Explore a novel approach for predicting debug information in stripped binaries through this 27-minute conference talk. Learn about using machine learning to train probabilistic models on non-stripped binaries and applying them to predict properties of meaningful elements in unseen stripped binaries. Delve into topics such as recovering variables, predicting names and types, and evaluating prediction accuracy. Gain insights into the challenges of working with stripped binaries and discover how this technique can be applied to malware inspection.