It’s time to get creative in the fight against climate change, and machine learning can help us do that. Not Cool episode 17 continues our discussion of “Tackling Climate Change with Machine Learning,” a nearly 100 page report co-authored by 22 researchers from some of the world’s top AI institutes. Today, Ariel talks to Natasha Jaques and Tegan Maharaj, the respective authors of the report’s “Tools for Individuals” and “Tools for Society” chapters. Natasha and Tegan explain how machine learning can help individuals lower their carbon footprints and aid politicians in implementing better climate policies. They also discuss uncertainty in climate predictions, the relative price of green technology, and responsible machine learning development and use.

Topics discussed include:

-Reinforcement learning

-Individual carbon footprints

-Privacy concerns

-Residential electricity use

-Asymmetrical uncertainty

-Natural language processing and sentiment analysis

-Multi-objective optimization and multi-criteria decision making

-Hedonic pricing

-Public goods problems

-Evolutionary game theory

-Carbon offsets

-Nuclear energy

-Interdisciplinary collaboration

-Descriptive vs. prescriptive uses of ML