MIT researchers “speak objects into existence” using AI and robotics
The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.
A smarter way for large language models to think about hard problems
This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.
New control system teaches soft robots the art of staying safe
MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.
Researchers discover a shortcoming that makes LLMs less reliable
Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.
MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases
BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.
The cost of thinking
MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.
Bigger datasets aren’t always better
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
Understanding the nuances of human-like intelligence
Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.
Leading quantum at an inflection point
The MIT Quantum Initiative is taking shape, leveraging quantum breakthroughs to drive the future of scientific and technological progress.
Charting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
MIT researchers propose a new model for legible, modular software
The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.
Teaching robots to map large environments
A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.
3 Questions: How AI is helping us monitor and support vulnerable ecosystems
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
A faster problem-solving tool that guarantees feasibility
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
The brain power behind sustainable AI
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.