Games 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.
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Human-computer interaction
Human-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.
Read MoreNew MIT class uses anthropology to improve chatbots
MIT computer science students design AI chatbots to help young users become more social, and socially confident.
Read MoreImproving AI models’ ability to explain their predictions
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
Read MoreMixing generative AI with physics to create personal items that work in the real world
To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
Read MorePersonalization features can make LLMs more agreeable
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
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