Unpacking the bias of large language models
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Composed of “computing bilinguals,” the Undergraduate Advisory Group provides vital input to help advance the mission of the MIT Schwarzman College of Computing.
Researchers designed a tiny receiver chip that is more resilient to interference, which could enable smaller 5G “internet of things” devices with longer battery lives.
A faculty member since 1994, Chandrakasan has also served as dean of engineering and MIT’s inaugural chief innovation and strategy officer, among other roles.
The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.
By performing deep learning at the speed of light, this chip could give edge devices new capabilities for real-time data analysis.
A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.
A new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
Campus gathers with Vice President for Energy and Climate Evelyn Wang to explore the Climate Project at MIT, make connections, and exchange ideas.
The system automatically learns to adapt to unknown disturbances such as gusting winds.
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
The approach could help animators to create realistic 3D characters or engineers to design elastic products.
Researchers developed an algorithm that lets a robot “think ahead” and consider thousands of potential motion plans simultaneously.
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
In an annual tradition, MIT affiliates embarked on a trip to Washington to explore federal lawmaking and advocate for science policy.