Meta's FAIR lab has introduced the Segment Anything Model (SAM), an advanced image segmentation model designed to revolutionize computer vision by enabling promptable segmentation tasks, akin to the impact of foundation models in natural language processing. SAM can segment objects through simple prompts like clicks or bounding boxes, and it can handle uncertainties by generating multiple valid masks. The model's real-time performance is powered by its innovative architecture, which includes an image encoder, a prompt encoder, and a mask decoder, all trained on the extensive SA-1B dataset comprising over a billion masks. SAM's open-source availability encourages further research and application in various fields, including medical imagery and geospatial data, by significantly enhancing AI-assisted labeling processes. Its integration into platforms like Encord aims to streamline data annotation, showcasing SAM's potential to transform industries by reducing manual effort and improving segmentation accuracy.