Kureha Yamaguchi

Community Manager @ CCAI | University of Cambridge
Information and Computer Engineering Master's Student at the University of Cambridge, researching ML for Climate Change Adaptation

[Podcast] Algorithmic Decisions & Power and Sustainability

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[Report] Climate Change and AI: Recommendations for Government Action

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GPAI Report in collaboration with ο»Ώ Climate Change AI ο»Ώ and Centre For AI & Climate. This report describes key opportunities for AI to facilitate climate action - from optimising power grids to monitoring land use to designing better batteries - with case studies of example projects in the private and public sectors. βš‘πŸ”‹πŸŒ±

Follow the CCAI Twitter to keep up to date with the latest news 🌍
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[COP26] A Clearer Picture: Towards Radical Transparency in Measurement, Reporting and Verification of Climate Action with AI

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You can now watch the COP26 event that CCAI co-organised with the Centre for AI & Climate and Climate TRACE 🌍

This extremely relevant discussion explores how AI can help provide:
  • increased transparency and accountability to global and local climate action
  • insight into how policymakers can facilitate impactful work by leveraging digital MRV (Monitoring, Reporting + Verification)
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Moderator:

  • ο»Ώ David Dao ο»ΏΒ  (ETH ZΓΌrich, Climate Change AI)

Panelists:

  • Dr. Dava Newman (MIT Media Lab)
  • Dr. Marco Schletz (University of North Carolina)
  • Luiza Barguil (XPrize)
  • Matthew Gray (TransitionZero, Climate TRACE)
  • Dr. Alejandro Paredes Trapero (FSC Indigenous Foundation)

Next Happy Hour: Wed 17th November @ 5pm - 6pm ET / 10pm - 11pm GMT+1

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An informal space for you to network and engage in discussions with others interested in or currently working at the intersection of climate change and machine learning.

Sign up --> https://www.eventbrite.com/e/ccai-happy-hour-5pm-et-tickets-161692595907

We hope to see you there!
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[CCAI Webinar] Carbon capture in geological formations optimized by machine learning

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When: Friday, 19 November 2021 from 11-12:00pm Eastern Time (3-4:00pm UTC)

Speakers: Philipp Witte (Researcher/ Applied Scientist Microsoft Research), Qie Zhang (Principal Data and Applied Scientist Microsoft Research)
Register here: https://www.eventbrite.com/e/carbon-capture-in-geological-formations-optimized-by-machine-learning-registration-193702488357

"Carbon capture and storage (CCS) is among the most promising technologies to decarbonize industrial emissions, such as those coming from cement or steel production. The core idea of geological CCS is to compress CO2 emissions and then store them permanently several kilometers beneath the surface in CO2 storage sites. Successful pilots have demonstrated the feasibility of this technology, however, in order to have substantial impact, CCS capacity has to increase hundredfold. The speakers, researchers at Microsoft, will share their experience using machine learning techniques to accelerate the Northern Lights partnership, one of the flagship CCS projects and a collaboration between the Norwegian government and energy companies."
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[Reminder] Deadline for FDL 2022 challenge submission is tonight (Sun 7th Nov), midnight AoE (Anywhere on Earth)

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[Research] Do our coasts need a digital twin?

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I came across some really interesting work by Frontier Development Lab's (FDL) 2021 "Digital Twin Earth - Coast's" team, which is explained very well in their showcase video. It's only 16mins long and definitely worth a watch!

In a nutshell, they've been developing the first coastal digital twin providing fast and accurate water level dynamics at coastal regions using state-of-art deep learning techniques. They hope to forecast potential climate scenarios and discover new dynamics of coastal ecosystems with this digital twin.

Exacerbated by climate change, coastal flooding is increasingly becoming one of the greatest challenges facing human society. So ML application such as this, capable of enhancing flood predictions to inform evacuation orders etc, is crucial. Lives would be saved but thinking in the long term, it still troubles me to think that entire coastal neighbourhoods would have to continue being rebuilt over and over again until... total inundation. I suppose that's the thing about ML for climate change, particularly in the application of climate adaptation. As beautifully explained by  Priya Donti  in How A.I. Will Revolutionize Climate Tech (The Interchange, Jun 2021), in some cases, AI can really only offer a "stopgap" to buy us time until a more long term Climate Change solution comes along. So whilst this digital twin has its limitations, it certainly still serves as a valuable bandaid to increase the safety of coastal populations from rising sea levels for the time being ☺️. What are your thoughts? 
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[CCAI Webinar] Machine learning for Climate Science and Earth Observation

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JOIN US IN 10 MINS TIME for what promises to be another informative CCAI Webinar, featuring two esteemed speakers:

Gustau Camps-Valls Professor, Universitat deValència.
Physics-aware ML for Earth sciences
Maike Sonnewald Associate Research Scholar, Princeton University.
A robust blueprint for trustworthy AI for climate analysis


link --> https://www.eventbrite.com/e/machine-learning-for-climate-science-and-earth-observation-registration-173088290737ο»Ώ
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Next Happy Hour: Wed 20th October @ 5pm - 6pm ET / 10pm - 11pm GMT+1

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An informal space for you to network and engage in discussions with others interested in or currently working at the intersection of climate change and machine learning.

Sign up --> https://www.eventbrite.com/e/ccai-happy-hour-5pm-et-tickets-161692595907

We hope to see you there!
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