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Is AI truly a beacon of hope in tackling climate change, or is it an overhyped solution?
Stay Informed and Engaged
For those interested in exploring this topic further, we encourage you to sign up to our free newsletter. AI's intersection with climate change is a dynamic field, offering both challenges and opportunities. By staying informed and engaged, we can better navigate this complex landscape towards a more sustainable and hopeful future.
Sources & References:
The research papers mentioned in this episode.
1. Maas, O., Boulanger, J-P., & Thiria, S. (2000). Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon. Neurocomputing, 30(1-4), 53-58. https://doi.org/10.1016/S0925-2312(99)00142-3
2. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy And Policy Considerations for Deep Learning in NLP. https://doi.org/10.48550/arXiv.1906.02243
3. Rolnick, D., et al. (2022). Tackling Climate Change With Machine Learning. ACM Computing Surveys, 55(2), Article 42. https://dl.acm.org/doi/pdf/10.1145/3485128
4. AI4Climate Action -AI for Climate Action: Technology Mechanism supports transformational climate solutions https://unfccc.int/news/ai-for-climate-action-technology-mechanism-supports-transformational-climate-solutions
By Jane Arandelovic - Founder of Laying Waste MediaIs AI truly a beacon of hope in tackling climate change, or is it an overhyped solution?
Stay Informed and Engaged
For those interested in exploring this topic further, we encourage you to sign up to our free newsletter. AI's intersection with climate change is a dynamic field, offering both challenges and opportunities. By staying informed and engaged, we can better navigate this complex landscape towards a more sustainable and hopeful future.
Sources & References:
The research papers mentioned in this episode.
1. Maas, O., Boulanger, J-P., & Thiria, S. (2000). Use of neural networks for predictions using time series: Illustration with the El Niño Southern oscillation phenomenon. Neurocomputing, 30(1-4), 53-58. https://doi.org/10.1016/S0925-2312(99)00142-3
2. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy And Policy Considerations for Deep Learning in NLP. https://doi.org/10.48550/arXiv.1906.02243
3. Rolnick, D., et al. (2022). Tackling Climate Change With Machine Learning. ACM Computing Surveys, 55(2), Article 42. https://dl.acm.org/doi/pdf/10.1145/3485128
4. AI4Climate Action -AI for Climate Action: Technology Mechanism supports transformational climate solutions https://unfccc.int/news/ai-for-climate-action-technology-mechanism-supports-transformational-climate-solutions