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機器人
前所未有！人工智能讓我們更清楚地觀看太空
Artificial intelligence could help us see farther into space than ever before
導讀:斯坦福大學和SLAC國家加速器實驗室的研究人員開發的新型神經網絡加快了求解速度，可以讓我們更清楚地觀看宇宙。
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.
時空的扭曲聽起來就像在《星際迷航》中的那樣，而不是現實世界中的這樣。然而，這不一定是正確的：分析引力波的圖像有助於擴展哈勃望遠鏡的範圍和分辨率，這使我們能夠更清楚地觀察宇宙。
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.
這是好消息嗎？應用人工智能神經網絡大大加快了求解速度——比以前的方法快了1000萬倍。這意味著人類專家數周甚至數月才能完成的分析，神經網絡隻需一秒鍾即可完成。
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.
斯坦福大學和SLAC國家加速器實驗室的研究人員開發的新型神經網絡可以分析所謂的引力透鏡效應。這個效應是阿爾伯特·愛因斯坦首先提出的，他認為像恒星這樣的巨大物質對圍繞它們的彎曲光線有效應。這種效應類似於望遠鏡，它能使我們更清晰地觀察遠處的物體。然而，與望遠鏡不同，引力透鏡會將物體扭曲成模煳的光環和弧線——因此，掌握它們需要計算機的計算能力。
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.
為了訓練他們的網絡，該項目的研究人員用了大約五十萬個重力透鏡的模擬圖像。之後，神經網絡能夠發現新的鏡頭並確定它們的性質——質量的分布以及背景星係的放大程度。
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.
類似大型巡天望遠鏡（LSST）這種項目，SLAC正在建設3.2億像素的相機，預計已知的強引力透鏡將從幾百增加到幾萬個，這個工作是在最完備的時機完成的。
"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》的論文合著者、博士後研究員Laurence Perreault Levasseur說：“我們沒有足夠的人用傳統方法及時分析這些數據，而神經網絡將幫助我們識別有趣的對象並快速分析它們。這將給我們更多時間來考慮關於宇宙的恰當問題。”
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，神經網絡甚至不需要超級計算機來運行：經過測試的神經網絡在iPhone上工作。這能比以往更詳細地研究宇宙？（答案是肯定的）因為有一個應用程序！
最後更新：2017-10-08 02:35:55