New method improves the reliability of statistical estimations
The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
Topic
Laboratory for Information and Decision Systems (LIDS)
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Prognostic tool could help clinicians identify high-risk cancer patients
Using a versatile problem-solving framework, researchers show how early relapse in lymphoma patients influences their chance for survival.
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.
MIT engineers design an aerial microrobot that can fly as fast as a bumblebee
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
New control system teaches soft robots the art of staying safe
MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.
Researchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
Bigger datasets aren’t always better
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
Charting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
Teaching robots to map large environments
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
A faster problem-solving tool that guarantees feasibility
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
MIT Schwarzman College of Computing welcomes 11 new faculty for 2025
The faculty members occupy core computing and shared positions, bringing varied backgrounds and expertise to the MIT community.
Fighting for the health of the planet with AI
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
New prediction model could improve the reliability of fusion power plants
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
Accounting for uncertainty to help engineers design complex systems
The approach could enable autonomous vehicles, commercial aircraft, or transportation networks that are more reliable in the face of real-world unpredictability.
3 Questions: The pros and cons of synthetic data in AI
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.