MOUNTAIN VIEW, Calif. — Inside Google’s secretive X laboratory, known for inventing self-driving cars and augmented reality glasses, a small group of researchers began working several years ago on a simulation of the human brain.
山景城，加利福尼亚——在研发出自动驾驶汽车和增强现实眼镜的谷歌X实验室中，一个研究小组近年来一直致力于模拟人类大脑的运作模式。
There Google scientists created one of the largest neural networks for machine learning by connecting 16, 000 computer processors, which they turned loose on the Internet to learn on its own.
在这里，谷歌的科学家用16 000台计算机构建了世界上最大的机器学习神经网络系统——谷歌大脑，并让它在因特网上自主学习。在YouTube上看了数以千万记的视频之后，你猜谷歌大脑做了什么？与许多人类在YouTube上做的事情差不多——看猫。
The neural network taught itself to recognize cats, which is actually no frivolous activity. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20, 000 distinct items.
学会如何识别猫对于谷歌大脑来说并不是一件不值一提的小事。研究者们将在本周于在苏格兰爱丁堡举行的学术会议上发表他们的研究成果。尽管现如今，喵星人的视频在因特网上到处都是，但是谷歌的科学家和程序员仍然认为这一模拟结果是一个惊喜。与之前的成果相比，它能够更为精确地识别近20 000个不同的物体。
The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. It is leading to significant advances in areas as diverse as machine vision and perception, speech recognition and language translation.
这一研究有效利用了计算成本的下降和大型数据中心的海量计算机资源，这种研究方法已经成为新一代计算机科学研究的主流方法，并被广泛应用于机器视觉、机器知觉、语音识别和机器翻译等领域中。
Although some of the computer science ideas that the researchers are using are not new, the sheer scale of the software simulations is leading to learning systems that were not previously possible. And Google researchers are not alone in exploiting the techniques, which are referred to as “deep learning” models. Last year Microsoft scientists presented research showing that the techniques could be applied equally well to build computer systems to understand human speech.
尽管这一研究中运用的计算机科学的概念并不新奇，但是如此规模庞大的模拟在之前是不可想象的。在这一被称为“深度学习”的计算机模型的研究领域中，并不只有谷歌一家。去年微软科学家的研究表明，这一技术同样可以很好地应用于计算机系统的语音识别。
“This is the hottest thing in the speech recognition field these days, ” said Yann LeCun, a computer scientist who specializes in machine learning at the Courant Institute of Mathematical Sciences at New York University.
“这算得上是最近语音识别领域中最热门的东西了，”纽约大学科朗数学研究院的机器学习领域的计算机专家Yann LeCun如是说。
And then, of course, there are the cats.
接下来，我们说说喵的事情。
To find them, the Google research team, led by the Stanford University computer scientist Andrew Y. Ng and the Google fellow Jeff Dean, used an array of 16, 000 processors to create a neural network with more than one billion connections. They then fed it random thumbnails of images, one each extracted from 10 million YouTube videos.
斯坦福大学的计算机科学家Andrew Y. Ng和谷歌工程师Jeff Dean带领的谷歌研究团队，用16 000个处理器的阵列构建了一个超过十亿个节点的神经网络。然后，他们从一千万YouTube视频中选取缩略图给这一神经网络。
The videos were selected randomly and that in itself is an interesting comment on what interests humans in the Internet age. However, the research is also striking. That is because the software-based neural network created by the researchers appeared to closely mirror theories developed by biologists that suggest individual neurons are trained inside the brain to detect significant objects.
由于这些视频是随机选取的，因此它们代表了这一互联网时代最吸引人的东西。但是这一研究仍然是令人振奋的。基于软件的神经网络的运行方式与生物学家的关于大脑的神经网络的运行理论相似，即每个单独的神经单元都尽可能的找寻重要的物品。
Currently much commercial machine vision technology is done by having humans “supervise” the learning process by labeling specific features. In the Google research, the machine was given no help in identifying features.
现如今商用的机器视觉技术都是在人类的“督查”下完成的，即在学习的过程中人为地指定特定的表征。而在谷歌的研究中，机器独立地识别和定义物品的特征。
“The idea is that instead of having teams of researchers trying to find out how to find edges, you instead throw a ton of data at the algorithm and you let the data speak and have the software automatically learn from the data, ” Dr. Ng said.
