Researchers have developed a tool called Zero-Shot Coreset Selection (ZCore) to automatically select valuable subsets of data from massive amounts of data generated by robots and visual AI systems, without the need for labels or domain expertise. ZCore uses existing foundation models to generate a zero-shot embedding space for unlabeled data and quantifies the relative importance of each example based on overall coverage and redundancy within the embedding distribution. The technique has been shown to be effective in reducing the amount of data needed for training while maintaining model performance, with a 95% reduction in robot data that covered all the settings of the initial, full dataset. ZCore is an open-source tool available on GitHub and will soon be added to the Enterprise version of FiftyOne.