Automated system teaches users when to collaborate with an AI assistant
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
Topic
Computer Science and Artificial Intelligence Laboratory (CSAIL)
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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.
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.
Three MIT affiliates receive Schmidt awards
Jörn Dunkel and Surya Ganguli ’98, MNG ’98 receive Science Polymath awards; Josh Tenenbaum is named AI2050 Senior Fellow.
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.
Aging Brain Initiative symposium showcases “cutting-edge” research across MIT
Seed projects, posters represent a wide range of labs working on technologies, therapeutic strategies, and fundamental research to advance understanding of age-related neurodegenerative disease.
This 3D printer can watch itself fabricate objects
Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.
Explained: Generative AI
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence?
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.