The array of AI applications within climate tech is staggering -- and rapidly expanding.
There are lots of exciting point solutions, but there’s no clear example of AI directly and meaningfully reducing GHG emissions on a global scale. Yet.
Last year we had Priya Donti on the show. She’s a PhD student at Carnegie Mellon and co-chair of the Climate Change AI organization. This week, she came back with her Climate Change AI co-chair Lynn Kaack, a postdoc researcher at ETH-Zurich.
Priya and Lynn were co-authors on a blockbuster paper on the topic back in June 2019, called “Tackling climate change with machine learning.”
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They came back on the show to talk about what has changed since that episode -- both the progress and the bottlenecks in applying AI to climate change.
They detail the strengths and weaknesses of AI in climate technology using a few case studies:
- Optimizing power and heating/cooling systems
- Insight into large bodies of data, such as analyzing the physical and transition risk to a company’s assets
- Accelerating technology innovation
They also discuss the organizational approaches to AI: Do you go vertical or horizontal? That is, do you hire AI practitioners to work within an organization with deep domain experience, such as a utility, or is it more effective to leave those challenges to an organization of AI generalists who work across many fields?
Lynn points out there’s a third way: spinning up an AI group within an organization.
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