ABOUT THIS EPISODE
The potential impact of Artificial Intelligence on our worsening climate crisis is certainly a hot topic, pardon the pun.
Fortunately, we have an expert on today’s show to help us make sense of it all. Iuna Tsyrulneva is one of the leading voices in AI and sustainability. Well versed on both topics, Iuna holds a PhD in materials science, and works at Earth Observatory of Singapore with an interdisciplinary approach. She’s full of interesting insights, and brings a dynamic approach to thinking about the larger issue.
For example, did you know that saying “thank you” after every prompt actually contributes to AI energy usage? And they say kindness costs nothing…
As you’ll hear from Iuna, optimisation is the key to affecting sustainability in Artificial Intelligence. And that goes beyond ‘clean code’. Throughout our conversation, Iuna unpacks the end-to-end scope of optimisation, or what she calls her ‘four pillars’ of sustainable AI, that being algorithmic, hardware, data center and usage optimisation.
In this episode, Gaël and Iuna also discuss:
- Sustainable AI vs AI for Sustainability
- How (if at all) can we mitigate Jevons paradox?
- Tackling the financial incentive vs sustainability in AI
- Why she’s optimistic about the future of sustainable AI
- Using more energy efficient languages to build next-gen AI
- Leveraging open-source frameworks to lower resource costs
- Practical tips on how to make AI models more energy efficient
And much more!
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📧 Each month our readers get carefully curated news on digital sustainability, packed with exclusive Green IO content. Subscribe to the Green IO newsletter: https://newsletter.greenio.tech
📣 Green IO next Conference is in Paris on December 9 - 11. Every Green IO listener can get a free ticket using the voucher GREENIOVIP. A small gift for your huge support. 🎁
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- Research: https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
- Research: https://institute.global/insights/climate-and-energy/responsible-progress-sustainable-ai-the-benefits-of-greening-our-digital
- https://www.iea.org/reports/energy-and-ai/ai-and-climate-change
- Greening AI: A Policy Agenda for the Artificial Intelligence and Energy Revolutions. Tony Blair Institute (2024) https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
- Bashir, Noman, Priya Donti, James Cuff, Sydney Sroka, Marija Ilic, Vivienne Sze, Christina Delimitrou, and Elsa Olivetti. 2024. “The Climate and Sustainability Implications of Generative AI.” An MIT Exploration of Generative AI, March. https://doi.org/10.21428/e4baedd9.9070dfe7.
- Computational efficiency of AI models: https://openai.com/index/ai-and-compute/
- Computational efficiency of AI models: arXiv:2104.10350 [cs.LG]. https://doi.org/10.48550/arXiv.2104.10350
- Techniques and approaches for developing Sustainable AI: https://institute.global/insights/climate-and-energy/greening-ai-a-policy-agenda-for-the-artificial-intelligence-and-energy-revolutions
- Trade between accuracy and efficiency of AI models: Dhar, P. The carbon impact of artificial intelligence. Nat Mach Intell 2, 423–425 (2020). https://doi.org/10.1038/s42256-020-0219-9
- AI for Sustainability: Vinuesa, R., Azizpour, H., Leite, I. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat Commun 11, 233 (2020). https://doi.org/10.1038/s41467-019-14108-y
- Examples of AI for Sustainability: https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/; https://www.iea.org/reports/energy-and-ai/ai-and-climate-change
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