Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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Machine learning and climate risk adaptation
Manage episode 486424957 series 2469488
Content provided by PNAS. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by PNAS or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Using reinforcement learning to plan for an uncertain climate future
Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us.
In this episode, Ning Lin talks about how reinforcement learning methods plant to mitigate climate risk despite uncertainty in climate change risk forecasts.
In this episode, we cover: •[00:00] Introduction •[1:04] Civil engineer Ning Lin introduces why climate forecast uncertainty complicates risk management planning. •[02:41] Lin explains how reinforcement learning works. •[03:26] She talks about why the team studied risk management for Manhattan. •[04:54] Lin explains the results of the reinforcement learning study. •[05:40] She recounts the results that surprised her. •[07:25] Lin explains the takeaways from the study for emergency planners. •[09:00] She enumerates the caveats and limitations of the study. •[10:11] Conclusion.
About Our Guest:
Ning Lin Professor Princeton University
View related content here: https://www.pnas.org/cgi/doi/10.1073/pnas.2402826122
Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs!
Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast
Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter
397 episodes
Share
Manage episode 486424957 series 2469488
Content provided by PNAS. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by PNAS or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Using reinforcement learning to plan for an uncertain climate future
Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us.
In this episode, Ning Lin talks about how reinforcement learning methods plant to mitigate climate risk despite uncertainty in climate change risk forecasts.
In this episode, we cover: •[00:00] Introduction •[1:04] Civil engineer Ning Lin introduces why climate forecast uncertainty complicates risk management planning. •[02:41] Lin explains how reinforcement learning works. •[03:26] She talks about why the team studied risk management for Manhattan. •[04:54] Lin explains the results of the reinforcement learning study. •[05:40] She recounts the results that surprised her. •[07:25] Lin explains the takeaways from the study for emergency planners. •[09:00] She enumerates the caveats and limitations of the study. •[10:11] Conclusion.
About Our Guest:
Ning Lin Professor Princeton University
View related content here: https://www.pnas.org/cgi/doi/10.1073/pnas.2402826122
Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs!
Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast
Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter
397 episodes
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Similar to PNAS Science Sessions
Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
…
continue reading
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…
continue reading
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…
continue reading
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…
continue reading
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…
continue reading
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…
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