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Could an AI Technique Fight Climate Change?
An AI technique called reinforcement learning enables an algorithm to learn how to perform a task using trial and error in a simulator, a digital twin. This technique could be used to solve some of the world’s most complex problems. By applying this technique, we can test initiatives across a digital twin of the Earth that could help us tackle climate change.
Reinforcement learning has applications in every industry. For instance, retailers earlier expected customers to buy as per their behavior patterns as they would indicate their future preferences. But after the COVID-19 pandemic, we live in a world where consumer purchase patterns are evolving rapidly. Now, manufacturers and retailers are under the pressure to build dynamic supply chains that are accountable for climate, political, and societal shifts anywhere in the world at a moment’s notice. This serves a complex problem in terms of optimization and with the right data and feedback loops, it is well suited for solving the problem with reinforcement learning.
This technique could be applied to a digital twin of the earth to test a multitude of different climate-saving initiatives across the globe that can best be sequenced and combined into a mutually reinforcing whole.
The first requirement for this is having enough data. Satellites constantly beam data to the Earth that could be used to advance the understanding of how and why is the climate changing. The global datasphere is set to reach 175 zettabytes by 2025.
Ocean monitoring too has improved drastically. For instance, The US National Oceanic and Atmospheric Administration, monitors ocean temperatures, currents, levels, and chemistry using thousands of buoys and floats that take daily measurements at surface and deep levels.
Also, some of the world’s largest companies are waking up to their environmental responsibilities, launching many climate-saving initiatives with ambitious goals. Reinforcement learning could assess the effects of these initiatives as well.
In March the European Commission announced its ‘Destination Earth’ initiative where scientists will work to create a digital twin of the Earth that enables the mapping of climate change and the assessment of solutions that could slow or reverse it. The EU plans to open the digital Earth model for use by industry over time. The models generated by the Destination Earth initiative could provide a testing ground to determine whether reinforcement learning could analyze climate initiatives across the world, gauging their collective effect and determining what further actions need to be taken to halt or reverse climate change.
This is a summary of the article published on WEF. Click here to read the full article.
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