Bigger datasets aren’t always better
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
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.
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.
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
The faculty members occupy core computing and shared positions, bringing varied backgrounds and expertise to the MIT community.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
The approach could enable autonomous vehicles, commercial aircraft, or transportation networks that are more reliable in the face of real-world unpredictability.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
AeroAstro professor and outgoing co-director of the Center for Computational Science and Engineering will play a vital role in fostering community for bilingual computing faculty.
This new approach could lead to enhanced AI models for drug and materials discovery.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.