. (The idea: Developers can easily connect mobile and web applications to cloud services and therefore build smart applications without ever studying the complex computer science that underpins artificial intelligence.)
还有几家科技创业公司正将深度学习技术用于实时分析医疗图像，并提供诸如文本分析、计算机视觉及语音识别这类云计算服务项目。近年来，Twitter、Pinterest,、Dropbox、雅虎和谷歌等公司都收购了一些专攻深度学习技术的创业公司。IBM公司刚刚收购了一家位于丹佛，名为AlchemyAPI的初创企业，用以提升其Watson超级计算机的智能水平，并支持其全新的Bluemix云平台（该平台的理念是：开发者可以方便地将移动和网络应用与云服务连接起来，借以打造一些智能应用，而无需再钻研人工智能背后复杂的计算机科学）。

That’s not all. As consumer robots, driverless cars and smart homes become real, deep learning will be there, too, providing the eyes, ears, and some of the brains for our new toys. DARPA, the U.S. Department of Defense’s research agency, is also investigating how deep learning techniques might be able to help it make sense of the streams of communications crossing intelligence networks everyday.
不止于此。随着消费级机器人、无人驾驶汽车及智能家居逐渐成为现实，深度学习技术也将如影随形，为我们这些新玩具提供耳目和一些头脑功能。美国国防部高级研究计划局（DARPA）也在探索如何借助深度学习技术来实时理解庞大的情报信息流。

Something tells me it’s looking at Google’s FaceNet and getting pretty excited, too.
我猜想，DARPA正在关注谷歌的FaceNet系统，并为之激动。