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How can physics help solve real world problems? - NEIL JOHNSON, Head of Dynamic Online Networks Lab

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内容由Mia Funk, AI, and Robotics Interviews - Creative Process Original Series提供。所有播客内容(包括剧集、图形和播客描述)均由 Mia Funk, AI, and Robotics Interviews - Creative Process Original Series 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

How can physics help solve messy, real world problems? How can we embrace the possibilities of AI while limiting existential risk and abuse by bad actors?

Neil Johnson is a physics professor at George Washington University. His new initiative in Complexity and Data Science at the Dynamic Online Networks Lab combines cross-disciplinary fundamental research with data science to attack complex real-world problems. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures to the online world of collective behavior on social media.

“A lot of our work is comparative. We look at background behavior. Is there a burst of new activity? We zoom in on that and ask why that is suddenly appearing and why it didn't appear before. Imagine one day you wake up and you find water in a pot is boiling and you want to understand why water is boiling. If you go at it one molecule at a time, it's not giving you the big picture of what is going on. We've probably all done this: you take milk, stick it in the fridge, too lazy to go to the grocery, so you just leave it there. The 11th day, the milk's gone bad. Why did that happen on the 11th day? What was happening was that all you could see was the kind of macro level, you couldn't see the individual pieces of milk. This is a new area of physics, exactly the same as how shock waves—a wave that builds up so quickly, there's no kind of precursor—appear. Using the data we collect online, we have a tool for making predictions of when we expect shocks to arise and what shape they'll have. So the reason we went for a systems level view is because you can't understand water boiling one molecule at a time.”

https://physics.columbian.gwu.edu/neil-johnsonhttps://donlab.columbian.gwu.edu

www.creativeprocess.infowww.oneplanetpodcast.org IG www.instagram.com/creativeprocesspodcast

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Manage episode 418217044 series 3334556
内容由Mia Funk, AI, and Robotics Interviews - Creative Process Original Series提供。所有播客内容(包括剧集、图形和播客描述)均由 Mia Funk, AI, and Robotics Interviews - Creative Process Original Series 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

How can physics help solve messy, real world problems? How can we embrace the possibilities of AI while limiting existential risk and abuse by bad actors?

Neil Johnson is a physics professor at George Washington University. His new initiative in Complexity and Data Science at the Dynamic Online Networks Lab combines cross-disciplinary fundamental research with data science to attack complex real-world problems. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures to the online world of collective behavior on social media.

“A lot of our work is comparative. We look at background behavior. Is there a burst of new activity? We zoom in on that and ask why that is suddenly appearing and why it didn't appear before. Imagine one day you wake up and you find water in a pot is boiling and you want to understand why water is boiling. If you go at it one molecule at a time, it's not giving you the big picture of what is going on. We've probably all done this: you take milk, stick it in the fridge, too lazy to go to the grocery, so you just leave it there. The 11th day, the milk's gone bad. Why did that happen on the 11th day? What was happening was that all you could see was the kind of macro level, you couldn't see the individual pieces of milk. This is a new area of physics, exactly the same as how shock waves—a wave that builds up so quickly, there's no kind of precursor—appear. Using the data we collect online, we have a tool for making predictions of when we expect shocks to arise and what shape they'll have. So the reason we went for a systems level view is because you can't understand water boiling one molecule at a time.”

https://physics.columbian.gwu.edu/neil-johnsonhttps://donlab.columbian.gwu.edu

www.creativeprocess.infowww.oneplanetpodcast.org IG www.instagram.com/creativeprocesspodcast

  continue reading

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