MIT launches new data privacy-focused initiative
Future of Data, Trust, and Privacy initiative aims to address AI-driven analytics and changing attitudes about personal data.
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.
Wireless sensing technology could help improve patients’ technique with inhalers and insulin pens.
Five courses celebrate the nanoscale, highlight technologies in photogrammetry and 360-degree videography.
New technique speeds up calculations of drug molecules’ binding affinity to proteins.
Leveraging years of MIT cognitive science research, Nara Logics incorporates findings about the brain into its AI platform.
A new method called tensor holography could enable the creation of holograms for virtual reality, 3D printing, medical imaging, and more — and it can run on a smartphone.