ΑΙhub.org
 

AI and climate change – a virtual briefing with Climate Change AI researchers


by
03 February 2021



share this:
earth

In December, Heinrich-Böll-Stiftung hosted a virtual briefing featuring researchers from Climate Change AI (CCAI). They talked about the role machine learning can play in facilitating climate change mitigation and adaptation strategies, AI applications that increase emissions, and energy use in AI itself.

AIhub focus issue on climate action

On the topic of facilitating climate change mitigation and adaptation strategies, a number of examples were given where AI could help. These include: gathering information, forecasting, improving operational efficiencies, predictive maintenance, accelerating scientific experimentation, and approximating time-intensive simulations.

The researchers then talked about AI applications that increase emissions. There are two aspects to this. Firstly, the use of AI in applications that directly increase emissions, such as use in the oil, gas and mining industries. Secondly, AI applications with uncertain impact. For example, AI is a key component in creating new technologies, like autonomous vehicles. In technologies such as these there is no clear understanding of whether their implementation would have a positive or negative effect on the climate.

The team discussed the energy consumption of AI systems. They touched on the need to understand the power consumption for applications during both the training and usage phases.

Finally, we heard about the role of policy implementation with regards to aligning the use of AI with climate change strategies.

You can watch the briefing in full here:

Taking part in the session were:

  • Priya L. Donti, Carnegie Mellon University, and Co-founder and Chair, Climate Change AI
  • Lynn H. Kaack, ETH Zürich, and Co-founder and Chair, Climate Change AI
  • David Rolnick, McGill University and Mila, and Co-founder and Chair, Climate Change AI

CCAI is a group of volunteers from academia and industry who believe that tackling climate change requires concerted societal action, in which machine learning can play an impactful role.

Read the paper Artificial Intelligence and Climate Change: Opportunities, considerations, and policy levers to align AI with climate change goals by Lynn H. Kaack, Priya Donti, Emma Strubell and David Rolnick.



tags: ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.

Images of AI – between fiction and function

“The currently pervasive images of AI make us look somewhere, at the cost of somewhere else.”

Grace Wahba awarded the 2025 International Prize in Statistics

  16 Apr 2025
Her contributions laid the foundation for modern statistical techniques that power machine learning algorithms such as gradient boosting and neural networks.

Repurposing protein folding models for generation with latent diffusion

  14 Apr 2025
The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding?

AI UK 2025 conference recordings now available to watch

  11 Apr 2025
Listen to the talks from this year's AI UK conference.

#AAAI2025 workshops round-up 2: Open-source AI for mainstream use, and federated learning for unbounded and intelligent decentralization

  10 Apr 2025
We hear from the organisers of two workshops at AAAI2025 and find out the key takeaways from their events.

Accelerating drug development with AI

  09 Apr 2025
Waterloo researchers use machine learning to predict how new drugs could affect the body




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association