Penn postdoctoral research fellow David Rolnick co-authored a paper on artificial intelligence's ability to help combat climate change.
The paper, titled “Tackling Climate Change with Machine Learning,” argues that machine learning can be used in many climate change related areas including energy production, carbon dioxide removal, and solar geoengineering, according to National Geographic. The paper was originally submitted in June and revised earlier this month.
In the paper, the researchers wrote that artificial intelligence could help assess damage after disasters, select new materials to use for batteries or carbon capture technology, and reduce food waste. Rolnick, a National Science Foundation Mathematical Sciences Postdoctoral Research Fellow in the Körding Lab at Penn, served as the lead author.
“AI can help pinpoint where deforestation is happening using satellite imagery or aerial imagery,” Rolnick told WCAI. “Or [we can] gather data on where buildings and infrastructure are, which is essential to policymakers in developing appropriate policies around the world.”
The researchers wrote that computer models could also help design new carbon markets and expand “precision agriculture," or the accurate and controlled practice of using technology to grow crops and raise livestock.
“It's not generally appreciated just how much carbon emissions come from agriculture,” Rolnick told WCAI.
Yet, while computerized analysis can provide massive amounts of information to scientists and government leaders, the researchers added that data alone cannot solve everything.
“AI is not a silver bullet,” Rolnick told National Geographic.