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Astronomical surveys have expanded to such vast scales that human analysis alone is no longer sufficient. Instead, we increasingly rely on machine learning algorithms to sift through data and identify sources of objects of interest. When harnessed effectively, these technologies are extraordinarily powerful. This talk will explore how AI systems have been integrated into the Deeper, Wider, Faster program, dramatically reducing the workload of astronomers through both supervised and unsupervised learning methods. A particularly intriguing area of exploration is the challenge of detecting faint space debris and distinguishing it from genuine astronomical phenomena.