Multiple AI models help robots execute complex plans more transparently
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
A multimodal system uses models trained on language, vision, and action data to help robots develop and execute plans for household, construction, and manufacturing tasks.
MIT researchers propose “PEDS” method for developing models of complex physical systems in mechanics, optics, thermal transport, fluid dynamics, physical chemistry, climate, and more.
MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.
This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
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.
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
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.
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.
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
A pivotal talk led postdoc Kristina Monakhova to develop smart, computational cameras and microscopes for intelligent systems.
The team’s new algorithm finds failures and fixes in all sorts of autonomous systems, from drone teams to power grids.