MIT Generative AI Week fosters dialogue across disciplines
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles.
Rodney Brooks, co-founder of iRobot, kicks off an MIT symposium on the promise and potential pitfalls of increasingly powerful AI tools like ChatGPT.
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
Jörn Dunkel and Surya Ganguli ’98, MNG ’98 receive Science Polymath awards; Josh Tenenbaum is named AI2050 Senior Fellow.
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
Seed projects, posters represent a wide range of labs working on technologies, therapeutic strategies, and fundamental research to advance understanding of age-related neurodegenerative disease.
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
By blending 2D images with foundation models to build 3D feature fields, a new MIT method helps robots understand and manipulate nearby objects with open-ended language prompts.
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.