Q&A: Vivienne Sze on crossing the hardware-software divide for efficient artificial intelligence
Her research focuses on more-efficient deep neural networks to process video, and more-efficient hardware to run applications.
Her research focuses on more-efficient deep neural networks to process video, and more-efficient hardware to run applications.
Student inventors recognized on World IP Day for groundbreaking, patentable solutions to issues related to maternal health, energy efficiency, and plastic waste.
Four MIT undergraduates whose research areas explore artificial intelligence, space, and climate change honored for their academic achievements.
The advance could accelerate engineers’ design process by eliminating the need to solve complex equations.
MIT alumni and friends from around the globe attended an online event that featured presentations from Institute leaders, faculty, and alumni about human health-related research.
Future of Data, Trust, and Privacy initiative aims to address AI-driven analytics and changing attitudes about personal data.
Researchers propose a method for finding and fixing weaknesses in automated programming tools.
Cardea software system aims to bring the power of prediction to hospitals by streamlining complex machine learning processes.
Using deep convolutional neural networks, researchers devise a system that quickly analyzes wide-field images of patients’ skin in order to more efficiently detect cancer.
System uses penetrative radio frequency to pinpoint items, even when they’re hidden from view.
MIT research combines machine learning with nanoparticle design for personalized drug delivery.
A new approach to identifying useful formulations could help solve the degradation issue for these promising new lightweight photovoltaics.
Regina Barzilay, Fotini Christia, and Collin Stultz describe how artificial intelligence and machine learning can support fairness, personalization, and inclusiveness in health care.
A new tool helps humans better understand and develop artificial intelligence models by searching and highlighting representative scenarios.
Deep-learning technique optimizes the arrangement of sensors on a robot’s body to ensure efficient operation.