Aging Brain Initiative awards fund five new ideas to study, fight neurodegeneration
Competitive seed grants launch yearlong investigations of novel hypotheses about potential causes, biomarkers, treatments of Alzheimer’s and ALS.
Competitive seed grants launch yearlong investigations of novel hypotheses about potential causes, biomarkers, treatments of Alzheimer’s and ALS.
Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.
A new artificial intelligence technique only proposes candidate molecules that can actually be produced in a lab.
MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.
Researchers have developed a technique that enables a robot to learn a new pick-and-place task with only a handful of human demonstrations.
MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.
A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.
A multidisciplinary team of graduate students helps infuse ethical computing content into MIT’s largest machine learning course.
Fellowship funds graduate studies for outstanding immigrants and children of immigrants.
New program strives to bridge the talent gap for underrepresented groups in the tech industry.
Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Machine learning can help.
The programs are designed to foster an understanding of how artificial intelligence technologies work, including their social implications.
When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition — just like the human brain.
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
MIT researchers design a robot that has a trick or two up its sleeve.