This U of T student developed an algorithm to search for extraterrestrial life while he was in high school. Now, he's using AI to look for aliens as part of an international team of researchers
When Peter Ma’s high school classmates were supposed to pair up for a computer science project, no one wanted to be on his alien detection squad. The Grade 12 student was planning to look for signs of intelligent life using an algorithm he’d written based on open-source data from the University of California, Berkeley. So Ma—a restless autodidact who had taught himself to code the year before—worked on the project by himself.
When my parents bought me a small telescope, like a lot of people in my field. I was five. I didn’t know what I was looking at—everything, I guess—but it looked cool. In high school, I found Berkeley’s SETI open-source data. They had a call-out for machine learning collaborations on their GitHub repositories, and that’s how I first realized the potential of machine learning in SETI. As I worked on my project, I cold-emailed half the people in the Berkeley SETI lab asking questions about the data and their approach. They were quite appreciative of a relatively young kid being interested and curious, and they eventually offered me a summer job.
You start by pointing your telescope at a star. If you see a narrowband signal, that’s good. If you point your telescope away from the star and still see that signal, that’s bad. That means the signal is coming from your environment. So you can look for an on-off pattern. You move your telescope back and forth across a star. If the signal isn’t background noise, it should be there and then disappear.
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