What ChatGPT and generative AI mean for science
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A Nature news feature examining practical applications and risks of generative AI in scientific research, including concerns about AI-generated text detection, publishing transparency, and governance—relevant for understanding AI safety implications in knowledge production systems.
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This Nature news feature explores the emerging applications and implications of generative AI tools like ChatGPT for scientific research and publishing. The article highlights a case study where computational biologists used ChatGPT to improve manuscript readability in minutes at minimal cost, while also discussing broader concerns about AI-generated text detection, transparency in scientific publishing, and the need for clear guidelines governing AI use in research. The piece examines both the practical benefits and potential risks these tools present to the scientific community.
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| Page | Type | Quality |
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| AI Development Racing Dynamics | Risk | 72.0 |
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What ChatGPT and generative AI mean for science
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In December, computational biologists Casey Greene and Milton Pividori embarked on an unusual experiment: they asked an assistant who was not a scientist to help them improve three of their research papers. Their assiduous aide suggested revisions to sections of documents in seconds; each manuscript took about five minutes to review. In one biology manuscript, their helper even spotted a mistake in a reference to an equation. The trial didn’t always run smoothly, but the final manuscripts were easier to read — and the fees were modest, at less than US$0.50 per document.
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Nature 614 , 214-216 (2023)
doi: https://doi.org/10.1038/d41586-023-00340-6
Updates & Corrections
Correction 08 February 2023 : This News feature misrepresented Scott Aaronson’s views on the accuracy of watermarking in identifying AI-produced text. Human-produced text might also be flagged as having a watermark, but the probability is extremely low.
References
Pividori, M. & Greene, C. S. Preprint at bioRxiv https://doi.org/10.1101/2023.01.21.525030 (2023).
GPT, Osmanovic Thunström, A. & Steingrimsson, S. Preprint at HAL https://hal.science/hal-03701250 (2022).
Nature Mach. Intell. 5 , 1 (2023).
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Glaese, A. et al. Preprint at https://arx
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