本文摘要：The immense processing power of Googles global computing network and the brainpower of its secretive Google X research labs remain largely hidden from a curious world. But this week we were given a glimpse of what the companys great minds,

The immense processing power of Googles global computing network and the brainpower of its secretive Google X research labs remain largely hidden from a curious world. But this week we were given a glimpse of what the companys great minds, human and electronic, are thinking about: cats.谷歌全球计算出来网络的强劲信息处理能力以及谜样的Google X实验室中的技术天才很少为外界熟知。但上周我们幸运地一睹该公司的强劲头脑（不管是人脑还是电脑）在想要什么：猫。Google scientists built the worlds biggest electronic simulation of a brain, running on 16,000 computer processors, and discovered what it would learn when exposed to 10m clips randomly selected from YouTube videos. Unprompted, the computer brain taught itself to identify the feline face.谷歌科学家们用1.6万块电脑处理器建构了全球仅次于的电子仿真神经网络，并通过向其展出自YouTube上随机挑选的1000万段视频，实地考察其需要教给什么。结果显示，在无外界指令的自发性条件下，该人工神经网络自律学会了辨识猫的面孔。

That might seem a trivial accomplishment, demonstrating little more than the obsession of cat owners with posting videos of their pets. But in fact Google has made a significant advance in artificial intelligence, a research field that has promised much but delivered little to computer users.或许这看上去只是荒谬的成就，除了指出猫主人们热衷上载宠物视频之外，解释没法更加多问题。但实质上该成果指出谷歌在人工智能领域已获得重大进展。对电脑用户而言，人工智能研究仍然前景辽阔，但目前为止成果寥寥。

In their presentation at a machine learning conference in Edinburgh, the Google researchers demonstrated the companys ambitions in AI as well as the strength of its computing resources.在爱丁堡一个关于机器学习的会议上，谷歌研究人员所作的展示指出该公司在人工智能领域雄心勃勃，并有极为强劲的计算资源作为承托。Standard machine learning and image recognition techniques depend on initial training of the computer with thousands of labelled pictures, so it starts off with an electronic idea of what, say, a cats face looks like. Labelling, however, requires a lot of human labour and, as the Google researchers say, there is comparatively little labelled data out there.标准的机器学习以及图像识别技术依赖数以千计带上标签的图片，对电脑展开初始训练，使电脑从一开始就对猫脸宽什么样有一个概念。但是给图片加标签必须花费大量人力，并且正如谷歌研究人员所说，带上标签的数据比较受限。

Google needs to master what it calls self-taught learning or deep learning, if it is to extend its search capabilities to recognise images among the vast volume of unstructured and unlabelled data. That would enable someone who, for example, owned an unidentified portrait painted by an unknown artist to submit a photograph of it to a future Google – and stand a reasonable chance of having both the scene and the painter identified through comparison with billions of images across the internet.为将搜寻能力扩展至面向海量非结构化及无标签数据的图像识别领域，谷歌必须掌控其所谓的自学或深度自学技术。利用此类技术，未来如果某人有一幅出自于知道名画家的刻画知道何处风景的画作，他可将此所画的照片上载谷歌，经谷歌将其与互联网上数十亿收的图像展开核对后，此人有非常好的机会得知风景所在地与画家身份。The study presented this week is a step towards developing such technology. The researchers used Google data centres to set up an artificial neural network with 1bn connections and then exposed this newborn brain to YouTube clips for a week, without labelling data of any sort.谷歌上周展出的研究成果，就是向研发此类技术迈进的一步。研究人员利用谷歌数据中心，建构具备10亿个相连的人工神经网络，后用一周时间让这个新生大脑认识YouTube视频片段，而并未以任何方式贴标签。

本文关键词：贝博,app,贝博app官网,官网,科技,资讯,谷歌,神经网络,识别

本文来源：贝博app官网-www.ambikaassociates.com