The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
Laboratory for Information and Decision Systems (LIDS)
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Read MoreUnpacking 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.
Read MoreInroads to personalized AI trip planning
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.
Read MoreMelding data, systems, and society
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.
Read MoreAI-enabled control system helps autonomous drones stay on target in uncertain environments
The system automatically learns to adapt to unknown disturbances such as gusting winds.
Read MoreAn anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
Read MoreLearning how to predict rare kinds of failures
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
Read MoreThe sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
Read MoreWith AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
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