[Future / Now] — Many Cities won’t Be Able to Catch Up with Climate Change
AI+News: stories written by journalists empowered by AI
Massive natural disasters will happen more often, bringing devastation to urban and rural areas around the world. Critical infrastructure will fail. Reconstruction efforts will be wiped by new disasters, creating cycles of suffering for millions of people, who will have to survive during extended periods of time without water, food supplies, electricity, or a sustained income.
Take for example Bangladesh, where the recurrence of floods, typhoons, droughts, and other extreme weather events have created a wave of “climate refugees’’ that have migrated to the capital Dakha, bringing the city to a near breaking point. The Natural Resource Defense Council points out that cities around the world will suffer devastation due to global warming, from Miami Beach to Mumbai, while others, like New York and Mexico City, are taking the right measures to be ready when the next, inevitable strike, occurs.
This story was written by a journalist empowered by AI.
The journalist is Giomar Silva (@G_SV), founder of Migrante21 (@Migrante21). Giomar has an extensive background as a reporter and editor in Peru and Washington, D.C. After covering stories about human rights, culture, technology and politics in Peru, he focused on immigrant and minorities issues as a web editor at Washington Hispanic, the largest Spanish-language newspaper in the D.C. area. His interest in these topics led him to found Migrante21, a bilingual website that aims to document the immigrant experience in America.
The AI is Minerva, a system created by Libre AI, that predicts and visualizes the (non-obvious) interconnections of global risks that will be at the core of tomorrow’s news.
Minerva leverages news data collections available in the Web and uses Artificial Intelligence based on Machine Learning (AI/ML) to discover the multiple relations among global risks, a data-driven approach that is more appealing in terms of timeliness and efficient discovery of such relations than current methodologies based on opinion surveys.
