MIT Generative AI Week fosters dialogue across disciplines
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
The series aims to help policymakers create better oversight of AI in society.
MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
During 18 years of leadership, Evans established new R&D mission areas, strengthened ties to the MIT community, and increased inclusion and education efforts.
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.
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.
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.