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When Niloufar Salehi was six years old, her brother swallowed a coin. Her mom didn’t speak English. So, when the ambulance got there, she translated between the doctors and her mom, a stressful task. Today, Salehi is researching how to improve machine translation in high-stakes settings, especially hospitals.

Dave Epstein, a doctoral student in UC Berkeley’s Department of Electrical Engineering and Computer Sciences, has been named to the class of 2022 Paul & Daisy Soros Fellows. The fellows are immigrants and children of immigrants chosen for “their potential to make significant contributions to the United States."

Timnit Gebru's exit from Google showed why it’s dangerous for so much of AI research to be funded by big technology companies, some said. But, at a UC Berkeley discussion group, Gebru said the problem is much larger.

The Association for Computing Machinery (ACM), the world’s largest scientific and educational computing society, has awarded UC Berkeley professor Pieter Abbeel the 2021 ACM Prize in Computing for his foundational work in robot learning.

At UC Berkeley's Division of Computing, Data Science, and Society, women aren’t just participating in STEM. They’re leading. And they’re among many women at Berkeley working to change practices in STEM to make it easier for young women to find a sense of belonging, representation and success.

Data scientists can improve the accuracy, speed and longevity of science by focusing on solving issues related to the “life cycle” of data, said Deb Agarwal, a Lawrence Berkeley National Laboratory senior scientist, at the Women in Data Science at Berkeley conference this month.

A new research center at the University of California, Berkeley, funded by alumni Eric and Wendy Schmidt, will tackle major environmental challenges including climate change and biodiversity loss by combining data science and environmental science.

The public shouldn’t indiscriminately buy into big technology companies’ visions for the metaverse, a space being sold as the next digital frontier, experts said at a UC Berkeley panel.

Significant advances in artificial intelligence (AI) over the past decade have relied upon extensive training of algorithms using massive, open-source databases. But when such datasets are used “off label” and applied in unintended ways, the results are subject to machine learning bias that compromises the integrity of the AI algorithm, according to a new study by researchers at the University of California, Berkeley, and the...

We need to urgently rethink how we create, use, communicate and educate around artificial intelligence, said a multidisciplinary group of experts during a recent UC Berkeley Tanner Lecture discussion.