Training and running artificial intelligence models requires copious amounts of electricity, which is resulting in large amounts of carbon emissions
Amid all the excitement about and increasing use of artificial intelligence, there’s growing concern about its darker side — its environmental impact.
Researchers have known for several years now that training large-scale AI models that power applications like ChatGPT requires lots of energy. GPT-3, the model OpenAI later refined for ChatGPT, required an estimated 1,287 megawatt-hours to train, according to a 2021 research paper on which UC Berkeley computer science professor David Patterson was the lead author. That’s equivalent to the amount of electricity 123 average American homes use in a full year.
Similarly, Google has replaced Gopher with the much larger Gemini. Google spokeswoman Corina Standiford declined to say how much energy was needed to train Gemini, but in general, the bigger the model, the more energy is needed to train it, experts say. But all those queries and prompts can add up quickly, and bigger models tend to require more energy for inference than smaller ones, according to the 2023 Luccioni paper. Indeed, thanks to the widespread use of ChatGPT, that model likely used more energy in responding to people’s prompts than was needed to train it within just a few months of its launch, according to the paper.
The 67th SF Film Festival pays tribute to the Bay Area and local voices The festival runs from Wednesday, April 24 through Sunday, April 28 Even a small model can result in significant amounts of emissions. The emissions generated by training a six-billion parameter model just 13% of the way to completion were equivalent to that produced by the average U.S. home over the course of a full year, according to a 2022 paper on which Jesse Dodge, a senior research scientist at the Allen Institute for AI, was the lead author.
Artificial Intelligence Climate Change Energy Use Carbon Emissions Chatgpt Openai Google Gemini Gpt-3
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