Guided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
Networks
Learning how to predict rare kinds of failures
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
Designing a new way to optimize complex coordinated systems
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
New computational chemistry techniques accelerate the prediction of molecules and materials
With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.
Creating and verifying stable AI-controlled systems in a rigorous and flexible way
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
AI copilot enhances human precision for safer aviation
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
AI copilot enhances human precision for safer aviation
Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.
Researchers create a tool for accurately simulating complex systems
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
Drones navigate unseen environments with liquid neural networks
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.