Student-powered machine learning
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
A machine-learning method imagines what a sentence visually looks like, to situate and ground its semantics in the real world, improving translation, like humans can.
Thousands of children participate in MIT-developed artificial intelligence curriculum.
A new training approach yields artificial intelligence that adapts to diverse play-styles in a cooperative game, in what could be a win for human-AI teaming.
Brown and three other scientists recognized for advancing statistical, theoretical analyses of neuroscience data.
Study shows AI can identify self-reported race from medical images that contain no indications of race detectable by human experts.
Graduate student Sarah Cen explores the interplay between humans and artificial intelligence systems, to help build accountability and trust.
MIT and Mass General Brigham researchers and physicians connect in person to bring AI into mainstream health care.
Researchers devise an efficient protocol to keep a user’s private information secure when algorithms use it to recommend products, songs, or shows.
The MIT professor is honored for extraordinary accomplishments in mathematics, computer science, and quantum physics.
Have a question about numerical differential equations? Odds are this CSAIL research affiliate has already addressed it.
Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.
Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.
MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.
MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.