Creating AI that matters
How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.
How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.
To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.
After being trained with this technique, vision-language models can better identify a unique item in a new scene.
Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.
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.
New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.
MIT researchers discovered a hidden atomic order that persists in metals even after extreme processing.
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.
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.
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.
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.