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As a machine learning scientist at NASA, Hamed Valizadegan once trained an algorithm to examine images of blood vessels in astronauts' retinas, improving efforts to understand vision changes in microgravity.
身为NASA的机器学习科学家，哈米德·瓦利扎德甘曾经训练过一种算法来检查宇航员视网膜中的血管影像，从而提高了理解微重力下视力变化的能力。
It was important work, but Valizadegan, who never lost his childhood love of the night sky, couldn't shake his desire to study the stars.
这是一项重要的工作，但瓦利扎德甘从未失去儿时对夜空的热爱，无法动摇他研究星星的愿望。
"I could watch the sky for hours, contemplating the meaning of life and whether we are alone in this vast universe," he says.
“我可以看着天空几个小时，思考生命的意义，以及我们在这个浩瀚的宇宙中是否孤独，”他说。
Early on, however, his space scientist colleagues seemed reluctant to embrace artificial intelligence as a tool for exploring the cosmos.
然而，早些时候，他的太空科学家同事似乎不愿意接受人工智能作为探索宇宙的工具。
That may be because advanced algorithms don't typically show their work.
这可能是因为高级算法通常不会展示它们的工作。
Sophisticated AI systems are inspired by the brain, so individual synthetic "neurons" make computations and then pass information to other nodes in the network.
复杂的人工智能系统受到大脑的启发，因此单一合成“神经元”进行计算，然后将信息传递到网络中的其他节点。
The resulting systems are so dense with calculations it can be impossible to know how they arrive at answers.
由此产生的系统计算量如此密集，以至于不可能知道它们是如何得出答案的。
That black box quality, Valizadegan says, was a turnoff to scientists who embraced historical standards for ultraprecise modeling and simulations.
瓦利扎德甘说，这种黑盒子的品质让那些拥护超精确建模和模拟历史标准的科学家感到厌烦。
But modern astronomy was reaching a bottleneck.
但现代天文学正达到瓶颈。
Telescopes in space and on Earth collect so much information that humans can't decipher it quickly, or even at all.
太空和地球上的望远镜收集的信息如此之多，人类无法快速破解，甚至根本无法破解。
And future observatories were being planned that would only flood the field with more observations.
未来的天文台正在规划中，这只会让该领域充满更多的观测结果。
Take the Vera C. Rubin Observatory in Chile, which scientists first proposed building in 2001.
以智利的Vera C. Rubin天文台为例，科学家于2001年首次提议建造该天文台。
Starting in 2025, it will image the whole sky every three nights with the world's largest camera, with a resolution of 3,200 megapixels.
从2025年开始，它将每三个晚上用世界上最大的相机拍摄整个天空，分辨率为32亿像素。
It's expected to capture data on one million supernovae every year, as well as tens of thousands of asteroids and other celestial objects.
预计每年将捕获一百万颗超新星以及数万颗小行星和其他天体的数据。
How could any number of human scientists possibly study them all on their own?
这么多的人类科学家怎么可能独自研究它们呢？
In 2014 Valizadegan teamed up with astronomer Jon Jenkins, who invited him to join a more automated search for another Earthlike planet in our galaxy.
2014年，瓦利扎德甘与天文学家乔恩·詹金斯合作，后者邀请他加入一项更自动化的寻找银河系中另一颗类地行星的行动。
It was just the type of dreamy project Valizadegan was hoping for.
这正是瓦利扎德甘所希望的梦幻项目。
While life might exist in strange forms on planets unlike our own, scientists have set their sights on finding the familiar: a rocky world orbiting a star, with a stable atmosphere and liquid water.
虽然生命可能以奇怪的形式存在于与地球不同的行星上，但科学家们已经把目光投向了寻找熟悉的东西：一个绕着恒星运行的岩石世界，拥有稳定的大气层和液态水。
But discovering such a planet is literally an astronomical problem.
但发现这样一颗行星其实是天文学问题。
Some estimates put the number of planets in the Milky Way in the hundreds of billions -- with only some small but unknown proportion of them being Earthlike.
据估计，银河系中有数千亿颗行星，其中只有一小部分是类地行星，但比例未知。
On this quest, humanity is off to a relatively slow start.
在这项探索中，人类的起步相对缓慢。
Astronomers found the first planet orbiting a star other than our own -- an exoplanet -- in 1995.
天文学家于1995年发现了第一颗绕着我们自己的恒星以外的恒星运行的行星（系外行星）。
Efforts accelerated during the 2010s with the Kepler Space Telescope, which peered at 150,000 stars in one small patch of sky for nine years, rotating occasionally to scan a new section of space.
21世纪的前10年，开普勒太空望远镜加速了这方面的努力，该望远镜在9年的时间里凝视着一小片天空中的15万颗恒星，偶尔旋转以扫描新的空间部分。
Its successor, the Transiting Exoplanet Survey Satellite, was launched into space in 2018 to observe much more of the sky, focusing on about 200,000 stars closer to Earth.
它的继任者凌日系外行星巡天卫星于2018年发射到太空，以观测更多的天空，重点是距离地球较近的约20万颗恒星。
