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[170296] NASA scientists describe ‘absolute sh*tshow’ at agency as Trump budget seeks to dismantle top US climate lab 投稿者：ArthurDiern
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In its place, it would move some of the lab’s functions into a broader environmental modeling effort across the agency.
Career specialists are now working remotely, awaiting details and even more unsure about their future at the lab after they were kicked out of their longtime home in New York City last week. Closing the lab for good could jeopardize its value and the country’s leadership role in global climate science, sources say.
“It’s an absolute sh*tshow,” one GISS scientist said under condition of anonymity because they were not authorized to speak to the media. “Morale at GISS has never been lower, and it feels for all of us that we are being abandoned by NASA leadership.”
“We are supposedly going to be integrated into this new virtual NASA modeling institute, but (we have) no idea what that will actually look like,” they said.
NASA is defending its budget proposal, with a nod toward the lab’s future.
“NASA’s GISS has a significant place in the history of space science and its work is critical for the Earth Science Division, particularly as the division looks to the future of its modeling work and capabilities,” NASA spokesperson Cheryl Warner said in a statement.
“Fundamental contributions in research and applications from GISS directly impact daily life by showing the Earth system connections that impact the air we breathe, our health, the food we grow, and the cities we live in,” Warner said.
GISS has a storied history in climate science on the global scale.
James Hansen, a former director, first called national attention to human-caused global warming at a Senate hearing during the hot summer of 1988. The lab, founded in 1961, is still known worldwide for its computer modeling of the planet that enable scientists to make projections for how climate change may affect global temperatures, precipitation, extreme weather events and other variables.
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[170294] “AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’” 投稿者：Vincentbiami
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Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.”
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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