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Ben Recht

Professor of computer science at UC Berkeley, specializing in polling and data analysis.

Top 3 podcasts with Ben Recht

Ranked by the Snipd community
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Nov 13, 2024 • 1h 18min

Can We Ever Trust the Polls Again?

Ben Recht is a computer science professor at UC Berkeley with expertise in polling, and Leif Weatherby is a German professor and director of NYU's Digital Theory Lab, specializing in political analysis. They explore whether polling can be trusted after a recent election, discussing its inaccuracies and the societal implications of misinterpreting data. The duo critiques the Democratic Party's messaging and the disconnect with voters, while also navigating the potential landscape for 2028 presidential candidates, blending insights with humor throughout.
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Sep 11, 2024 • 1h 23min

Do We Like Living in DataWorld and iPhones and the End of History with Ben Recht and Leif Weatherby

In a thought-provoking conversation, Ben Recht, a UC Berkeley professor known for his expertise in computer science and machine learning, teams up with Leif Weatherby, an NYU associate professor of German. They explore the implications of Nate Silver's book and the growing obsession with Big Data, critiquing its impact on society and sports. The duo humorously discusses iPhone stagnation as a metaphor for the 'end of history,' examining the tension between data analytics and meaningful narratives across various fields, including politics and content creation.
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Mar 6, 2024 • 1h 56min

AI is still a bit disappointing but at least it uses a lot of energy. A talk with Karen Hao and Ben Recht

In this podcast, they discuss the potential impact of AI technology on water and energy consumption, the challenges of creating efficient AI tools, and the current trends in AI development. They also touch on the environmental concerns surrounding AI operations and the importance of transitioning to smaller, more streamlined AI models. Additionally, the speakers explore the complexities of AI reporting, the advancements in AI music generation, and the parallels between human cognition and machine predictions.