Mood is a unique way for researchers to try to measure the impact of natural or unnatural disasters on people. However, it's simply impractical to ask every single person in the world how they're feeling in the aftermath of a sweeping event. But scientists from the Massachusetts Institute of Technology, the Chinese Academy of Sciences, and the Max Planck Institute for Human Development found a workaround. They used machine learning techniques to scan social media for sentiment shifts following the first wave of COVID-19 in 100 different countries and get real-time reads on how happy or sad the events related to the pandemic made people across the world. Think of the process as an AI-powered mood ring, but for millions of people.

Researchers used machine learning to analyze the sentiments associated with social media posts at the start of the pandemic.

Eighty-five percent of the world's population lives in areas impacted by human-induced climate change, according to an international team of researchers. They used a new machine learning approach to identify more than 100,000 scientific studies on the effects of climate change across every continent. This massive literature review created a global map of impacts, which the team then compared to changing trends of surface temperature and rain caused by humans. In the age of big data, using AI is an important tool for climate scientists, the researchers say. While it can't substitute for expert assessments like the Intergovernmental Panel on Climate Change (IPPC), using machine learning to sort through climate studies is invaluable to helping map evidence in a systematic way.