Adam Zewe
Adam Zewe, MIT News Office
Articles
Robotic hand identifies objects after one grasp
MIT researchers have developed a three-fingered robotic gripper that can “feel” with great sensitivity along the full length of each finger.
Data transmission chip for decoding demonstrates strong energy efficiency
The chip developed by MIT researchers, which can decipher any encoded signal, could enable lower-cost devices that perform better while requiring less hardware.
Wireless technique helps quantum computing systems keep their cool
MIT researchers have developed a wireless technique that enables a super-cold quantum computer to send and receive data without generating too much error-causing heat.
Technique developed to improve machine-learning models’ reliability
MIT researchers have developed a machine-learning model to determine its confidence in a prediction while using no additional data.
Boosting quantum computing signals while reducing noise
Squeezing noise over a broad frequency bandwidth in a quantum computing system could lead to faster and more accurate quantum computing measurements.
Neural network models learning from examples without training
An MIT study shows how large language models can learn a new task from just a few examples, without the need for any new training data.
Quantum computing architecture designed to connect large-scale devices
MIT researchers have demonstrated directional photon emission, the first step toward extensible quantum interconnects, which could help connect large-scale devices.
Paper-thin solar cell can turn surfaces into power source
MIT researchers develop a scalable fabrication technique to produce ultrathin solar cells that can be seamlessly added to any surface. See video.
Technique developed to help 2D materials expand
A technique that accurately measures how atom-thin materials expand when heated could help engineers develop faster, more powerful electronic devices.
Machine learning facilitates turbulence tracking in fusion reactors
MIT researchers have developed a machine learning approach that can affect the energy generated during fusion reactions, with implications for reactor design.