[R] Tackling Climate Change with Machine Learning – video & blog post summary
Dear ML community,
The paper “Tackling Climate Change with Machine Learning” was the most interesting paper i have come across since I work in the data science realm. It was created by 22 AI researchers including Andrew Ng, Yoshua Bengio, David Rolnick and others from Google, Stanford, Harvard, Deepmind, Microsoft Research etc.
Because i believe that it contains many great research works and projects which deserve more attention, i spent the last weeks and weekends to create a video summary and a blog post series, which try to give an easy to grasp overview.
Here is the video summary: https://youtu.be/pHdv4o0mfd0
And here are the parts of the blog post series:
- Electricity Systems
- Buildings & Cities
- Farms & Forests
- Industry & Carbon Dioxide Removal
- Datasets & further resources
If you want to learn more afterwards, check out the http://climatechange.ai project, which emerged from the paper, where you will find further resources, such as datasets, initiatives and talks from ICML 2019.
There will be workshops at NeurIPS 2019 (Vancouver, Canada) and AMLD 2020 (Lausanne, Switzerland) that will focus on this matter as well.
Machine Learning is not a miracle cure and cannot solve all climate change related problems. Policy makers must decide to act to drive large-scale progress. But ML is an invaluable tool which can reduce greenhouse gas emissions in many domains and sometimes even help create better policies, as the research shows.
I hope this summary will spark further ideas and maybe inspire you to do something about one of the greatest challenges we face as a planet. Let’s use the diverse talents we have to drive some progress and create a better future!