The role of artificial intelligence in space observation and data technology (www.techradar.com)

AI is reshaping space observation from slow, specialist-led workflows into a fast, data-driven, collaborative science. Telescopes and missions like the Vera C. Rubin Observatory and Euclid — alongside radio arrays that can collect hundreds of terabytes nightly — produce data volumes and event rates (tens of thousands of alerts per night) that traditional pipelines cannot handle. Machine learning now performs near‑real‑time signal triage and anomaly detection, sharpens image resolution, cleans noisy measurements, and mines archival datasets: a recent student-led ML project reportedly flagged ~1.5 million new potential targets. That shift turns humans into strategists and experiment designers while AI handles the heavy data lifting, enabling rapid follow-ups on transients, candidate technosignatures, and other rare events. For the AI/ML community this trend creates clear technical priorities and opportunities: scalable, low-latency inference at petabyte scale; robust anomaly detection and uncertainty quantification for prioritizing follow-up; federated and decentralized data access to overcome paywalled clouds; and physics-aware simulation and generative models to explore hypothesis spaces (e.g., origins-of-life scenarios). It also opens democratized science—students and under-resourced teams can participate using open-source stacks—while highlighting the need for equitable data infrastructure, reproducible models, and coordinated, autonomous observation planners that can direct global telescope networks in real time.