Every company betting that artificial intelligence will transform how we work and live has a big—and growing—problem: AI is inherently ravenous for electricity.
Some experts project that global electricity consumption for AI systems could soon require adding the equivalent of a small country’s worth of power generation to our planet. That demand comes as the world is trying to electrify as much as possible and decarbonize how that power is generated in the face of climate change.
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In recent years, the enthusiasm for AI technologies has grown, ranging from innovative applications in facial recognition, autonomous vehicles, smart home technology, and more. While it holds great promise for our future, the AI industry bears one looming challenge that threatens its longevity: energy efficiency. AI requires hefty amounts of computational power spurred by enormous energy consumption. This inequity has led many to question whether sustainable sources of energy can satisfy the ever-growing appetites of AI and machine learning.
Researchers and industry professionals have explored a wide variety of solutions to improve the efficiency of AI. For example, companies have tested the power of quantum computing, a new form of processing power with the potential to solve complex problems faster. Though this application is far from widespread, tests have revealed that it is more energy-efficient than traditional AI models.
Along with technological advancements, the renewable energy sector has been able to meet the increasing energy needs of AI. In particular, solar power has been under consideration as a solution due to its widespread availability and the growing industry of solar efficiency and storage. Analysts have reported that up to 50% of AI’s energy needs can be met by the power of the sun.
It is also important to note that new energy-efficient hardware solutions have made a significant contribution to the AI industry. For instance, specialized devices such as GPUs have been shown to be up to 34 times more powerful than conventional CPUs. These new advancements can shorten the time needed for AI models to be trained and increase the accuracy of their results.
Overall, AI is becoming ravenous for energy, and the challenge of creating a sustainable solution to satisfy its appetite lies at the frontiers of technology and energy efficiency. Research and development in areas such as quantum computing, renewable energy sources, and energy-efficient hardware have the potential to lighten the load imposed by AI’s burgeoning energy requirements. In the end, it is still up to the industry experts to find and implement a solution that is both sustainable and effective in supporting the future of AI.
