本文摘要:“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.有些人总讨厌夸口说道:“我从不不会记得别人的相貌


“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.有些人总讨厌夸口说道:“我从不不会记得别人的相貌。”在人工智能研究突飞猛进的今天,还要这么夸口就有点怪异了。事实上,现在有些电脑能忘记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公开发表了一篇有关全新人工智能系统的研究论文。这一系统取名为FaceNet,谷歌堪称它是迄今为止最准确的人脸识别技术。

面临一个取名为“人面数据库”(Labeled Faces in the Wild)的常用人脸识别数据库时,FaceNet辨识的准确率几近百分之百。这个数据库容纳了网上收集的一万三千多张人脸照片。

而在面临一个所含2.6亿张人脸照片的可观数据库时,这个系统的准确率也多达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,面临“人面数据库”时,他们主要测试该系统的“证实能力”。就本质而言,他们取决于的是这套算法在辨别两张照片否同属一人时究竟有多精确。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,对这套数据库的辨识准确率多达99%。

去年,Facebook公司的研究人员公开发表论文称之为,他们也能做多达97%的准确率。根据这篇论文援引的一些研究人员的众说纷纭,人类对该数据库的辨识准确率仅有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,谷歌研究人员使用的方法决不只是证实两张脸否一样这么非常简单。

这套系统还能将人名和脸给定——经典的人脸识别技术,甚至能把看上去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.目前这还意味着是研究而已,但它伴随着,在不远处的将来,我们常常在网上视频或大片里看见的那种能严惩犯罪、强化监控的电脑将更为触手可及。相比在交友应用于Tinder上划来划去,它可能会使网上交友更为非常简单(也更加逗留于表面)。

Have a thing for Brad Pitt circa 1998? Here are the 500 profiles that look the most like him.很讨厌1998年左右时的布拉德o皮特?这个数据库里有500张看上去很像他的脸。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,我们不会看见谷歌的FaceNet及Facebook的DeepFace系统在各自的网络平台上运营。它们不会让用户更为便利地(或者说更为自动化地)给照片贴上标签,寻找要去找的人,因为这些算法告诉照片中的这个人是谁,即使这些照片并没姓名标记。

此外,这类系统还能让网络公司更为便利地基于照片人物的身份,来分析它们的用户社交网络,评判全球风行趋势及名人的热门程度。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获得这类变革,但与之类似的电脑系统早已无处不在。它们都所含一种取名为“深度自学”的人工智能技术。

事实证明,这种技术需要极为有效地已完成辨识物体(按照某些标准来看,机器在这方面早已比人类很强了)、辨识语音及解读书面文字等机器分辨任务。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,微软公司、百度和雅虎也在“深度自学”研究上投放重金。


贝宝公司则将其用作压制欺诈。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于是以将深度自学技术用作动态分析医疗图像,并获取诸如文本分析、计算机视觉及语音辨识这类云计算服务项目。近年来,Twitter、Pinterest,、Dropbox、雅虎和谷歌等公司都并购了一些专攻深度自学技术的创业公司。IBM公司刚并购了一家坐落于丹佛,取名为AlchemyAPI的初创企业,借以提高其Watson超级计算机的智能水平,并反对其全新的Bluemix云平台(该平台的理念是:开发者可以便利地将移动和网络应用与云服务连接起来,借此打造出一些智能应用于,而需要再行钻研人工智能背后简单的计算机科学)。

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.好比于此。随着消费级机器人、无人驾驶汽车及智能家居渐渐沦为现实,深度自学技术也将如影随形,为我们这些新的玩具获取耳目和一些头脑功能。

美国国防部高级研究计划局(DARPA)也在探寻如何利用深度自学技术来动态解读可观的情报信息流。Something tells me it’s looking at Google’s FaceNet and getting pretty excited, too.我庞加莱,DARPA正在注目谷歌的FaceNet系统,并为之兴奋。