DAIR: Creating a new incentive structure for AI research

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.

Berkeley robot learning pioneer Pieter Abbeel wins ACM Prize in Computing

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. 

For Women’s History Month, CDSS female leaders share their own journeys in STEM

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.

The data lifecycle problem: An opportunity for data scientists

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.

New UC Berkeley center will apply data science to solving environmental challenges

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.

Experts urge caution around Meta’s metaverse at UC Berkeley panel

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.

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

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 University of Texas at Austin.

Changing the legacy and future of artificial intelligence

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.

Mobile phone data and machine learning helped Togo government provide assistance

UC Berkeley-led researchers used mobile phone data and machine learning to quickly and accurately direct the Togolese government’s COVID-19 cash assistance to its poorest residents in a first-of-its-kind study published March 16 in Nature. This breakthrough “phone-based approach” gives policymakers another tool to quickly target humanitarian aid in a crisis or where traditional poverty data isn’t available

UC Berkeley to start graduate student research hub on AI policy

UC Berkeley will launch this spring an interdisciplinary initiative to research and propose solutions for governing artificial intelligence, two prominent research institutions at UC Berkeley announced today.