In The Hunt for Red October, the Soviet submarine captain played by Sean Connery commands his crew to verify the location of a target. <<MOVIE CLIP: "Give me a ping Vasily. One ping only please.">>

　　在电影《猎杀红色十月号》（1990年美国电影）中，演员Sean Connery扮演的前苏联潜艇艇长指挥手下确定一个目标的位置时说：“Vasily，请发射一个声脉冲信号，只要一个！”

　　That ping is known as "active sonar." Bob Headrick of the Office of Naval Research, the ONR, says it's the audio equivalent of switching on a flashlight. You're getting information, but also broadcasting your location to other ships.

　　艇长说的声脉冲信号就是“主动声纳”。美国海军研究办公室（ONR）的Bob Headrick说，这相当于打开一个手电筒，你在获取他人信息的同时也泄露了自己的位置。

　　"And you know the number one priority in the submarine is to remain undetected." Subs can keep their secrecy by eavesdropping on other ships instead… listening for propellers and electronics and so on. Such methods, known as "passive sonar,” generally require a skilled operator. But researchers are teaching machines to do it, too.

　　“你知道，潜艇的首要任务是保持隐身，不被发现。”潜艇通过侦听其他舰船的声音—如螺旋桨、电子设备的噪声--来保护自己的秘密位置。这种方法叫做“被动声纳”，通常需要配备一名技能熟练的操作员。但研究人员正在教计算机做这件事。

　　They first recorded the underwater rumblings of cargo ships off the California coast <<ship sound>> using an array of 28 underwater microphones. They fed that sound, along with the ships' actual GPS coordinates, to their machine learning algorithms. And then they gave the algorithms new recordings, and asked: where's the ship?

　　研究人员首先搭建了28个水听器组成的传感器阵，记录加利福尼亚海岸过往船只发出的隆隆声，并将这些声音信号和船只的实际GPS坐标“喂给”机器学习算法。给算法一些新的船只噪音，然后向它提问：现在船在哪里？

　　"And it did extremely well." Emma Ozanich, a PhD Student in underwater acoustics at the Scripps Institution of Oceanography. Using the audio data, she says the algorithms pinpointed the ships to within a couple hundred meters, at distances of up to 10 kilometers.

　　斯克利普斯海洋研究所水声专业的博士生Emma Ozanich认为“计算机做得非常好”。利用噪音数据，机器学习算法可以在长达10公里距离的范围里定位船只，误差在两三百米以内。

　　But it's not so clear what the machines now know. "One of the interesting parts about machine learning, especially neural networks, is that it's more difficult to pull out what it's actually learning specifically. It's a little bit of a black box." The research is in The Journal of the Acoustical Society of America. [Haiqiang Niu et al., Ship localization in Santa Barbara Channel using machine learning classifiers]

　　但人们还不清楚机器目前知道哪些信息。“机器学习特别是神经网络有趣的一个部分是很难把它具体学习的东西提取出来，这有点像是一个黑匣子。”研究成果近日发表于美国声学学报（牛海强等人，2017年11月）。

　　Bob Headrick of ONR says the data set used here is relatively simple, compared to the real-world scenarios subs would have to solve. Still, he says, with lots more development: "You could conceive with enough effort you create the computer program that can beat the trained operator."

　　ONR的Bob Headrick指出，与潜艇需要解决的实际问题相比，本次研究使用的数据相对简单，但随着进一步的发展，“你可以通过努力，研发出一个能打败训练有素的操作员的计算机程序。”

　　There is a precedent, after all, for machines defeating our best human operators. It was in that other great battle: the game of chess.

　　毕竟机器打败最聪明人类已有先例。那是另一场战役：国际象棋。（编辑注：1997年人机象棋大赛，IBM电脑“深蓝”打败了当时的世界棋王卡斯帕罗夫。）

　　来源：科学美国人专栏《科学60秒》

　　作者：Christopher Intagliata

　　编辑/翻译：王荣泉

　　校对：牛海强

　　报道原文：

　　Computers Learn to Use Sound to Find Ships - Scientific American https://www.scientificamerican.com/podcast/episode/computers-learn-to-use-sound-to-find-ships/

　　牛海强论文：

　　Ship localization in Santa Barbara Channel using machine learning classifiers

　　http://asa.scitation.org/doi/abs/10.1121/1.5010064