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.
Algorithms
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.
Read More3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Read MoreA 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.
Read MoreCreating AI that matters
How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.
Read MoreMethod teaches generative AI models to locate personalized objects
After being trained with this technique, vision-language models can better identify a unique item in a new scene.
Read MoreOptimizing food subsidies: Applying digital platforms to maximize nutrition
An algorithm can change the face of food assistance policy in the Global South, says MIT assistant professor and J-WAFS researcher Ali Aouad.
Read MoreHelping scientists run complex data analyses without writing code
Co-founded by an MIT alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.
Read MoreFighting for the health of the planet with AI
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
Read MoreAI maps how a new antibiotic targets gut bacteria
MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
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