This project develops methods and tools that use artificial intelligence and machine learning (AI/ML) to help overcome the problem of radio-frequency interference (RFI) in measurements made by radio telescopes. The techniques could benefit other sensitive receivers threatened by RFI, for example weather radars. RFI is a growing challenge for instruments like telescopes and radars due to increasing usage of the radio spectrum by mobile wireless communications and other applications. The undesired signals from nearby transmitters overpower the faint signals from far away that the instruments intend to measure. To continue performing their missions, these instruments require improved techniques to filter out RFI, and where that is not possible, to identify and discard data that are irretrievably corrupted by RFI. Recent progress in AI/ML offers new approaches that could be applied to RFI mitigation, with the promise of being fast enough to keep up with the high volumes of data that stream from state-of-the-art instruments. This project will deliver documented data sets, open-source AI/ML models, and software tools. The models and tools are intended for use by non-experts in AI/ML to address RFI challenges in their instruments or scientific studies. The project starts by assembling the largest-ever dataset of RFI observations by radio telescopes, through doing an RFI survey at selected US astronomy facilities. The results inform the development of digital twins: data simulator