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“AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’â€
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Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.â€
For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.
Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.
“Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,†Luccioni explained.
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If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator.
Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.
When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.
Why it’s hard to measure AI’s environmental impact
Putting a number on the environmental impact of AI has proved challenging.
The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models.
That’s partly why the researchers chose to represent carbon emissions within a range, Dauner said.
Furthermore, many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI’s water consumption.
“You can’t really say AI consumes this much energy or water on average — that’s just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,†Ren said.
One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested.
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Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.â€
For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.
Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.
“Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,†Luccioni explained.
https://tripscan.biz
tripskan
If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator.
Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.
When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.
Why it’s hard to measure AI’s environmental impact
Putting a number on the environmental impact of AI has proved challenging.
The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models.
That’s partly why the researchers chose to represent carbon emissions within a range, Dauner said.
Furthermore, many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI’s water consumption.
“You can’t really say AI consumes this much energy or water on average — that’s just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,†Ren said.
One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested.
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The way to bring down costs, Gibson believes, is more panelized, multi-family housing.
“It can be done so much more efficiently,†Gibson said, “and there’s a lot more repetition†for the developer, making the process faster and less expensive than custom multi-family builds.
Goodson, the homeowner in Maine, was able to save big money with his engineering background and penchant for DIY. He installed a rooftop solar system and electrical improvements himself, saving hundreds of thousands of dollars in the process. He wound up spending around $500,000 in all, which he estimates was $200,000 less than he otherwise would have.
“It’s a big number to swallow, I’m not making light of that at all, but it’s not that far out of what’s reasonable,†Goodson told CNN. It’s also not considering the long-term savings he will experience with no utility bills.
He was also able to take advantage of federal tax credits that reduced the cost of his rooftop solar, which saved him more than $10,000 on his panels. Those tax credits are now endangered with House Republicans’ tax bill.
“That was huge,†he said. “It’s fairly unfortunate they’re looking at doing away with it.â€
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Homeowners also need to factor in additional costs, like buying and developing a suitable plot of land, and in some cases, getting access to water, electricity and septic, Gibson added.
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The way to bring down costs, Gibson believes, is more panelized, multi-family housing.
“It can be done so much more efficiently,†Gibson said, “and there’s a lot more repetition†for the developer, making the process faster and less expensive than custom multi-family builds.
Goodson, the homeowner in Maine, was able to save big money with his engineering background and penchant for DIY. He installed a rooftop solar system and electrical improvements himself, saving hundreds of thousands of dollars in the process. He wound up spending around $500,000 in all, which he estimates was $200,000 less than he otherwise would have.
“It’s a big number to swallow, I’m not making light of that at all, but it’s not that far out of what’s reasonable,†Goodson told CNN. It’s also not considering the long-term savings he will experience with no utility bills.
He was also able to take advantage of federal tax credits that reduced the cost of his rooftop solar, which saved him more than $10,000 on his panels. Those tax credits are now endangered with House Republicans’ tax bill.
“That was huge,†he said. “It’s fairly unfortunate they’re looking at doing away with it.â€
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