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.
What does the future hold for generative AI?
Rodney Brooks, co-founder of iRobot, kicks off an MIT symposium on the promise and potential pitfalls of increasingly powerful AI tools like ChatGPT.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Search algorithm reveals nearly 200 new kinds of CRISPR systems
By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.
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.
Synthetic imagery sets new bar in AI training efficiency
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.
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.
Writing code, and decoding the world
Héctor Beltrán’s new book examines hackers in Mexico, whose work leads them to reflect on the roles they play in society.
Explained: Generative AI
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
MIT engineers are on a failure-finding mission
The team’s new algorithm finds failures and fixes in all sorts of autonomous systems, from drone teams to power grids.
Using AI to optimize for rapid neural imaging
MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.
Using language to give robots a better grasp of an open-ended world
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.
New techniques efficiently accelerate sparse tensors for massive AI models
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.
Accelerating AI tasks while preserving data security
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.
Making genetic prediction models more inclusive
MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.