AI learns how vision and sound are connected, without human intervention
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
A detailed MIT analysis identifies some promising options but also raises unexpected concerns.
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.
A new book coauthored by MIT’s Dimitris Bertsimas explores how analytics is driving decisions and outcomes in health care.
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
New type of “state-space model” leverages principles of harmonic oscillators.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
Ultraviolet light “fingerprints” on cell cultures and machine learning can provide a definitive yes/no contamination assessment within 30 minutes.
A new approach could enable intuitive robotic helpers for household, workplace, and warehouse settings.
Chemists could use this quick computational method to design more efficient reactions that yield useful compounds, from fuels to pharmaceuticals.
Researchers have created a unifying framework that can help scientists combine existing ideas to improve AI models or create new ones.