“It helps us make more informed predictions about changes in the environment, so that we can deploy mitigation efforts earlier.” [John Hopkins University]
But: “AI uses a lot of energy.”
DeepMind, the Google-owned artificial intelligence lab, has recently declared that it can “apply the latest AI developments to help fight climate change and build a more sustainable, low-carbon world”.
Their climate & sustainability lead, Sims Witherspoon, spoke at TED Countdown about how AI can accelerate our transition to renewables, explaining, “Climate change is a multifaceted problem with no single solution. We need to move beyond discussing what we can do and start focusing on how we can do it.”
The tech site WIRED spoke with her this week about techno-utopianism, ways AI can help fight climate change, and what’s currently standing in the way:
You’ve said you’re a techno-optimist, so what’s the techno-optimist view of a future where AI is fully brought to bear on climate change?
The techno-optimist’s view is that—provided we’re able to wield it effectively—we’re able to use a transformative tool like AI to solve sector-specific and non-sector-specific problems more quickly, and at a scale we wouldn’t be able to without AI. One of the things I’m most excited about is the versatility and scalability of the tool. And given the amount of problems we need to solve related to climate change, what we need is a highly versatile and highly scalable tool.
There are other such projects underway, including an AI model by NASA and IBM which can help fight climate change:
An artificial intelligence-powered geospatial model for Earth observations like tracking deforestation, predicting crop yields, and monitoring greenhouse gasses. It’s an open-source model which will be available on Hugging Face… “AI remains a science-driven field, and science can only progress through information sharing and collaboration,” said Jeff Boudier, head of product and growth at Hugging Face, in a press release. “This is why open-source AI and the open release of models and datasets are so fundamental to the continued progress of AI and making sure the technology will benefit as many people as possible.”
Academics at John Hopkins University explain how AI can be so useful in tracking and mitigating the effects of climate change
“Climate data sets are enormous and take significant time to collect, analyze, and use to make informed decisions and enact actual policy change,” says Jim Bellingham, a pioneer in autonomous underwater robotics systems and executive director of the Johns Hopkins Institute for Assured Autonomy. “Using AI to factor in elements of climate change that are constantly evolving helps us make more informed predictions about changes in the environment, so that we can deploy mitigation efforts earlier.”
But there are problems with AI – and the biggest elephant in the room is the amount of energy needed to power the servers. In particular, its own carbon footprint could be a serious issue. And whilst AI can help us fight climate change, it does have an energy problem:
‘AI uses a lot of energy,’ said the computer scientist Prof. Dignum, who is part of a 52-person expert group advising the European Commission on trustworthy and ‘human-centric’ AI. The storage, and particularly the processing, of data to train algorithms – the ‘recipes’ computers use to make calculations – in data centres or in the cloud across different centres with rows of machines doing computations consume energy, she says. For one algorithm to train itself on whether an image shows a cat, for instance, it needs to process millions of cat images. The ecosystem for information and communications technology, of which data centres are a part, are comparable to aviation in terms of fuel emissions.
‘It’s a use of energy that we don’t really think about,’ said Prof. Dignum. ‘We have data farms, especially in the northern countries of Europe and in Canada, which are huge. Some of those things use as much energy as a small city.’ She draws on a University of Massachusetts, US, study which found that training a large AI model to handle human language can lead to emissions of nearly 300,000 kilograms of carbon dioxide equivalent, about five times the emissions of the average car in the US, including its manufacture. Swedish researcher Anders Andrae has forecast that data centres could account for 10% of total electricity use by 2025.