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 colleague 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.”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 systemsInsight into large bodies of data, such as analyzing the physical and transition risk to a company’s assetsAccelerating technology innovationThey 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.The Interchange is brought to you by Smarter Grid Solutions, a leading enterprise energy management software company. Find out how Smarter Grid Solutions’ software can give you real control over your clean energy assets at sgsderms.com/interchange.The Interchange is brought to you by Enel X, a leader in energy storage, DER management software, and smart electric vehicle charging stations to increase project value. Learn what Enel X can do for your business.