人工智能:追踪世界野生动物足迹 分享到
Artificial Intelligence Used to Track World’s Wildlife 2018-07-0367111

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Scientists have long struggled with how to measure the effects of climate change on wildlife. This is especially true for birds flying in and out of coastal areas bordering the Arctic Ocean.
长期以来,科学家们一直在致力研究如何衡量气候变化对野生动物的影响。这对于在北冰洋周边的沿海地区飞行的鸟类来说尤其如此。
In the past, researchers depended mainly on information gathered by satellite to follow the movement of birds and animals. But this method can be costly and result in huge amounts of information, which can be difficult to process.
过去,研究人员主要依靠卫星收集的信息来跟踪鸟类和动物的活动。但是这种方法代价很高,而且会导致大量的信息,难以处理。
Now scientists are turning to another kind of technology to help them follow birds and other wildlife. Songbirds are especially difficult to study in the wild. They can fly across thousands of kilometers and their bodies are too small for electronic tracking devices.
现在科学家们正转向另一种技术来帮助他们跟踪鸟类和其他野生动物。在野外研究鸣鸟格外困难。鸣鸟可以飞行数千公里,对于电子跟踪设备而言,它们的体型太小。
A new report tells about a project in which researchers recorded songbirds and then put the sounds into a computer program to be studied.The researchers created machine learning algorithms with the ability to focus on bird calls while ignoring other noises. The algorithm helps improve the likelihood of predicting the correct result. Similar machine learning methods have been used to recognize human speech.
一份新的报告讲述了一个项目,在这个项目中,研究人员录下了鸣鸟的声音,然后将其输入电脑程序进行研究。研究人员发明了一种机器学习算法,能够专注于鸟叫声而忽略其他声音。该算法有助于提高预测正确结果的可能性。类似的机器学习方法也被用来识别人类的语言。
Birds are known to make more singing and calling noises after arriving in areas where they plan to mate. Spring is short in extreme northern areas of the American state of Alaska. There, the birds must mate and have their young before winter.
众所周知,鸟类在到达它们计划交配的地方会发出更多的鸣叫声和呼唤声。在美国阿拉斯加州最北部的地区,春天十分短暂。在那里,鸟类必须在冬天之前交配并繁殖后代。
The algorithm is designed to use the recordings to estimate when the birds arrive in large groups to begin mating.
该算法的设计旨在利用这些录音来预估鸟类何时成群到达并开始交配。
A lead researcher on the project is Ruth Oliver from Columbia University in New York. Oliver told VOA that she and others traveled to northern Alaska to put recording equipment in four different places. The equipment was used to make four half-hour recordings each day. This produced more than 1,200 hours of audio, but not all of it was from birds.
纽约哥伦比亚大学的露丝·奥利弗是该项目的首席研究员。奥利弗对美国之音说,她和其他人前往阿拉斯加北部,将录音设备放在四个不同的地方。这些设备每天录制四个半小时的录音。设备生成了超过1200小时的音频,但并非全部来自鸟类。
“There’s a lot of other noise in these recordings. Even in May, in northern Alaska, there’s lots of wind, lots of rain, and all of that is confounding when you’re listening to birds.”
“录音中还有很多其他噪音。即使是5月的阿拉斯加北部,也有不少风声和雨声,在听鸟叫的时候,这些声音会让人迷惑。”
Oliver adds that the machine learning method is a valuable tool to help identify and process the most useful parts of the recordings.
奥利弗补充说,机器学习方法是一种有价值的工具,可以帮助识别和处理录音中最有用的部分。
While some results were produced completely by machine, Oliver said human supervision helped to improve the process. In both cases, the researchers reported the computer was correct in estimating when the birds arrived to their breeding grounds.
虽然有些结果完全是由机器产生的,但奥利弗说,人工监督有助于改进这一过程。在两者作用下,研究人员报告说计算机对鸟类何时到达它们的繁殖地的预测是正确的。
Oliver says the unsupervised machine learning method could be retooled to use sounds to measure the movements of other wildlife.“This could allow us to track large-scale changes in how animals are responding to climate change,” she said.
奥利弗说,这种无人监督的机器学习方法可以重新调整,即用声音来测量其他野生动物的运动。她说:“这可以让我们追踪动物在应对气候变化方式中发生的大规模变化。”
Emily Jo Williams is a bird expert with the American Bird Conservancy, a non-profit group.She told VOA she agrees that machine learning can provide scientists with valuable information about how climate changes are affecting animal populations.
艾米丽·乔·威廉姆斯是美国鸟类保护协会这一非营利组织的鸟类专家。她对美国之音表示,她认为机器学习可以为科学家提供有关气候变化如何影响动物种群的宝贵信息。
“We know from some research that some birds’ ranges have actually changed, and they’ve moved in response to what we think is a warming climate."
“我们从一些研究中了解到,一些鸟类的种类实际上已经发生了变化,我们认为气候在变暖,而鸟类也采取了行动。”
In another study, researchers reported how machine learning methods can be used to identify, count and describe animals in the wild.
在另一项研究中,研究人员报告了如何使用机器学习方法来对野生动物进行识别、计数和描述。
Currently, scientists depend mainly on cameras in the wild to keep track of animals. But just like the first study, the second also points out the problems of trying to deal with the large amounts of information gathered.
目前,科学家主要依靠野外的摄像机来追踪动物。但就像第一个研究一样,第二个研究也指出了需要处理收集到的大量信息这一问题。
Processing this data can be costly and involve a lot of people.
处理这些数据的成本很高,涉及人数众多。
Researchers created a machine learning system they say uses artificial intelligence or AI to identify,count, and describe the behaviors of 48 animal species. They said their method can correctly identify animals at a rate of 94 percent.
研究人员发明了一种机器学习系统,他们说该系统使用人工智能(AI)来对48种动物的行为进行识别、计数和描述。他们说,这种方法可以正确识别出94%的动物。
The scientists said results showed AI can be used as an effective way to gather “a wealth of information” on huge numbers of animals in the wild. They say the new methods will save time and costs, and can even operate in real-time.
科学家们说,研究结果表明,人工智能可以作为收集众多野生动物“大量信息”的有效途径。他们说,新方法将节约时间和成本,甚至可以进行实时操作。
I’m Bryan Lynn.
布莱恩·林恩为您播报。

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