Name: AI Can Help Fight Climate Change, But It Also Adds to It
Uploaded: 2023-10-02T21:32:00Z
Duration: 6 min 8 s
Description: Artificial intelligence could help reduce carbon emissions and water use. But AI itself uses a lot of energy to do that. Host Zoe Thomas speaks with Nuha Dolby about her reporting on how companies can reduce AI’s climate impact. Plus, TNB looks at whether it’s possible to have a modern career without a smartphone. Photo: Mark Lennihan/AP Photo

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Artificial intelligence could help reduce carbon emissions and water use. But AI itself uses a lot of energy to do that. Host Zoe Thomas speaks with Nuha Dolby about her reporting on how companies can reduce AI’s climate impact. Plus, TNB looks at whether it’s possible to have a modern career without a smartphone. Photo: Mark Lennihan/AP Photo

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AI Can Help Fight Climate Change, But It Also Adds to It

Zoe: Welcome to "Tech News Briefing." It's Monday, October 2nd. I'm Zoe Thomas for The Wall Street Journal. Technology can have a big impact on the climate. Data centers emit about the same greenhouse gas as the aviation industry and consume a lot of water. But artificial intelligence can help us better understand and cut down on that, if we can limit AI's own climate impact. Here to talk about how to do that is Nuha Dolby who reported on this for The WSJ's Sustainability Pro team. Nuha, we talk a lot on this show about the potential of AI, one thing it could do is help reduce climate change impact. How could it do that?

Nuha: Yeah, so there are a number of ways, but broadly it's about improving efficiency. So for instance, Alphabet's Google and American Airlines have used artificial intelligence and they've used that to help planes create fewer vapor trails and those vapor trails actually contribute to global warming. The companies also use it to forecast river floods and they can use it to recommend eco-friendly routes. There are startups too, based out of predominantly San Francisco, one's using AI to simplify the process for companies to get clean power. And that's also being used by everyday people too, so lots of people and image generators that have become pretty, pretty popular have used it to generate images of what warmer worlds will look like, so things like ocean encroachment to looking at what the world would look like on fire if it gets hot.

Zoe: All right, let's talk about AI's own climate impact. Have there been any studies about AI and its energy use?

Nuha: Yeah, so this is kind of a newer area that people are looking into, but there are a couple studies. One came out of Hugging Face, which is an AI app developer, and the research scientists there had decided to map the lifetime carbon footprint of a machine learning model with 176 billion parameters and that model's called BLOOM. So the factors outside of the energy used just in training the models, something that a lot of research in that area hasn't really factored in, wound up being so large that they actually doubled the total emissions of the entire model. So for instance, manufacturing a GPU, or, like, a graphics processing unit, it's a piece of hardware that's in most computers and is also used in this deep machine learning, manufacturing those involves a lot of pure water and rare metals and that kind of thing and that'll add to the climate cost. BLOOM just alone used more of 1,000 of those GPUs, and that's just one of many factors that this research took into account.

Zoe: So more impact than just its energy use, but if we were to look at BLOOM's energy use, do we have a sense of the scale there?

Nuha: So for a model of similar size that might be more familiar, OpenAI's ChatGPT-3 had significantly higher carbon emissions, so more than 20 times higher, and it consumed three times as much power as BLOOM. BLOOM also used enough energy in just its training over a number of months to power the average American home for just over four decades and the training run also had 25 times the emissions of just one passenger's round-trip flight from New York to San Francisco.

Zoe: So if you're feeling guilty about that trans-Atlantic flight you took recently, here's some context for it. What about water use? Data centers typically use a huge amount of water to cool themselves. What is the water use like for AI?

Nuha: So research out of the University of California, Riverside has shown that about ChatGPT-3 needs to drink, like, a 500 milliliter bottle of water for just a basic conversation of between 20 to 50 inquiries, depending on where that electricity is generated. GPT-4 probably uses more. The research out of the university also did estimates for Google's large language model known as LaMDA and that one used around a million liters of water for its training alone. Google's on-site data center water consumption overall in 2022 has also gone up by around 20% compared with the year before.

Zoe: Have the companies said anything about ways that they might reduce that?

Nuha: Google has said that when they try to use water they try to use things that aren't freshwater, so things like wastewater, industrial, or even seawater. Google has a 2030 target to replenish 120% of the water it consumes.

Zoe: Are there ways to reduce AI's climate impact, things that companies or users might be able to do?

Nuha: One thing people can do, of course is using less, but a practical step in limiting those emissions is just to not integrate AI into things that it doesn't need to be in. Our search engines work now, we have lots of software that works just fine, and as companies try to integrate AI into it they're increasing the climate cost of all those basic actions that everyone does and has done just fine before and can continue to do just fine without that integration.

Zoe: What about in terms of location? For example, should we be thinking about where these AI models are housed?

Nuha: In the US, for instance, where there's no central electric grid, training models in one state versus another can have a pretty big impact on carbon emissions and you don't actually have to move to do that 'cause all this is done over the internet. In California, where we have a lot of wind power, there's a good shot that the energy's producing less emissions than if you're using it in Virginia which has lots of coal and other fossil fuels. Internationally, because it's typically warmer in Asia, Microsoft said last year that its Asian data center's actual water use effectiveness was three times worse than that of their locations in the US, so if you're training an AI model you could triple your water use just by having that be in Asia.

Zoe: All right, that was Nuha Dolby who reported on this for WSJ Pro Sustainability. And that's it for "Tech News Briefing." Today's show was produced by Anthony Bansie with supervising producer Melony Roy. I'm Zoe Thomas for The Wall Street Journal. We'll be back tomorrow. Thanks for listening.

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