Chad Small
If you ask environmentalists about artificial intelligence, they will likely say its biggest drawback involves energy use. One AI research company estimates that querying a service like ChatGPT or Google AI uses 30 times as much energy as a conventional Google search. Energy use is not the only environmental component of AI. Cooling the large data centers that house these AI tools requires enormous amounts of water, an increasingly scarce resource in many parts of the United States. One large data center can suck up the same amount of water per day as a small town. Others have pointed out that AI chatbots can spread misinformation about the climate crisis.
Many environmentalists have argued that AI should be significantly curtailed for these reasons. Perhaps unexpectedly, Earth scientists—researchers who study the environment to make life-saving discoveries—have found themselves at odds with this group of natural allies.
Whether it be for weather forecasting or earthquake detection, Earth scientists argue that AI—particularly machine learning, which is an AI technique that autonomously pulls insights from pools of data—is the most energy-efficient and fastest way to make new breakthroughs. “Traditional” weather forecasters and other researchers have exclusively used computers to solve equations that past scientists would have had to solve by hand. But even if a computer does these calculations much faster than a person, it still takes energy, and a lot of it. AI models for Earth science use pattern recognition and other inference tools to produce results, instead of explicitly solving equations, which brings down energy costs. But even for this application, AI, like any new technology, is not a perfect solution.
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