Artificial intelligence is now a part of the quest to find extraterrestrial life.

Researchers have developed an AI system that outperforms traditional methods in the search for alien signals. And early results were intriguing enough to send scientists back to their radio telescopes for a second look.

The study, published last week in Nature Astronomy, highlights the crucial role that AI techniques will play in the ongoing search for extraterrestrial intelligence.

The team behind the paper trained an AI to recognize signals that natural astrophysical processes couldn’t produce. They then fed it a massive dataset of over 150 terabytes of data collected by the Green Bank Telescope, one of the world’s largest radio telescopes, located in West Virginia.

The AI flagged more than 20,000 signals of interest, with eight showing the tell-tale characteristics of what scientists call “technosignatures,” such as a radio signal that could tip scientists off to the existence of another civilization.

In the face of a growing deluge of data from radio telescopes, it’s critical to have a fast and effective means of sorting through it all.

That’s where the AI system shines.

The system was created by Peter Ma, an undergraduate student at the University of Toronto and the lead author of the paper co-authored by a constellation of experts affiliated with the University of Toronto, UC Berkeley and Breakthrough Listen, an international effort launched in 2015 to search for signs of alien civilizations.

Ma, who taught himself how to code, first became interested in computer science in high school. He started working on a project where he aimed to use open-source data and tackle big data problems with unanswered questions, particularly in the area of machine learning.