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.

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.

A new artificial intelligence tool developed by the MIT-IBM Watson AI Lab leverages the virtual eyes and ears of a computer to separate similar sounds that are tricky even for humans to differentiate. The tool improves on earlier iterations by matching the movements of individual musicians, via their skeletal keypoints, to the tempo of individual parts, allowing listeners to isolate a single flute or violin among multiple flutes or violins. 

Augmented reality (AR) and virtual reality (VR) companies have been working for years on developing systems that can incorporate real-life objects into their digitized worlds. With that in mind, a team from CSAIL has developed a smart glove that can detect your hand pose, as well as distinguish between more than 30 different household objects.

One of the hottest topics in robotics is the field of soft robots, which utilizes squishy and flexible materials rather than traditional rigid materials. But soft robots have been limited due to their lack of good sensing. A good robotic gripper needs to feel what it is touching (tactile sensing), and it needs to sense the positions of its fingers (proprioception). Such sensing has been missing from most soft robots.

In a new study at the International Conference on Learning Representations, MIT researchers propose a kind of creativity test to see how far GANs can go in riffing on a given image.

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models.

Engineers have discovered many ways of encoding curious exploration into machine learning algorithms. A research team at MIT wondered if a computer could do better, based on a long history of enlisting computers in the search for new algorithms.