These simple changes can make AI research much more energy efficient
Since the first paper studying this technology’s impact on the environment was published three years ago, a movement has grown among researchers to self-report the energy consumed and emissions generated from their work. Having accurate numbers is an important step toward making changes, but actually gathering those numbers can be a challenge.
“You can’t improve what you can’t measure,” says Jesse Dodge, a research scientist at the Allen Institute for AI in Seattle. “The first step for us, if we want to make progress on reducing emissions, is we have to get a good measurement.”
To that end, the Allen Institute recently collaborated with Microsoft, the AI company Hugging Face, and three universities to create a tool that measures the electricity usage of any machine-learning program that runs on Azure, Microsoft’s cloud service. With it, Azure users building new models can view the total electricity consumed by graphics processing units (GPUs)—computer chips specialized for running calculations in parallel—during every phase of their project, from selecting a model to training it and putting it to use. It’s the first major cloud provider to give users access to information about the energy impact of their machine-learning programs.
While tools already exist that measure energy use and emissions from machine-learning algorithms running on local servers, those tools don’t work when researchers use cloud services provided by companies like Microsoft, Amazon, and Google. Those services don’t give users direct visibility into the GPU, CPU, and memory resources their activities consume—and the existing tools, like Carbontracker, Experiment Tracker, EnergyVis, and CodeCarbon, need those values in order to provide accurate estimates.
The new Azure tool, which debuted in October, currently reports energy use, not emissions. So Dodge and other researchers figured out how to map energy use to emissions, and they presented a companion paper on that work at FAccT, a major computer science conference, in late June. Researchers used a service called Watttime to estimate emissions based on the zip codes of cloud servers running 11 machine-learning models.
They found that emissions can be significantly reduced if researchers use servers in specific geographic locations and at certain times of day. Emissions from training small machine-learning models can be reduced up to 80% if the training starts at times when more renewable electricity is available on the grid, while emissions from large models can be reduced over 20% if the training work is paused when renewable electricity is scarce and restarted when it’s more plentiful.
The Download: toxic chemicals, and Russia’s cyberwar tactics
What are chemical pollutants doing to our bodies? It’s a timely question given that last week, people in Philadelphia cleared grocery shelves of bottled water after a toxic leak from a chemical plant spilled into a tributary of the Delaware River, a source of drinking water for 14 million people. And it was only last month that a train carrying a suite of other hazardous materials derailed in East Palestine, Ohio, unleashing an unknown quantity of toxic chemicals.
There’s no doubt that we are polluting the planet. In order to find out how these pollutants might be affecting our own bodies, we need to work out how we are exposed to them. Which chemicals are we inhaling, eating, and digesting? And how much? The field of exposomics, which seeks to study our exposure to pollutants, among other factors, could help to give us some much-needed answers. Read the full story.
This story is from The Checkup, Jessica’s weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
+ The toxic chemicals all around us. Meet Nicolette Bugher, a researcher working to expose the poisons lurking in our environment and discover what they mean for human health. Read the full story.
+ Building a better chemical factory—out of microbes. Professor Kristala Jones Prather is helping to turn microbes into efficient producers of desired chemicals. Read the full story.
+ Microplastics are messing with the microbiomes of seabirds. The next step is to work out what this might mean for their health—and ours. Read the full story.
The Download: sleeping in VR, and promising clean energy projects
People are gathering in virtual spaces to relax, and even sleep, with their headsets on. VR sleep rooms are becoming popular among people who suffer from insomnia or loneliness, offering cozy enclaves where strangers can safely find relaxation and company—most of the time.
Each VR sleep room is created to induce calm. Some imitate beaches and campsites with bonfires, while others re-create hotel rooms or cabins. Soundtracks vary from relaxing beats to nature sounds to absolute silence, while lighting can range from neon disco balls to pitch-black darkness.
The opportunity to sleep in groups can be particularly appealing to isolated or lonely people who want to feel less alone, and safe enough to fall asleep. The trouble is, what if the experience doesn’t make you feel that way? Read the full story.
Inside the conference where researchers are solving the clean-energy puzzle
There are plenty of tried-and-true solutions that can begin to address climate change right now: wind and solar power are being deployed at massive scales, electric vehicles are coming to the mainstream, and new technologies are helping companies make even fossil-fuel production less polluting.
But as we knock out the easy climate wins, we’ll also need to get creative to tackle harder-to-solve sectors and reach net-zero emissions.
Inside the conference where researchers are solving the clean-energy puzzle
The Advanced Research Projects Agency for Energy (ARPA-E) funds high-risk, high-reward energy research projects, and each year the agency hosts a summit where funding recipients and other researchers and companies in energy can gather to talk about what’s new in the field.
As I listened to presentations, met with researchers, and—especially—wandered around the showcase, I often had a vague feeling of whiplash. Standing at one booth trying to wrap my head around how we might measure carbon stored by plants, I would look over and see another group focused on making nuclear fusion a more practical way to power the world.
There are plenty of tried-and-true solutions that can begin to address climate change right now: wind and solar power are being deployed at massive scales, electric vehicles are coming to the mainstream, and new technologies are helping companies make even fossil-fuel production less polluting. But as we knock out the easy wins, we’ll also need to get creative to tackle harder-to-solve sectors and reach net-zero emissions. Here are a few intriguing projects from the ARPA-E showcase that caught my eye.
“I heard you have rocks here!” I exclaimed as I approached the Quaise Energy station.
Quaise’s booth featured a screen flashing through some fast facts and demonstration videos. And sure enough, laid out on the table were two slabs of rock. They looked a bit worse for wear, each sporting a hole about the size of a quarter in the middle, singed around the edges.
These rocks earned their scorch marks in service of a big goal: making geothermal power possible anywhere. Today, the high temperatures needed to generate electricity using heat from the Earth are only accessible close to the surface in certain places on the planet, like Iceland or the western US.
Geothermal power could in theory be deployed anywhere, if we could drill deep enough. Getting there won’t be easy, though, and could require drilling 20 kilometers (12 miles) beneath the surface. That’s deeper than any oil and gas drilling done today.
Rather than grinding through layers of granite with conventional drilling technology, Quaise plans to get through the more obstinate parts of the Earth’s crust by using high-powered millimeter waves to vaporize rock. (It’s sort of like lasers, but not quite.)