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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.
身為NASA的機器學習科學家，哈米德·瓦利扎德甘曾經訓練過一種算法來檢查宇航員視網膜中的血管影像，從而提高了理解微重力下視力變化的能力。
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.
這是一項重要的工作，但瓦利扎德甘從未失去兒時對夜空的熱愛，無法動搖他研究星星的愿望。
"I could watch the sky for hours, contemplating the meaning of life and whether we are alone in this vast universe," he says.
“我可以看著天空幾個小時，思考生命的意義，以及我們在這個浩瀚的宇宙中是否孤獨，”他說。
Early on, however, his space scientist colleagues seemed reluctant to embrace artificial intelligence as a tool for exploring the cosmos.
然而，早些時候，他的太空科學家同事似乎不愿意接受人工智能作為探索宇宙的工具。
That may be because advanced algorithms don't typically show their work.
這可能是因為高級算法通常不會展示它們的工作。
Sophisticated AI systems are inspired by the brain, so individual synthetic "neurons" make computations and then pass information to other nodes in the network.
復雜的人工智能系統受到大腦的啟發，因此單一合成“神經元”進行計算，然后將信息傳遞到網絡中的其他節點。
The resulting systems are so dense with calculations it can be impossible to know how they arrive at answers.
由此產生的系統計算量如此密集，以至于不可能知道它們是如何得出答案的。
That black box quality, Valizadegan says, was a turnoff to scientists who embraced historical standards for ultraprecise modeling and simulations.
瓦利扎德甘說，這種黑盒子的品質讓那些擁護超精確建模和模擬歷史標準的科學家感到厭煩。
But modern astronomy was reaching a bottleneck.
但現代天文學正達到瓶頸。
Telescopes in space and on Earth collect so much information that humans can't decipher it quickly, or even at all.
太空和地球上的望遠鏡收集的信息如此之多，人類無法快速破解，甚至根本無法破解。
And future observatories were being planned that would only flood the field with more observations.
未來的天文臺正在規劃中，這只會讓該領域充滿更多的觀測結果。
Take the Vera C. Rubin Observatory in Chile, which scientists first proposed building in 2001.
以智利的Vera C. Rubin天文臺為例，科學家于2001年首次提議建造該天文臺。
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.
從2025年開始，它將每三個晚上用世界上最大的相機拍攝整個天空，分辨率為32億像素。
It's expected to capture data on one million supernovae every year, as well as tens of thousands of asteroids and other celestial objects.
預計每年將捕獲一百萬顆超新星以及數萬顆小行星和其他天體的數據。
How could any number of human scientists possibly study them all on their own?
這么多的人類科學家怎么可能獨自研究它們呢？
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，瓦利扎德甘與天文學家喬恩·詹金斯合作，后者邀請他加入一項更自動化的尋找銀河系中另一顆類地行星的行動。
It was just the type of dreamy project Valizadegan was hoping for.
這正是瓦利扎德甘所希望的夢幻項目。
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.
雖然生命可能以奇怪的形式存在于與地球不同的行星上，但科學家們已經把目光投向了尋找熟悉的東西：一個繞著恒星運行的巖石世界，擁有穩定的大氣層和液態水。
But discovering such a planet is literally an astronomical problem.
但發現這樣一顆行星其實是天文學問題。
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.
據估計，銀河系中有數千億顆行星，其中只有一小部分是類地行星，但比例未知。
On this quest, humanity is off to a relatively slow start.
在這項探索中，人類的起步相對緩慢。
Astronomers found the first planet orbiting a star other than our own -- an exoplanet -- in 1995.
天文學家于1995年發現了第一顆繞著我們自己的恒星以外的恒星運行的行星（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世紀的前10年，開普勒太空望遠鏡加速了這方面的努力，該望遠鏡在9年的時間里凝視著一小片天空中的15萬顆恒星，偶爾旋轉以掃描新的空間部分。
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.
它的繼任者凌日系外行星巡天衛星于2018年發射到太空，以觀測更多的天空，重點是距離地球較近的約20萬顆恒星。
