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How Language-Generation AIs Could Transform Science


Shobita Parthasarathy, University of Michigan in Ann Arbor.

"If anyone can use LLMs to make complex research comprehensible, but they risk getting a simplified, idealized view of science that is at odds with the messy reality, that could threaten professionalism and authority." -Shobita Parthasarathy

Credit: Nature

Shobita Parthasarathy is a specialist in the governance of emerging technologies at the University of Michigan in Ann Arbor.

Machine-learning algorithms that generate fluent language from vast amounts of text could change how science is done—but not necessarily for the better, says Parthasarathy in an interview. She discusses a recent report titled, "What's in the Chatterbox," in which she and other researchers try to anticipate societal impacts of large language models (LLMs).

"I had originally thought that LLMs could have democratizing and empowering impacts," Parthasarathy said. "They could empower people to quickly pull insights out of information. But the algorithmic summaries could make errors, include outdated information, or remove nuance and uncertainty, without users appreciating this."

From Nature
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