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.
Laboratory for Information and Decision Systems (LIDS)
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.
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.
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.
A new way to test how well AI systems classify text
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
Eco-driving measures could significantly reduce vehicle emissions
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
New algorithms enable efficient machine learning with symmetric data
This new approach could lead to enhanced AI models for drug and materials discovery.
A new way to edit or generate images
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.