Unpacking the “black box” to build better AI models
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
Simulating discrimination in virtual reality
The role-playing game “On the Plane” simulates xenophobia to foster greater understanding and reflection via virtual experiences.
Strengthening electron-triggered light emission
A new method can produce a hundredfold increase in light emissions from a type of electron-photon coupling, which is key to electron microscopes and other technologies.
Cognitive scientists develop new model explaining difficulty in language comprehension
Built on recent advances in machine learning, the model predicts how well individuals will produce and comprehend sentences.
This is your brain. This is your brain on code
MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.
Subtle biases in AI can influence emergency decisions
But the harm from a discriminatory AI system can be minimized if the advice it delivers is properly framed, an MIT team has shown.
Machine learning and the arts: A creative continuum
CAST Visiting Artist Andreas Refsgaard engages the MIT community in the ethics and play of creative coding.
Meet the 2022-23 Accenture Fellows
This year's fellows will work across research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization, and chemical transformations.
Large language models help decipher clinical notes
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
MIT researchers use quantum computing to observe entanglement
Researchers at the Center for Theoretical Physics lead work on testing quantum gravity on a quantum processor.
Ushering in a new era of computing
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
Breaking the scaling limits of analog computing
New technique could diminish errors that hamper the performance of super-fast analog optical neural networks.
The task of magnetic classification suddenly looks easier
MIT undergraduate researchers Helena Merker, Harry Heiberger, and Linh Nguyen, and PhD student Tongtong Liu, exploit machine-learning techniques to determine the magnetic structure of materials.
Teresa Gao named 2024 Mitchell Scholar
The MIT senior will pursue postgraduate studies in computer science in Ireland.