The MIT–MBZUAI Collaborative Research Program will unite faculty and students from both institutions to advance AI and accelerate its use in pressing scientific and societal challenges.
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.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
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.
