A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrates a novel method for training AI models to understand and navigate the physical world by analyzing video footage of everyday activities. The system, called ‘Time-Contrastive Learning,’ learns by observing the same scene from multiple camera angles, allowing it to identify objects and their spatial relationships without human-labeled data. This approach, which mimics how infants learn about their environment, could lead to more adaptable and efficient robots capable of performing complex tasks in unstructured settings like homes or warehouses. The research highlights a significant step toward creating AI with a more robust, common-sense understanding of the physical world. Read the full article at https://technologyreview.com/2024/05/15/1090000/ai-learns-physical-world-video.