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In early April, when the dust settled after mass firings across HHS, workers in NCI’s communications office were relieved they still had their jobs.
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It didn’t last. A month later, HHS fired nearly all of them, three former workers said. Combined with retirements and other departures, a skeleton crew of six or seven remain of about 75 people. “We were all completely blindsided,” a fired worker said. NCI leadership “had no idea that this was happening.”
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As a result, websites, newsletters, and other resources for patients and doctors about the latest evidence in cancer treatment aren’t being updated. They include Cancer.gov and NCI’s widely used Physician Data Query, which compile research findings that doctors turn to when caring for cancer patients.

Gary Kreps, founding director of the Center for Health and Risk Communication at George Mason University, said he relied on Physician Data Query when his father was diagnosed with advanced stomach cancer, taking PDQ printouts when he met with his dad’s doctors. “It made a huge difference,” Kreps said. “He ended up living, like, another three years” — longer than expected — “and enjoyed the rest of his life.”

As of May 30, banners at the top of the Cancer.gov and PDQ websites said, “Due to HHS restructuring and reduction in workforce efforts, the information on this website may not be up to date and pages will indicate as such.” The banners are gone, but neither website was being updated, according to a fired worker with knowledge of the situation.

Outdated PDQ information is “really very dangerous,” Kreps said.

Wiping out NCI’s communications staff makes it harder to share complex and ever-changing information that doctors and patients need, said Peter Garrett, who headed NCI’s communications before retiring in May. Garrett said he left because of concerns about political interference.

“The science isn’t finished until it’s communicated,” he said. “Without the government playing that role, who’s going to step in?”

“AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’”
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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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