There are applications for this in nearly every industry. For example, whereas once retailers could reasonably expect that past consumer behaviors would indicate future preferences, they now operate in a world where consumer purchase patterns and preferences evolve rapidly--all the more so as the COVID-19 pandemic repeatedly redefines life. Manufacturers and consumer-packaged-goods companies are under pressure to build dynamic supply chains that account for climate, political and societal shifts anywhere in the world at a moment's notice. Each of these challenges represents a complex and highly dynamic optimization problem, which, with the right data and feedback loops, is well suited for solving with reinforcement learning.

We are at the cusp of a geospatial artificial intelligence (AI) revolution. At its core, Geospatial AI is a discipline that combines innovations in spatial science and AI methods to extract knowledge from spatial big data, which encompasses any data related to the Earth's surface. Astoundingly, the market of geospatial analytics is expected to grow at a CAGR of 24% between 2020-2025. As the technology continues to develop at a record-breaking pace, one of the most important applications of Geospatial AI is to help mitigate and combat the effects of climate change and natural disasters. Data gathered through geospatial techniques on factors like flood levels, land surface temperatures, and wind speeds can provide vital insights to governments and local authorities.

Some of the biggest names in AI research have laid out a road map suggesting how machine learning can help save our planet and humanity from imminent peril. The report covers possible machine-learning interventions in 13 domains, from electricity systems to farms and forests to climate prediction. Within each domain, it breaks out the contributions for various subdisciplines within machine learning, including computer vision, natural-language processing, and reinforcement learning. Recommendations are also divided into three categories: "high leverage" for problems well suited to machine learning where such interventions may have an especially great impact; "long-term" for solutions that won't have payoffs until 2040; and "high risk" for pursuits that have less certain outcomes, either because the technology isn't mature or because not enough is known to assess the consequences. Many of the recommendations also summarize existing efforts that are already happening but not yet at scale.

Every part of our daily lives can play a role in causing it, from electricity, to transportation, the homes we live in, the food we eat, even the healthcare services we rely on. And all of those aspects of our lives are also affected by climate change. Much of what we know about the impacts of climate change comes from sophisticated computer models. Now, a group of computer scientists is calling on their colleagues to put advanced computing and artificial intelligence to work to solve the climate problem. "A.I. can help pinpoint where deforestation is happening using satellite imagery or aerial imagery," said David Rolnick, lead author of a new study outlining how artificial intelligence could help with climate change.

Every part of our daily lives can play a role in causing it, from electricity, to transportation, the homes we live in, the food we eat, even the healthcare services we rely on. And all of those aspects of our lives are also affected by climate change. Much of what we know about the impacts of climate change comes from sophisticated computer models. Now, a group of computer scientists is calling on their colleagues to put advanced computing and artificial intelligence to work to solve the climate problem. "I can help pinpoint where deforestation is happening using satellite imagery or aerial imagery," said David Rolnick, lead author of a new study outlining how artificial intelligence could help with climate change.

Much of modern-day agriculture is dominated by monoculture, the practice of producing a single crop on a large swath of land. This approach makes it easier for farmers to manage their fields with tractors and other basic automated tools, but it also strips the soil of nutrients and reduces its productivity. As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide. Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.