In "The Hunt for Red October," the soviet submarine captain played by Sean Connery commands his crew to verify the location of a target.
在影片《獵殺紅色十月》中，肖恩·康納利扮演的蘇聯潛艇艦長命令船員確認目標方位。(電影片段：“瓦西里，發個‘呯’聲，發一聲即可。”)
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.
這種聲音就是所謂的主動聲吶。海軍研究辦公室的鮑勃·海德利克表示，發出這種聲音與打開手電筒無異，雖然會獲得信息，但是也向其他船隻透露了自身位置。
"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.
“駕駛潛艇，保持航行路線不爲人知是重中之重。”潛艇收聽推進器和電子元件的聲音，監聽其他船隻位置，保持行駛路線的隱祕性。以上方法又稱“被動聲吶”，通常只有訓練有素的.操作員才能掌握。不過研究人員現在正在教授機器這項技術。
They first recorded the underwater rumblings of cargo ships off the California coast <> 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?
他們在加利福尼亞灣水面下安裝了28個麥克風，記錄加利福尼亞灣貨船所發出的轟鳴聲。他們將這種聲音加入機器學習算法，然後爲算法播放一段新錄音，並對其提問：船在哪裏?
"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.
斯克裏普斯海洋研究所水中聲學專業博士生艾瑪·歐扎尼齊說：“計算機算法表現得不錯。”她表示，藉助音頻數據，算法可在長達10千米的距離中將船隻方位限定在幾百米之內。
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.
然而人們仍不清楚機器究竟學會了什麼。“人們發現，機器學習，尤其是神經網絡的具體學習內容更加難以確定，這正是其耐人尋味的原因之一，它與黑匣子有些許類似。”這份研究報告發表在《美國聲學協會期刊》上。
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."
海軍研究辦公室的鮑勃·海德利克表示，相比於現實中潛艇需要解決的問題，這些設定數據要相對簡單一些。儘管如此，這也有了長足進步：“人們可以絞盡腦汁，設想自己設計出一種電腦程序，足以擊敗訓練有素的專業人士。”
There is a precedent, after all, for machines defeating our best human operators. It was in that other great battle of the Cold War: the game of chess.
畢竟機器在特定領域戰勝人類早有先例，而該領域正是冷戰時期另一大戰場：國際象棋。
