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What ChatGPT and generative AI mean for science

Researchers are excited but apprehensive about the latest advances in artificial intelligence.
  1. Chris Stokel-Walker

    1. Chris Stokel-Walker is a freelance journalist in Newcastle, UK.

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  2. Richard Van Noorden
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Conceptual illustration showing the chatbot as a magical genie emerging from a computer.

Illustration by Paweł Jońca

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

  1. Pividori, M. & Greene, C. S. Preprint at bioRxiv https://doi.org/10.1101/2023.01.21.525030 (2023).

  2. GPT, Osmanovic Thunström, A. & Steingrimsson, S. Preprint at HAL https://hal.science/hal-03701250 (2022).

  3. Nature Mach. Intell. 5, 1 (2023).

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  6. Kirchenbauer, J. et al. Preprint at https://arxiv.org/abs/2301.10226 (2023).

  7. Singhal, K. et al. Preprint at https://arxiv.org/abs/2212.13138 (2022).

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