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机器人
前所未有！人工智能让我们更清楚地观看太空
Artificial intelligence could help us see farther into space than ever before
导读:斯坦福大学和SLAC国家加速器实验室的研究人员开发的新型神经网络加快了求解速度，可以让我们更清楚地观看宇宙。
Distortions in space-time sound like they’d be more of a concern on an episode of Star Trek than they would in the real world. However, that’s not necessarily true: analyzing images of gravitational waves could help enormously extend both the range and resolution of telescopes like Hubble, and allow us to see farther into the universe than has been possible before.
时空的扭曲听起来就像在《星际迷航》中的那样，而不是现实世界中的这样。然而，这不一定是正确的：分析引力波的图像有助于扩展哈勃望远镜的范围和分辨率，这使我们能够更清楚地观察宇宙。
The good news? Applying an artificial intelligence neural network to this problem turns out to accelerate its solution well beyond previous methods -- like 10 million times faster. That means that analysis which could take human experts weeks or even months to complete can now be carried out by neural nets in a fraction of a single second.
这是好消息吗？应用人工智能神经网络大大加快了求解速度——比以前的方法快了1000万倍。这意味着人类专家数周甚至数月才能完成的分析，神经网络只需一秒钟即可完成。
Developed by researchers at Stanford University and the SLAC National Accelerator Laboratory, the new neural network is able to analyze images of so-called gravitational lensing. This is an effect first hypothesized about by Albert Einstein, who suggested that giant masses such as stars have the effect of curving light around them. This effect is similar to a telescope in that it allows us to examine distant objects with more clarity. However, unlike a telescope, gravitational lenses distort objects into smeared rings and arcs -- so making sense of them requires the calculating abilities of a computer.
斯坦福大学和SLAC国家加速器实验室的研究人员开发的新型神经网络可以分析所谓的引力透镜效应。这个效应是阿尔伯特·爱因斯坦首先提出的，他认为像恒星这样的巨大物质对围绕它们的弯曲光线有效应。这种效应类似于望远镜，它能使我们更清晰地观察远处的物体。然而，与望远镜不同，引力透镜会将物体扭曲成模糊的光环和弧线——因此，掌握它们需要计算机的计算能力。
To train their network, researchers on the project showed it around half a million simulated images of gravitational lenses. After this was done, the neural net was able to spot new lenses and determine their properties -- down to how their mass was distributed, and how great the magnification levels of the background galaxy were.
为了训练他们的网络，该项目的研究人员用了大约五十万个重力透镜的模拟图像。之后，神经网络能够发现新的镜头并确定它们的性质——质量的分布以及背景星系的放大程度。
Given that projects like the Large Synoptic Survey Telescope (LSST), a 3.2-gigapixel camera currently under construction at SLAC, is expected to increase the number of known strong gravitational lenses from a few hundred to tens of thousands, this work comes at the perfect time.
类似大型巡天望远镜（LSST）这种项目，SLAC正在建设3.2亿像素的相机，预计已知的强引力透镜将从几百增加到几万个，这个工作是在最完备的时机完成的。
"We won’t have enough people to analyze all these data in a timely manner with the traditional methods," said postdoctoral fellow Laurence Perreault Levasseur, a co-author on the associated Nature research paper. "Neural networks will help us identify interesting objects and analyze them quickly. This will give us more time to ask the right questions about the universe."
《自然》的论文合着者、博士后研究员Laurence Perreault Levasseur说：“我们没有足够的人用传统方法及时分析这些数据，而神经网络将帮助我们识别有趣的对象并快速分析它们。这将给我们更多时间来考虑关于宇宙的恰当问题。”
Impressively, the neural network doesn’t even need a supercomputer to run on: one of the tested neural nets was designed to work on an iPhone. Studying the universe in greater detail than ever? Turns out there’s an app for that!
令人印象深刻的是，神经网络甚至不需要超级计算机来运行：经过测试的神经网络在iPhone上工作。这能比以往更详细地研究宇宙？（答案是肯定的）因为有一个应用程序！
最后更新：2017-10-08 02:35:55