While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.
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.
Co-founded by an EECS alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.