‘Changing Course’: Lectures on Data Representations and Equitable Approaches to Computing

Each year, the Brown Institute sponsors talks that explore the intersection between media and technology. This year we have three virtual presentations lined up, each challenging us to think about data and computation in new ways. Johanna Drucker starts the series by looking at alternatives to representing time — looking beyond graphic standards such as

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Magic Grant projects ‘Public Analysis of TV News’ and ‘Sports Illustrated’ featured on Bloomberg QuickTake

On October 21, 2020, Ashlee Vance, Senior Writer at Bloomberg Businessweek interviewed Stanford Professor Kayvon Fatahalian and highlighted advancements in artificial intelligence through Magic Grant projects Public Analysis of TV News and Sports Illustrated. In the piece, Vance and Fatahalian highlight the tremendous opportunities that AI has provided for media makers. Both also offer thoughtfulness

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Institute Director Agrawala talks Deep Fakes with the Stanford Institute for Human-Centered AI

To spot a deep fake, researchers looked for inconsistencies between “visemes,” or mouth formations, and “phonemes,” the phonetic sounds. Using AI to Detect Seemingly Perfect Deep-Fake Videos was published on the Stanford Institute for Human-Centered Artificial Intelligence Blog on 10/13/2020, which featured Brown Institute Director Maneesh Agrawala. In the article, Agrawala spoke to the struggles

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Magic Grant project ‘Synthesizing Novel Video from GANs’ featured in Engadget

On August 14, 2020, the tech outlet Engadget featured recent research produced by the Magic Grant ‘Synthesizing Novel Video from GANs’ developed by grantee Haotian Zhang, alongside Cristobal Sciutto under faculty direction from Maneesh Agrawala and Kayvon Fatahalian.’ The article titled “These AI-generated tennis matches are both eerie and impressive,” describes the system developed under

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Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

Authors Daniel Y. Fu, Mayee F. Chen, Frederic Sala, Sarah M. Hooper, Kayvon Fatahalian, Christopher Ré Abstract Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estimating the accuracies of multiple noisy labeling sources (e.g., heuristics, crowd workers). Existing approaches

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Magic Grantee Derek Kravitz on the reporting behind The Times’ “Why New Orleans Pushed Ahead With Mardi Gras”

By Alex Calderwood Brown Institute fellow Derek Kravitz and New York Times correspondent Richard Fausset published a story yesterday that detailed why New Orleans officials went ahead with Mardi Gras, despite the growing worries at the time regarding COVID-19. The story, published in The Times as “Why New Orleans Pushed Ahead With Mardi Gras, Even

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