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.
This assistant, as Greene and Pividori reported in a preprint1 on 23 January, is not a person but an artificial-intelligence (AI) algorithm called GPT-3, first released in 2020. It is one of the much-hyped generative AI chatbot-style tools that can churn out convincingly fluent text, whether asked to produce prose, poetry, computer code or — as in the scientists’ case — to edit research papers (see ‘How an AI chatbot edits a manuscript’ at the end of this article).
