Technique enables AI on edge devices to keep learning over time
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
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.
Ten years after the founding of the undergraduate research program, its alumni reflect on the unexpected gifts of their experiences.
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.
Inaugural Fast Forward Faculty Fund grants aim to spur new work on climate change and deepen collaboration at MIT.
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.
In a Q&A, the MIT junior describes how all the pieces fell into place as he captured the “Tetris” world title.
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.
James Fujimoto, Eric Swanson, and David Huang are recognized for their technique to rapidly detect diseases of the eye; Subra Suresh is honored for his commitment to research and collaboration across borders.
In a new book, Richard “Dick” Larson draws on a lifelong commitment to STEM education at MIT to offer accessible advice on solving everyday problems and making smarter decisions.
StructCode, developed by MIT CSAIL researchers, encodes machine-readable data in laser-cut objects by modifying their fabrication features.
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.