Aiming beyond slight increases in accuracy

Ivakhnenko, A. G. The group method of data handling, a rival of the method of stochastic approximation. Sov. Automat. Contr. 13, 43–55 (1968).

Google Scholar 

Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121, 9816–9872 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Probst, D. et al. Biocatalysed synthesis planning using data-driven learning. Nat. Commun. 13, 964 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Thompson, N. C., Greenewald, K., Lee, K. & Manso, G. F. Deep learning’s diminishing returns. IEEE Spectr. 58, 50–55 (2021).

Article  Google Scholar 

Ahmed, N. & Wahed, M. The de-democratization of AI: deep Learning and the compute divide in artificial intelligence research. Preprint at https://arxiv.org/abs/2010.15581 (2020).

Jurowetzki, R., Hain, D., Mateos-Garcia, J. & Stathoulopoulos, K. The privatization of AI research(-ers): causes and potential consequences–from university-industry interaction to public research brain-drain? Preprint at https://arxiv.org/abs/2102.01648 (2021).

Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. Commun. ACM 63, 54–63 (2020).

Article  Google Scholar 

Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. Preprint at https://arxiv.org/abs/2204.05149 (2022).

Probst, D. Social and environmental impact of recent developments in machine learning on biology and chemistry research. Preprint at https://arxiv.org/abs/2210.00356 (2022).

Scao, T. L. et al. BLOOM: a 176B-parameter open-access multilingual language model. Preprint at https://arxiv.org/abs/2211.05100 (2022).

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