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In 2020, Lisa Kaltenegger, an exoplanet astrophysicist and director of the Carl Sagan Institute at Cornell University, and collaborator Dang Pham wondered if machine learning systems could be trained to pinpoint life-enabling resources like water -- something ExoMiner cannot do.
2020年，系外行星天文物理学家、康乃尔大学卡尔萨根研究所所长丽莎·卡尔特内格和合作者范滕想知道是否可以训练机器学习系统来精确定位水等孕育生命的资源，这是Exo Miner无法做到的。
"If you find ice, you can infer water," Kaltenegger says. "If you can find clouds, you infer water. So we asked, how good is it in finding water, clouds, and ice?"
“如果你找到冰，你就可以推断出水，”卡尔特内格说。“如果你能找到云，你就能推断出水。所以我们发问，寻找水、云和冰有多好？
Kaltenegger and Pham used measurements of the Earth's atmosphere to simulate exoplanets with a rocky surface, water, clouds, and ice.
卡尔特内格和范滕利用地球大气层的测量来模拟具有岩石表面、水、云和冰的系外行星。
They also trained an algorithm to look for a sign of life called a red edge, wavelengths of light that plants reflect back into space.
他们还训练了一种算法来寻找生命迹象，称为红边，即植物反射回太空的光波长。
They found their software could detect the existence of life in a simulated atmosphere about three-quarters of the time, which could greatly improve the initial hunt for another Earth.
他们发现他们的软件可以在大约四分之三的时间内检测到模拟大气中生命的存在，这可以极大地改善对另一个地球的最初搜寻。
"I thought it would be very, very hard to do, but machine learning algorithms are quite effective in finding patterns in the data," Kaltenegger says.
“我认为这非常非常难做到，但机器学习算法在寻找资料模式方面非常有效，”卡尔特内格说。
The computer programs were best at spotting the telltale signs of leafy plants and less reliable when looking for evidence of lichen, tree bark, or biofilm.
电脑程序最擅长发现绿叶植物的迹象，但在寻找地衣、树皮或生物膜的证据时不太可靠。
There are caveats. These algorithms cannot provide absolute certainty. Rather, one could estimate that some percentage of a planet's surface is covered with life.
有一些警告。这些算法不能提供绝对的确定性。相反，人们可以估算出行星表面的一定比例被生命覆盖。
That's not the same as a discovery, Kaltenegger points out. Instead, it's a helpful clue.
卡尔特内格指出，这与发现不同。相反，这是一个有用的线索。
"It's not going to be like, AI said we found an Earthlike planet," she explains. "AI is going to bring it to the level where some real people are going to have to look at it."
“人工智能不会说我们发现了一颗类地行星，”她解释道。“人工智能将把它提升到一些真实的人必须审视它的水平。”
Human scientists will still need to point more telescopes toward the planet and look for chemical signatures that could indicate life is there.
人类科学家仍然需要将更多的望远镜指向这颗行星，寻找可能显示生命存在的化学特征。
Ultimately, real people will be the ones deciding what such a discovery means.
最终，真实的人将决定这项发现的意义。
