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 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.
By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.
At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.
MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.
MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
