Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Read MorePostdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.
Read MoreWith insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.
Read MoreDeveloped to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
Read MoreBy performing deep learning at the speed of light, this chip could give edge devices new capabilities for real-time data analysis.
Read MoreAgreement between MIT Microsystems Technology Laboratories and GlobalFoundries aims to deliver power efficiencies for data centers and ultra-low power consumption for intelligent devices at the edge.
Read MoreA deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.
Read MoreThis new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
Read MoreThe technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
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