We live in a world of wireless signals flowing around us and bouncing off our bodies. MIT researchers are now leveraging those signal reflections to provide scientists and caregivers with valuable insights into people’s behavior and health.
MIT CSAIL unsealed a special time capsule from 1999 after a self-taught programmer Belgium solved a puzzle devised by MIT professor and famed cryptographer Ron Rivest.
Researchers from MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have shown that neural networks contain subnetworks that are up to one-tenth the size yet capable of being trained to make equally accurate predictions — and sometimes can learn to do so even faster than the originals.
The American Academy of Arts and Sciences (AAAS) announced that MIT professor David Karger was among their new 2019 members. The new class of more than 200 members recognizes the outstanding achievements of individuals in academia, the arts, business, government, and public affairs.
In a paper being presented at the International Conference on Learning Representations in May, MIT researchers describe an NAS algorithm that can directly learn specialized convolutional neural networks (CNNs) for target hardware platforms — when run on a massive image dataset — in only 200 GPU hours, which could enable far broader use of these types of algorithms.
MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) held a special workshop with Microsoft Research to explore key challenges in creating trustworthy and robust artificial intelligence (AI) systems. The effort focused on addressing concerns about the trustworthiness of AI systems, including rising concerns with the safety, fairness, and transparency of the technologies.
MIT researchers have built a system that takes a step toward fully automated smart home by identifying occupants, even when they’re not carrying mobile devices. The system, called Duet, uses reflected wireless signals to localize individuals.
