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.
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.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
David Alexander Bruns-Smith
Assistant Professor, shared appointment with MIT Sloan School of Management, [AI+D]
Lindsey Raymond
Assistant Professor, [CS] Shared appointment: Economics and EECS
Office: 45-501C and 52-454
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.