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The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’

Nature Biotechnology volume 42, pages 371–373 (2024)Cite this article

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As a longtime researcher at the intersection of artificial intelligence (AI) and biology, for the past year I have been asked questions about the application of large language models and, more generally, AI in science. For example: “Since ChatGPT works so well, are we on the cusp of solving science with large language models?” or “Isn’t AlphaFold2 suggestive that the potential of AI in biology and science is limitless?” And inevitably: “Can we use AI itself to bridge the lack of data in the sciences in order to then train another AI?”

I do believe that AI—equivalently, machine learning—will continue to advance scientific progress at a rate not achievable without it. I don’t think major open scientific questions in general are about to go through phase transitions of progress with machine learning alone. The raw ingredients and outputs of science are not found in abundance on the internet, yet the tremendous power of machine learning lies in data—and lots of them.

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Acknowledgements

Thanks to Tyler Bonnen, James Bowden, Jennifer Doudna, Lisa Dunlap, Alyosha Efros, Nicolo Fusi, Aaron Hertzmann, Hanlun Jiang, Aditi Krishnapriyan, Jitendra Malik, Sara Mostafavi, Hunter Nisonoff and Ben Recht for helpful comments on this piece as it was taking shape.

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Authors and Affiliations

  1. Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA

    Jennifer Listgarten

  2. Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA

    Jennifer Listgarten

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  1. Jennifer Listgarten
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Correspondence to Jennifer Listgarten.

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Listgarten, J. The perpetual motion machine of AI-generated data and the distraction of ChatGPT as a ‘scientist’. Nat Biotechnol 42, 371–373 (2024). https://doi.org/10.1038/s41587-023-02103-0

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