气候行动的机遇、挑战和风险.pdf

1、Opportunities,Challenges,&Risks of AI for Climate ActionPriya L.DontiMIT,Climate Change AI2Rule-based systems:Techniques that automatically reason over an explicit set of rulesMachine learning(ML):Techniques that automatically extract patterns from dataWhat is AI?Artificial intelligence(AI):Any algo

2、rithm that allows a machine to perform a complex task(e.g.,tasks associated with human intelligence)Electricity systemsBuildingsTransportationClimate predictionIndustrySocietal adaptationLand useDistilling raw data into insights(emissions,deforestation,buildings,crops,policy)4AI for climate action:R

3、ecurring themesFigure source:Nacpil and Cortez(2023)AI for climate action:Recurring themesDistilling raw data into insights(emissions,deforestation,buildings,crops,policy)Improving predictions(renewables,transportation demand,extreme events)5Image source:Open Climate FixAI for climate action:Recurri

4、ng themesDistilling raw data into insights(emissions,deforestation,buildings,crops,policy)Improving predictions(renewables,transportation demand,extreme events)Optimizing complex systems(heating and cooling,power grids,freight)6Images:Public domainAI for climate action:Recurring themesDistilling raw

5、 data into insights(emissions,deforestation,buildings,crops,policy)Improving predictions(renewables,transportation demand,extreme events)Optimizing complex systems(heating and cooling,power grids,freight)Predictive maintenance(methane leaks,resilient infrastructure)7Figure source:Aitio&Howey(2021)AI

6、 for climate action:Recurring themesDistilling raw data into insights(emissions,deforestation,buildings,crops,policy)Improving predictions(renewables,transportation demand,extreme events)Optimizing complex systems(heating and cooling,power grids,freight)Predictive maintenance(methane leaks,resilient