Every night, astronomers must carefully assess changing weather, the intensity of moonlight and shifting atmospheric conditions before deciding where to point a telescope. “One of the main achievements is that we set up all the infrastructure needed to deploy this self-driving telescope on a national observatory. With their teams at SkAI, they developed a deep-learning scheduling system. “It is exciting to see ideas from AI and reinforcement learning brought to telescope scheduling, where every decision must balance changing conditions and scarce observing time,” Vijayaraghavan said. This past spring and summer, the intelligent scheduling system completed two successful observing runs on the Blanco Telescope — one of the world’s most productive astronomical facilities.