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The capabilities of Artificial Intelligence and Machine Learning are impressive, but the achievements have often come with massive amounts of computer cycle processing. The intensive processing can use a lot of power and generate huge amounts of carbon emissions. Training of a single AI system can produce hundreds and thousands of pounds of emissions.
Andrea Renda, head of global governance and digital economy expert at the Centre for European Policy Studies in Brussels, said that “all of these data-intensive techniques are extremely dangerous for the environment unless you can use those techniques in a way that, while using a lot more energy, save a lot more energy because they provide for more efficient solutions.”
Anders Andrae, Swedish researcher at Huawei, said that “the situation is alarming. We have a tsunami of data approaching. Everything which can be is being digitized. It is a perfect storm. 5G is coming, IP traffic is much higher than estimated, and all cars and machines, robots and artificial intelligence are being digitized, producing huge amounts of data which is stored in data centers.”
Emma Strubell, assistant professor at Carnegie Mellon, said that “I’m not against energy use in the name of advancing science, obviously, but I think we could do better in terms of considering the trade off between required energy and resulting model improvement.”