Assessing Carbon Footprint Estimations of ChatGPT

Author:

d’Aramon Ithier,Ruf Boris,Detyniecki Marcin

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Radford, A., & Narasimhan, K. (2018). Improving language understanding by generative pre-training. https://api.semanticscholar.org/CorpusID:49313245.

2. UBS. Has the AI rally gone too far?. Accessed on 21-06-2023. https://www.ubs.com/global/en/wealth-management/insights/chief-investment-office/house-view/daily/2023/latest-25052023.html.

3. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., et al. (2021). Carbon emissions and large neural network training.

4. Stollberg, J., & Jonas, E. (2021). Existential threat as a challenge for individual and collective engagement: Climate change and the motivation to act. Current Opinion in Psychology, 42, 145–150. Psychology of Climate Change.

5. Masson, T., & Fritsche, I. (2021). We need climate change mitigation and climate change mitigation needs the ‘We’: A state-of-the-art review of social identity effects motivating climate change action. Current Opinion in Behavioral Sciences, 42, 89–96. Human Response to Climate Change: From Neurons to Collective Action

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