October 14, 2019

Climate intelligence: a clean way forward

A boat in the ocean

TL:DR;

  • The gap between technology and science is bridged by machine learning and artificial intelligence, guiding decision making when there is no time to lose.

A decade on from the financial crisis that saw stock markets crash, oil prices collapse, and clean technology venture funding dwindle, we’re seeing signs that “cleantech” and sustainability-focused technologies are back in focus — with the effects of climate change ever more apparent and increasingly urgent. As noted in a recent podcast by The Interchange from Greentech Media, this renewed interest comes from a variety of players ranging from startup entrepreneurs to established companies and new venture funds.

While climate change is portrayed as a divisive political issue, the scientific evidence is hard to ignore and extends across a range of quantifiable metrics including: the number of extreme weather events, rate of sea level rise, extent of coral reef damage linked to ocean acidification, and warming of ocean temperatures. Climate change is the reality that individuals and companies are navigating and have to account for.

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The complexity of the climate crisis is often exacerbated by the sheer volume of data, which makes manual analysis intractable. Machine learning and artificial intelligence (AI) have made waves in many software technologies because of their ability to analyze and draw inferences from large amounts of granular data. Researchers, entrepreneurs, and businesses have responded to the immediacy of climate change by combining domain expertise, various data sources, and machine learning and AI to not only better understand the science behind climate change, but also innovate across a variety of sectors including energy, food and agriculture, transportation, and infrastructure.

“Machine learning and artificial intelligence have made waves in many software technologies because of their ability to analyze and draw inferences from large amounts of granular data.”

Algorithms, such as convolutional neural networks originally designed for classifying images, allow researchers and companies to analyze high resolution satellite imagery provided by government agencies and private companies. This spatio-temporal information can provide insights on poverty, vegetation and land cover changes, and even infrastructure quality. When monitored over time, the sequences of these satellite images and the algorithmically extracted insights paint a picture of how climate change has impacted society.

In the energy sector, several startups have gained attention and investments from corporate venture funds like Shell Ventures and National Grid Partners, as well as seed accelerators like Y Combinator and even the billionaire-backed Breakthrough Energy Ventures (BEV):

  • Autogrid – Machine learning and analytics to help utilities, electricity retailers, and renewable energy developers flexibly extract capacity from distributed energy resources (Shell Ventures, National Grid Partners)
  • Traverse Technologies – AI-driven prospecting for optimal hydropower and wind generation sites (Y Combinator)
  • KoBold Metals – Computer vision and machine learning-enabled digital prospecting to search for likely sources of cobalt (BEV, Andreessen Horowitz)

Technology companies have also applied their AI research to help address climate change — first internally and increasingly for external use. Google DeepMind, known for defeating a Go world champion with their program AlphaGo, first applied their technology to reduce Google’s data center cooling energy usage in 2016. The team was able to reduce energy usage by 40% through applying machine learning programs to help improve operational efficiency. Additionally, DeepMind has applied deep learning to improve renewable energy generation by forecasting wind energy generation in order to provide more reliable estimates of when power would actually be generated. This helped boost the value of the wind energy generation by 20% and inform the hourly commitments that were promised to the power grid.

Meanwhile, Microsoft has applied machine learning research to its services and calculated, in a recent study, that its cloud services are up to 93% more energy efficient and up to 98% more carbon efficient than traditional enterprise data centers. Externally, Microsoft has pledged $50 million over 5 years to support organizations and researchers looking to tackle environmental challenges through their AI for Earth program.

While deep learning has helped startups and technology companies tackle climate problems, it needs to be balanced with a critical look at the resources used in model training. Researchers at the University of Massachusetts, Amherst have recently found that training large AI systems, given the existing mix of energy sources, emits large amounts of carbon dioxide. These results illustrate the importance of relying on renewable energy sources to fuel the development of these machine learning technologies.

“In many cases, the combination of better technology in improved algorithms and better data have allowed businesses to not only reduce their impact on the environment, but also operate more efficiently.”

As climate change’s effects continue to necessitate immediate action, companies have started to turn to machine learning and AI to help navigate vast amounts of complex data to help improve decision making. In many cases, the combination of better technology in improved algorithms and better data have allowed businesses to not only reduce their impact on the environment but also operate more efficiently. While the magnitude of climate change requires a variety of technologies and solutions, machine learning and AI are powerful new tools in the fight against climate change. The confluence of these technologies with the explosion of large quantities of accessible data presents a critical new opportunity for humanity to pave a more sustainable path forward.

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