America's Leading Alien Hunters Depend on AI to Speed Their Search

Harvard University's Galileo Project is using AI to automate the search for unidentified anomalous phenomena, marking a significant shift in how academics approach what was once considered fringe research. The project operates a Massachusetts observatory equipped with infrared cameras, acoustic sensors, and radio-frequency analyzers that continuously scan the sky for unusual objects. Researchers Laura Domine and Richard Cloete are training machine learning algorithms to recognize all normal aerial phenomena -- planes, birds, drones, weather balloons -- so the system can flag genuine anomalies for human analysis. The team uses computer vision software called YOLO (You Only Look Once) and has generated hundreds of thousands of synthetic images to train their models, though the software currently identifies only 36% of aircraft captured by infrared cameras. The Pentagon is pursuing parallel efforts through its All-domain Anomaly Resolution Office, which has examined over 1,800 UAP reports and identified 50 to 60 cases as "true anomalies" that government scientists cannot explain. AARO has developed its own sensor suite called Gremlin, using similar technology to Harvard's observatory. Both programs represent the growing legitimization of UAP research following 2017 Defense Department disclosures about military encounters with unexplained aerial phenomena.

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