“这一理念认为：与其投入大量的人力物力努力教会机器定义物品的边缘和特征什么的，还不如将大量的数据塞给软件算法，让数据说话，软件自己会从海量数据中自我学习，” Ng博士说。
“We never told it during the training, ‘This is a cat, ’ ” said Dr. Dean, who originally helped Google design the software that lets it easily break programs into many tasks that can be computed simultaneously. “It basically invented the concept of a cat. We probably have other ones that are side views of cats.”
“在整个训练过程中，我们从来没告诉过计算机：‘这种有四条腿一条尾巴的形态姿势各异的诡异萌物叫做猫’，”Dean博士，最初在谷歌负责将已将程序转变为多任务同时进行。“它自己发明了‘喵’的概念，可能还有‘喵的侧面’的概念。”
The Google brain assembled a dreamlike digital image of a cat by employing a hierarchy of memory locations to successively cull out general features after being exposed to millions of images. The scientists said, however, that it appeared they had developed a cybernetic cousin to what takes place in the brain’s visual cortex.
在看过了数百万的图像之后，谷歌大脑通过对记忆的辨识和特征筛选，最终“梦”到了一只猫。科学家认为他们建立了一个与大脑皮层视觉中枢类似的控制论模型。
Neuroscientists have discussed the possibility of what they call the “grandmother neuron, ” specialized cells in the brain that fire when they are exposed repeatedly or “trained” to recognize a particular face of an individual.
神经学家也正在讨论被他们称作“祖母神经元”存在的可能性，即大脑中特定的神经细胞，在不断重复地看到某一面部图像时被激活。“天天见就能混个脸熟，”加利福尼亚州帕洛阿尔托的Industrial Perception的Gary Bradski说。
While the scientists were struck by the parallel emergence of the cat images, as well as human faces and body parts in specific memory regions of their computer model, Dr. Ng said he was cautious about drawing parallels between his software system and biological life.
除了猫的图像以外，人脸和部分人体的图像也出现在这一计算机系统的记忆模块中。尽管取得了这一系列激动人心的结果，Ng博士对于将这一软件模拟系统与生物大脑的类比持谨慎态度。
“A loose and frankly awful analogy is that our numerical parameters correspond to synapses, ” said Dr. Ng. He noted that one difference was that despite the immense computing capacity that the scientists used, it was still dwarfed by the number of connections found in the brain.
“将我们的数字参数比喻做神经突触是一个牵强的甚至可以说糟糕的类比，”Ng博士说。他进一步之处，尽管研究者利用了一个极大的计算机系统，但是它的节点数与大脑相比仍然相形见绌。
“It is worth noting that our network is still tiny compared to the human visual cortex, which is a million times larger in terms of the number of neurons and synapses, ” the researchers wrote.
“值得一提的是，我们的神经网络与人类大脑皮层视觉中枢相比仍然是个小不点儿。无论从神经元数量还是突触数量而言，大脑都比我们的系统大百万倍，”研究者写道。
Despite being dwarfed by the immense scale of biological brains, the Google research provides new evidence that existing machine learning algorithms improve greatly as the machines are given access to large pools of data.
尽管远远比不上生物大脑，但是谷歌的研究表明，机器学习算法在学习了大量数据之后仍然有了很大的提升。
“The Stanford/Google paper pushes the envelope on the size and scale of neural networks by an order of magnitude over previous efforts, ” said David A. Bader, executive director of high-performance computing at the Georgia Tech College of Computing. He said that rapid increases in computer technology would close the gap within a relatively short period of time: “The scale of modeling the full human visual cortex may be within reach before the end of the decade.”
“斯坦福/谷歌论文通过一个更大的网络将神经网络的构建向前推进了一步，”佐治亚理工学院计算机学院的高性能计算的执行董事David A. Bader说。他认为计算机技术的迅速发展可以快速拉近计算机系统与大脑之间的差距：“也许在十年内我们就能够模拟完整的大脑皮层视觉中枢。”
Google scientists said that the research project had now moved out of the Google X laboratory and was being pursued in the division that houses the company’s search business and related services. Potential applications include improvements to image search, speech recognition and machine language translation.
谷歌科学家声称，这一谷歌X实验室的研究工作正在公司中的搜索等相关业务中的得到应用。潜在的应用主要包括图片搜索、语音识别和机器翻译。
Despite their success, the Google researchers remained cautious about whether they had hit upon the holy grail of machines that can teach themselves.
尽管取得了不错的进展，谷歌的研究者仍然对于他们是否真的得到了让机器自我学习的圣经持谨慎态度。“如果我们的现有算法没有任何问题，需要做的只是将网络变得更大而已的话，那就太完美了。但是直觉告诉我，我们还没有找到完美的算法。”Ng博士说道。
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