人工智能在对非洲象进行统计中发挥了关键作用

人工智能在对非洲象进行统计中发挥了关键作用

There are two kinds of elephants in Africa: the forest elephant and the savanna elephant (above), photographed this past spring in Liwonde National Park in Malawi. The Great Elephant Census found that Africa's savanna elephant population decreased by about a third in the seven years between 2007 and 2014.

非洲有两种大象:森林象和草原象(上图)，这是今年春天在马拉维利旺德国家公园拍摄的。大象普查发现，在2007年至2014年的七年间，非洲草原大象的数量减少了约三分之一。

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years.

几年前，微软的联合创始人保罗·艾伦公布了一项名为“大象普查”的调查结果，这个调查统计了所有草原大象的数量。它的发现震惊了自然保护界:在2007年至2014年的七年间，非洲草原大象的数量减少了约三分之一，而且在短短10年内就会从一些非洲国家完全消失。

To reverse that trend, researchers landed on a technology: artificial intelligence. AI's ability to find regularity in enormous volumes of information is demystifying not just elephant behavior but human behavior — specifically poacher behavior — too.

为了扭转这一趋势，研究人员开发了一种技术：人工智能。人工智能在海量信息中发现规律的能力不仅揭开了大象行为的神秘面纱，也揭开了人类行为，特别是偷猎者行为的神秘面纱。

"AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege. "And ideally tell us what they are doing."

“人工智能可以处理大量的信息，告诉我们大象在哪里，有多少大象，”康奈尔大学的研究员彼得·瑞格说。“最好告诉我们他们在做什么。”

There are two kinds of elephants in Africa: savanna elephants, which were counted by Allen's census, and forest elephants, which the census couldn't account for because that elephant lives beneath a thick rainforest canopy.

非洲有两种大象:草原象(由艾伦的普查统计得到)和森林象(由于大象生活在浓密的雨林树冠下，无法普查这两种大象的数量)。

Even at the level of the jungle, Wrege says, losing a forest elephant is easy to do. "Sometimes you see them, let's say, 15 meters [16 yards] away from you and then they move 5 meters into the forest and you can't see them," he said. "Somehow they just disappear."

瑞格说，即使在丛林中，失去一头森林象也很容易。他说:“有时候在离你15米(16码)远的地方看到它们，然后它们往森林里移动了5米，你就看不到它们了。”“它们不知怎么就消失了。”

Researchers at Cornell University have been studying the forest elephant for years, trying to figure out — like Allen did with the savanna elephant — how many there are and how fast they are being killed. Given how stealthy the forest elephants are, Wrege began to think that rather than look for them, maybe he should try something a little different: Maybe he should listen for them.

康奈尔大学的研究人员多年来一直在研究森林象，就像艾伦研究草原象一样，试图弄清楚它们的数量和被捕杀的速度。考虑到森林里的大象是多么的隐蔽，瑞格开始思考与其寻找它们，不如尝试一些不同的方法:也许他应该倾听它们的声音。

To do this, Wrege had 50 custom audio recorders made. He divided the rainforest into a grid and headed to Central Africa. His team hung the custom recorders every 23 feet to 30 feet in the treetops, just a little higher than an elephant could reach with its trunk while standing on its hind legs. Three months later, they would return to the forest, locate the recorders, change the batteries, put in new audio cards, and start all over again.

为此，瑞格定制了50台录音机。他将雨林分成网格状，然后前往中非。他的团队在树梢上每隔23到30英尺悬挂一个定制的记录器，这个高度仅比大象用后腿站立时用鼻子所能到达的高度高一点。三个月后，他们回到森林，找到录音机，换电池，放上新的声卡，再次重复之前的步骤。

As the months wore on, the recorders were collecting hundreds of thousands of hours of jungle sounds, more than any team of graduate students could realistically listen to — which meant Wrege had another problem: How could he sort through all these recordings to find the elephant voices he wanted?

几个月过去了，录音机收集了数十万小时的丛林声音，比任何一个研究生小组实际能听到的都要多——这意味着瑞格还有另一个问题:他如何才能从这些录音中找出他想要的大象的声音呢?

The computer scientist Josephine Wolff, who is now a professor at the Fletcher School at Tufts University. "At a party with a lot of background noise, the human brain can focus on a specific person's voice and amplify that above all the other voices. AI can do the same thing."

计算机科学家约瑟芬·沃尔夫现在是塔夫茨大学弗莱彻学院的教授。“在一个有很多噪音的聚会上，人类的大脑可以专注于一个特定的人的声音，并将其放大到所有其他声音之上。人工智能也能做到这一点。”

It is particularly good at working with images, so Wrege ran the audio through a software program that turned the recordings he had collected into spectrograms. He then had a company in Santa Cruz, Calif., build him a neural network that could sort through the cacophony of jungle sounds and find elephants.

它特别擅长处理图像，因此瑞格通过一个软件程序运行音频，该程序将他收集的录音转换成声谱图。后来，他在加州圣克鲁斯有一家公司，这家公司为他建立了一个神经网络，可以从丛林里的各种杂音中找出大象。