A new optimization framework for robot motion planning
MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles.
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.
The Nano Summit highlights nanoscale research across multiple disciplines at MIT.
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.
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.
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.