5 Ways Artificial Intelligence Can Help Save The Planet

If the world’s natural resources are increasingly stressed and depleted, the silver lining may be that we’re becoming better equipped at tracking that destruction and potentially doing something about it. Cheap, widespread sensor networks, the internet of things, magnitude-improvements in computing power, open source algorithms–these all allow us to manage oceans and forests more effectively, if we want the opportunity. Artificial intelligence systems that can sense, think, learn, and act on their own could allow a major upgrade in conservation efforts, in dealing with climate change, and living in a more energy-efficient manner.

A report released during the recent Davos World Economic Forum meeting laid more than 80 potential environmental applications for AI, ranging from the mundane to the futuristic. We spoke with Celine Herweijer, a partner at consultants PwC and one of the authors of the report. She argues that AI is now going mainstream: Algorithms and supercomputers that once were limited to specialist researchers at universities and government labs are now open to startups and everyday corporations. New ways of managing ecologically relevant systems are opening up as never before.

Solar, wind, and other renewables have the advantage of being carbon-free and ubiquitous. They can be situated in villages and towns and out-of-the-way places, bringing energy closer to everyone who needs it. The challenge is stitching these disparate sources together into a coherent, functional whole. That’s where autonomous systems come in. They can deal with the intermittency of renewables and react to the ebb and flow: when one source of power is coming online or going down, or when one user is ramping up demand and another is clocking off for the night. AI systems are flexible and they can do more work, and be in more places, than human grid managers.

“When you have a complex system with so many sources of renewables, you need them to talk to one another, so you can do storage and optimize the load,” Herweijer tells me. “That can’t happen without artificial intelligence enabling all these new sources to come together. They will enable these future systems where we have peer-to-peer energy trading and community exchange. They are what we need for a decentralized, autonomous grid.”

Similarly, AI will allow for a more decentralized water system, driven by sensors and new technologies like blockchain, Herweijer says. Smart contracts–legal arrangements automated with code–can enable swift trading of assets, including water rights. “Blockchain is vital for recording provenance, then you can have smart contracts and have people trading between parts of the decentralized network,” Herweijer says. “Utilities of the future, whether water or energy, will be more decentralized because that improves productivity.” The Department of Energy has some early-stage AI-based grid systems in development.

Modeling future weather events and climate patterns means processing complicated physical equations, like the fluid dynamics of the atmosphere and oceans. Climate scientists have relied on supercomputers, like the one at the Argonne National Laboratory, outside Chicago, to do their calculations. But there are only a few dozen true supercomputers around the world, meaning that access is limited: Many other scientific fields also require big computational capacity.

Deep-learning techniques, inspired by the way the human brain processes information, incorporate some of the complexity of the real world in climate modeling, allowing computers to run faster and do more calculations within a given period. “We’ll do simulations and modeling on home computers than we do now on supercomputers,” Herweijer says. “We can model small-scale features like wind storms that we struggled with in the past. Once you put AI in the system, you’ve got more people doing simulations and they’re doing it quickly. Forecasting of weather and climate impacts is going to get better rapidly over the next 10 years.”