Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
Saeed Miganeh’s work at MIT is helping him answer important questions about designing effective programs for poverty mitigation and economic growth in African countries.
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.
The approach can detect anomalies in data recorded over time, without the need for any training.
MIT Concrete Sustainability Hub research presents a streamlined pavement life-cycle assessment framework to enable a large set of stakeholders to conduct environmental analysis of pavements.
Reflecting a “Moore’s Law of aviation,” commercial flight has become roughly twice as safe each decade since the 1960s; Covid-19 added a wrinkle, however.
More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.
Shared commitment to data-driven approach will help identify new policy solutions to reduce poverty and increase economic mobility in the US.
Scientists created the step-by-step guide to unlock the potential of NASA’s James Webb Space Telescope for identifying habitable worlds in the universe.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
A new technique enables users to compare several large models and choose the one that works best for their task.
PhD student Xinyi Zhang is developing computational tools for analyzing cells in the age of multimodal data.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
The challenge asked teams to develop AI algorithms to track and predict satellites’ patterns of life in orbit using passively collected data
Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.