A new Centre at the University of Cambridge will develop artificial intelligence techniques to help address some of the biggest threats facing the planet.
Funded by UK Research and Innovation (UKRI), the Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) is one of 16 new Centres for Doctoral Training (CDTs) announced today. The Cambridge Centre will be led by Professor Simon Redfern, Head of the Department of Earth Sciences.
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Climate risk, environmental change and environmental hazards pose some of the most significant threats we face in the 21st century. At the same time, we have increasingly larger datasets available to observe the planet, from the atomic scale all the way through to global satellite observations.
“These datasets represent a transformation in the way we can study and understand the Earth and environment, as we assess and find solutions to environmental risk,” said Redfern. “Such huge datasets pose their own challenges, however, and new methods need to be developed to tap their potential and to use this information to guide our path away from environmental catastrophe.”
The new Centre brings computer scientists, mathematicians and engineers together with environmental and geoscientists to train the next generation of thought leaders in environmental data science. They will be equipped to apply AI to ever-increasing environmental data and understand and address the risks we face.
At the same time as human-induced climate change becomes increasingly apparent, urbanisation and the growth of megacities generate other risks, as society becomes potentially more fragile and vulnerable to geohazards such as earthquakes, volcanic eruptions, floods and tsunamis. Alongside satellite data, autonomous sensors, drones, and networks of instruments provide increasingly detailed information about such risks and their potential impacts.
Examples of the projects we are already engaged in that apply AI methods to exploring environmental risk include the use of satellite observations to chart the distribution and pathways of whales through the oceans, large datasets to understand biodiversity changes in woodland habitats, machine learning to understand earthquake risk and the use of drones to monitor hazards at active volcanos.
Cambridge is a world leader in artificial intelligence and machine learning research, and many of our AI researchers work alongside world leaders in environmental monitoring and modelling, including from the British Antarctic Survey and elsewhere at the University.
The new centre combines this work with the interests of dozens of external partners including Microsoft, DeepMind, The European Development Bank, Friends of the Earth, the European Space Agency, the Environment Agency, resource industry leaders and policy partners, to form an outstanding alliance focused on leading the next generation of environmental data science forward.
The first cohort of PhD students will start their studies in October 2019.
The new Centre is part of an overall £200 million funding announcement, which will support more than 1000 new research and business leaders in AI across the UK.
“Artificial intelligence has great potential to drive up productivity and enhance every industry throughout our economy, from more effective disease diagnosis to building smart homes,” said Business Secretary Greg Clark. “Today’s announcement is our modern Industrial Strategy in action, investing in skills and talent to drive high skilled jobs, growth and productivity across the UK.”
“The UK is not only the birthplace to the father of artificial intelligence, Alan Turing, but we are leading the way on work to ensure AI innovation has ethics at its core,” said Digital Secretary Jeremy Wright. “We want to keep up this momentum and cement our reputation as pioneers in AI. Working with world-class academic institutions and industry we will be able to train the next generation of top-tier AI talent and maintain the UK’s reputation as a trailblazer in emerging technologies.”
Republished from University Of Cambridge under Creative Commons Attribution 4.0 International License.
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