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Even with these space-based observatories, verifying that a planet orbits another star is time-consuming and difficult.
即使有了这些天基观测站，验证一颗行星是否绕着另一颗恒星运行也是耗时且困难的。
These telescopes can't visualize the planet itself -- just as how any common telescope can see Jupiter or Saturn.
这些望远镜无法看到行星本身，就像普通望远镜可以看到木星或土星一样。
Instead, they confirm its existence indirectly.
相反，他们间接证实了它的存在。
That involves measuring almost imperceptible blips in a star's brightness that could indicate a passing planet.
这涉及到测量恒星亮度中几乎难以察觉的光点，这些光点可能表明有一颗行星经过。
Astronomers scrutinize changes in starlight, known as light curves, to identify potential planets.
天文学家仔细观察星光的变化（称为光变曲线）来识别潜在的行星。
Then, to prove one exists, Earth-based telescopes measure how a star wobbles under its planet's gravitational tug.
然后，为了证明恒星的存在，地球上的望远镜测量了一颗恒星在其行星引力作用下如何摆动。
Once a planet is found, understanding what it is like is even trickier.
一旦发现一颗行星，要了解它是什么样子就更加困难了。
But astronomers can make assumptions based on size and distance from stars.
但天文学家可以根据恒星的大小和距离做出假设。
Thanks to these painstaking efforts, astronomers now know of at least 5,600 planets orbiting distant stars in the Milky Way.
由于这些艰苦的努力，天文学家现在知道银河系中至少有5600颗行星绕着遥远的恒星运行。
Some are huge gas giants bigger than Jupiter and Saturn; some are hot rocks smaller than Mars; most are worlds made of gas, rock, or both, often between the sizes of Earth and Neptune.
有些是比木星和土星还大的气态巨行星；有些是比火星还小的热岩石；大多数是由气体、岩石或两者组成的世界，大小通常在地球和海王星之间。
None resemble home. None have the conditions or the chemicals necessary for life as we know it.
没有一个地方像家园。没有一个具备我们所知的生命所需的条件或化学物质。
But AI could reveal something different, because it can take an even deeper look.
但人工智能可以揭示一些不同的东西，因为它可以进行更深入的观察。
In 2018 Valizadegan and his team began building a machine learning program to speed up exoplanet-hunting efforts.
2018年，瓦利扎德甘和他的团队开始建立机器学习程序，以加快系外行星搜寻工作。
They trained the software on data signifying confirmed planets as well as false positives, like binary stars that eclipse each other and can be mistaken for passing planets.
他们对软件进行了数据训练，这些数据表示已确认的行星以及误报，例如相互遮挡并可能被误认为经过的行星的双星。
They called it ExoMiner and put it to the test on the Kepler telescope's archive of observations.
他们将其命名为ExoMiner，并在开普勒望远镜的观测档案中对其进行了测试。
"I did not know how much we were going to be rewarded," Valizadegan says.
“我不知道我们会得到多少奖励，”瓦利扎德甘说。
But his model quickly identified 370 previously unknown exoplanets.
但他的模型很快就辨识出了370颗以前未知的系外行星。
"At first there was a lot of resistance from the exoplanet scientists, who said, 'No, this should not be labeled as a planet.' But over time, they got more confident."
“一开始，系外行星科学家们提出了很大的阻力，他们说，‘不，这不应该被贴上行星的标签。’但随着时间的推移，他们变得更加自信。”
ExoMiner has yet to be overruled, he says.
瓦利扎德甘说，ExoMiner尚未被否决。
None of these 370 new planets are like Earth -- or any other planet in our solar system, for that matter.
这370颗新行星中没有一颗像地球，也不像我们太阳系中的任何其他行星。
One new world, dubbed Kepler-495 c, is about twice the size of Earth and zips around its sunlike star at a blistering pace, every six days.
一个被称为Kepler-495 c的新世界大约是地球大小的两倍，每六天就以惊人的速度绕着它的类日恒星旋转一圈。
Another, called Kepler-27 d, is almost as big as Neptune, or about eight times as big as Earth, and experiences a speedy six-and-a-half-day year.
另一个名为开普勒-27 d，几乎与海王星一样大，大约是地球的八倍，并且经历了快速的六天半一年。
Fried by their stars' heat and radiation, these planets are likely uninhabitable.
这些行星被恒星的热量和辐射煎炸，很可能无法居住。
Valizadegan says ExoMiner is just the start of using AI to solve this ultimate needle-in-a-haystack problem.
瓦利扎德甘表示，ExoMiner只是使用人工智能解决这一终极大海捞针问题的开始。
A new generation of planet-hunting telescopes launching in the next decade will bring ever vaster quantities of starlight down to Earth.
未来十年发射的新一代行星搜寻望远镜将为地球带来更多的星光。
Future AI planet spotters, building on ExoMiner's success, are also in development.
基于ExoMiner的成功，未来的人工智能行星观测器也在开发中。
In fact, researchers now believe that AI can be useful in hunting for not merely new worlds but also conditions most likely to host life.
事实上，研究人员现在相信人工智能不仅可以用于寻找新世界，还可以用于寻找最有可能存在生命的条件。
