The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.
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.
MIT engineers design proteins by their motion, not just their shape
An AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.
A better method for identifying overconfident large language models
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact
Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.
A better method for planning complex visual tasks
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
New method could increase LLM training efficiency
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Mixing generative AI with physics to create personal items that work in the real world
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
MIT faculty, alumni named 2026 Sloan Research Fellows
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
Study: Platforms that rank the latest LLMs can be unreliable
Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
Antonio Torralba, three MIT alumni named 2025 ACM fellows
Torralba’s research focuses on computer vision, machine learning, and human visual perception.
Guided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
A new way to increase the capabilities of large language models
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
Enabling small language models to solve complex reasoning tasks
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
A smarter way for large language models to think about hard problems
This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.