System helps humans predict when self-driving cars will make mistakes
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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Human-computer interaction
With a feel for physics, AI models simulate a wider range of real-world scenarios
“GeoPT” helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately.
Read MoreThe benefits of medical AI assistance vary based on user expertise
Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
Read MoreDaniela Rus receives Bavarian Minister-President’s High-Tech Prize
Director of CSAIL and MIT professor honored for her contributions to robotics, artificial intelligence, and autonomous systems.
Read MoreGames people — and machines — play: Untangling strategic reasoning to advance AI
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
Read MoreHuman-machine teaming dives underwater
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Read MoreQ&A: MIT SHASS and the future of education in the age of AI
As the School of Humanities, Arts, and Social Sciences marks 75 years, Dean Agustín Rayo reflects on how AI is reshaping higher education and why SHASS disciplines continue to be central to MIT’s mission.
Read MoreEvaluating the ethics of autonomous systems
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Read MoreWristband enables wearers to control a robotic hand with their own movements
By moving their hands and fingers, users can direct a robot to play piano or shoot a basketball, or they can manipulate objects in a virtual environment.
Read MoreA better method for identifying overconfident large language models
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
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