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

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?”

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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

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

Jennifer Listgarten

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

Jennifer Listgarten

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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 (2024). https://doi.org/10.1038/s41587-023-02103-0

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Published: 25 January 2024

DOI: https://doi.org/10.1038/s41587-023-02103-0

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