References
[1]. Lahat, A., Shachar, E., Avidan, B. et al. (2023), Evaluating the use of large language model in identifying top research questions in gastroenterology. Sci Rep 13, 41642.
[2]. Kurian, N., Cherian, J., Sudharson, N. et al. (2023), AI is now everywhere. Br Dent J 234, 72 3.
[3]. Merow, C., Serra-Diaz, J.M., Enquist, B.J. et al. AI chatbots can boost scientific coding. Nat Ecol Evol (2023). https://doi.org/10.1038/s41559-023-02063-3
[4]. Krügel, S., Ostermaier, A. & Uhl, M. (2023), ChatGPT’s inconsistent moral advice influences users’ judgment. Sci Rep 13, 45694.
[5]. Nature Medicine, (2023), Will ChatGPT transform healthcare? https://www.nature.com/articles/s41591-023-02289-5
[6]. Shickel, B., Loftus, T.J., Ruppert, M. et al. (2023), Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks. Sci Rep 13, 1224 6.
[7]. Lee, C., Song, G., Kim, H. et al. (2023), Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat Mach Intell 5, 35–45
[8]. Spahn, C., Gómez-de-Mariscal, E., Laine, R.F. et al. (2022), DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches. Commun Biol 5, 688
[9]. Punjani, A., Zhang, H. & Fleet, D.J. (2020), Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat Methods 17, 1214–1221
[10]. Lee, C., Song, G., Kim, H. et al. (2023), Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat Mach Intell 5, 35–45
Cite this article
Tan,S. (2023). Exploring challenges and approaches for ChatGPT in multilingual and multisectoral contexts. Applied and Computational Engineering,19,165-169.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Lahat, A., Shachar, E., Avidan, B. et al. (2023), Evaluating the use of large language model in identifying top research questions in gastroenterology. Sci Rep 13, 41642.
[2]. Kurian, N., Cherian, J., Sudharson, N. et al. (2023), AI is now everywhere. Br Dent J 234, 72 3.
[3]. Merow, C., Serra-Diaz, J.M., Enquist, B.J. et al. AI chatbots can boost scientific coding. Nat Ecol Evol (2023). https://doi.org/10.1038/s41559-023-02063-3
[4]. Krügel, S., Ostermaier, A. & Uhl, M. (2023), ChatGPT’s inconsistent moral advice influences users’ judgment. Sci Rep 13, 45694.
[5]. Nature Medicine, (2023), Will ChatGPT transform healthcare? https://www.nature.com/articles/s41591-023-02289-5
[6]. Shickel, B., Loftus, T.J., Ruppert, M. et al. (2023), Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks. Sci Rep 13, 1224 6.
[7]. Lee, C., Song, G., Kim, H. et al. (2023), Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat Mach Intell 5, 35–45
[8]. Spahn, C., Gómez-de-Mariscal, E., Laine, R.F. et al. (2022), DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches. Commun Biol 5, 688
[9]. Punjani, A., Zhang, H. & Fleet, D.J. (2020), Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat Methods 17, 1214–1221
[10]. Lee, C., Song, G., Kim, H. et al. (2023), Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat Mach Intell 5, 35–45