“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
Using technology invented at MIT, Cartesian’s system for locating objects could also find uses in manufacturing, logistics, and robotics.
Student Spotlight: Nathaniel Morgan
A seasoned undergraduate researcher, Nathaniel Morgan has participated in UROP since his first year, and is now working with Omar Khattab on improving the capabilities of large language models.
Faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
A faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
Teaching AI models to say “I’m not sure”
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.