Researchers from CSAIL have developed a machine learning system that can either make a prediction about a task, or defer the decision to an expert. Most importantly, it can adapt when and how often it defers to its human collaborator, based on factors such as its teammate’s availability and level of experience.

With the boundless treasure trove of paintings that exist, the connections between these works of art from different periods of time and space can often go overlooked. It’s impossible for even the most knowledgeable of art critics to take in millions of paintings across thousands of years and be able to find unexpected parallels in themes, motifs, and visual styles.

Deep learning systems are revolutionizing technology around us, from voice recognition that pairs you with your phone to autonomous vehicles that are increasingly able to see and recognize obstacles ahead. But much of this success involves trial and error when it comes to the deep learning networks themselves. A group of MIT researchers recently reviewed their contributions to a better theoretical understanding of deep learning networks, providing direction for the field moving forward.

Recently, a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) took a new approach to getting us closer to a solution: a combinatorial machine learning system that selects peptides (short strings of amino acids) that are predicted to provide high population coverage for a vaccine. 



The MIT Stephen A. Schwarzman College of Computing announced its first two named professorships, beginning July 1, to Frédo Durand and Samuel Madden in the Department of Electrical Engineering and Computer Science (EECS). These named positions recognize the outstanding achievements and future potential of their academic careers.

Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19.

For humans, it can be challenging to manipulate thin flexible objects like ropes, wires, or cables. But if these problems are hard for humans, they are nearly impossible for robots. As a cable slides between the fingers, its shape is constantly changing, and the robot’s fingers must be constantly sensing and adjusting the cable’s position and motion.