When AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Solving AI’s ‘Last Mile’ Problem
Before joining MIT in 2025, Assistant Professor Omar Khattab had already created several transformative tools that made their mark on industry.
Awarded jointly by the Bavarian State Government and the Bavarian Academy of Sciences and Humanities, it is the most highly endowed award for technology and engineering in Germany.
“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.