Technique quickly identifies extreme event statistics
Machine-learning model provides risk assessment for complex nonlinear systems, including boats and offshore platforms.
MIT reshapes itself to shape the future
Gift of $350 million establishes the MIT Stephen A. Schwarzman College of Computing, an unprecedented, $1 billion commitment to world-changing breakthroughs and their ethical application.
Model helps robots navigate more like humans do
In simulations, robots move through new environments by exploring, observing, and drawing from learned experiences.
Regina Barzilay, James Collins, and Phil Sharp join leadership of new effort on machine learning in health
MacArthur “geniuses” in machine learning and synthetic biology to serve as faculty co-leads; Nobel laureate to chair advisory board of new research center.
3Q: David Simchi-Levi on advancing global retail operations through analytics and machine learning
David Simchi-Levi discusses insights from recent research in collaboration with some of the world's largest retailers.
Recognizing the partially seen
Advances in computer vision inspired by human physiological and anatomical constraints are improving pattern completion in machines.
Reducing false positives in credit card fraud detection
Model extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.
Machine-learning system tackles speech and object recognition, all at once
Model learns to pick out objects within an image, using spoken descriptions.
Abdul Latif Jameel Clinic for Machine Learning in Health at MIT aims to revolutionize disease prevention, detection, and treatment
A key part of the MIT Quest for Intelligence, J-Clinic builds on MIT expertise across multiple scientific disciplines.
Connor Coley named a Chemical and Engineering News Talented Twelve
Chemical engineering graduate student was named a “machine-learning maestro” by the magazine.
Artificial intelligence system uses transparent, human-like reasoning to solve problems
Model from MIT Lincoln Laboratory Intelligence and Decision Technologies Group sets a new standard for understanding how a neural network makes decisions.
Taking machine thinking out of the black box
Adaptable Interpretable Machine Learning project is redesigning machine learning models so humans can understand what computers are thinking.