MIT researchers have devised a method for assessing how robust machine-learning models known as neural networks are for various tasks, by detecting when the models make mistakes they shouldn’t.
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.
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future. They trained their model on mammograms and known outcomes from over 60,000 patients treated at MGH, and their model learned the subtle patterns in breast tissue that are precursors to malignancy.
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.
A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies.
In February 2019, the Institute established five working groups to generate ideas for different components of the structure and operation of the new MIT Stephen A. Schwarzman College of Computing. The Organizational Structure working group is charged with recommending ways to organize the college’s departments and programs, establish its governance, and link it academically with MIT’s five schools, among other considerations.
A new learning system developed by MIT researchers improves robots’ abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch — and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi.
A team led by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a robotic system that can detect if an object is paper, metal, or plastic.
The winning team of researchers is a Dream Team of experts from Massachusetts Institute of Technology and the Boston University. It includes experts in neuroscience, robotics, computer science, computer vision, artificial intelligence, mathematical systems theory, and a host of other related advanced technology domains.
MIT CSAIL researchers have devised a new way to find such patterns using machine learning.
Their system uses a neural network to automatically predict if a specific element will appear frequently in a data stream. If it does, it’s placed in a separate bucket of so-called “heavy hitters” to focus on; if it doesn’t, it’s handled via hashing.
