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.
Melding 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.
An anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
The sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
A new method for detecting gene-expression patterns linked to lineage progression, providing a powerful tool for studying cell state memory across biological systems.
Algorithms and AI for a better world
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
How hard is it to prevent recurring blackouts in Puerto Rico?
Using the island as a model, researchers demonstrate the “DyMonDS” framework can improve resiliency to extreme weather and ease the integration of new resources.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Beery, Farina, Ghassemi, Kim named AI2050 Early Career Fellows
The new crop of AI2050 Early Career Fellows was announced Dec. 10th.