“Generally, if people were more informed about the average
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(environmental) cost of generating a response, people would maybe start thinking, ‘Is it really necessary to turn myself into an action figure just because I’m bored?’ Or ‘do I have to tell ChatGPT jokes because I have nothing to do?’” Dauner said.
Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said.
“We don’t need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,” Luccioni said. “This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.”
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With less available information about AI’s resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy.
“Overall, I’m still positive about (the future). There are many software engineers working hard to improve resource efficiency,” Ren said. “Other industries consume a lot of energy too, but it’s not a reason to suggest AI’s environmental impact is not a problem. We should definitely pay attention.”
Sign up for CNN’s Life, But Greener newsletter. Our limited newsletter series guides you on how to minimize your personal role in the climate crisis — and reduce your eco-anxiety.
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[url=https://tripscan.biz]tripscan войти[/url] Whether it’s answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people’s lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called “token IDs” and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. https://tripscan.biz трип скан So, for each prompt you give AI, what’s the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, “smarter” LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. “This shows us the tradeoff between energy consumption and the accuracy of model performance,” said Maximilian Dauner, a doctoral student at Hochschule Munchen University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. “You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,” Dauner said. What you can do to reduce your carbon footprint Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. |
