Students pitch transformative ideas in generative AI at MIT Ignite competition
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
Héctor Beltrán’s new book examines hackers in Mexico, whose work leads them to reflect on the roles they play in society.
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
The team’s new algorithm finds failures and fixes in all sorts of autonomous systems, from drone teams to power grids.
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.
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.
At MIT, a driving force in the chip-making industry discusses the rise of TSMC and Taiwan as a manufacturing center.
AI models that prioritize similarity falter when asked to design something completely new.
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.