A faster way to preserve privacy online
New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.
Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.
Expanding the MIT-IBM Watson AI Lab’s network of neurons
On October 6, nearly 50 undergraduate and graduate students and postdocs, primarily from MIT, attended the MIT-IBM Watson AI Lab’s networking event. The goal was to connect young…
Learning on the edge
A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
New hardware offers faster computation for artificial intelligence, with much less energy
Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
Student-powered machine learning
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
On the road to cleaner, greener, and faster driving
Researchers use artificial intelligence to help autonomous vehicles avoid idling at red lights.
Unlocking new doors to artificial intelligence
MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.