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Researchers built an ‘AI Scientist’ — what can it do?
Credit: Moor Studio/Getty
Could science be fully automated? A team of machine-learning researchers has now tried to make this possible.
‘AI Scientist’, created by a team at the Tokyo-based company Sakana AI and in laboratories in Canada and the United Kingdom, can perform the full cycle of research, from scanning the literature on a topic and formulating hypotheses to trying out solutions and writing a paper. AI Scientist even does some of the job of peer reviewers and evaluates its own results.
AI Scientist joins a slew of efforts to automate parts of the scientific process using artificial intelligence (AI) agents. “To my knowledge, no one has yet done the total scientific community, all in one system,” says AI Scientist co-creator Cong Lu, a machine-learning researcher at the University of British Columbia in Vancouver, Canada. The results1 were posted on the arXiv preprint server last month.
“It’s impressive that they’ve done this end-to-end,” says Jevin West, a computational social scientist at the University of Washington in Seattle. “And I think we should be playing around with these ideas, because there could be potential for helping science.”
The output has not been earth-shattering so far, and the system can do research only in the field of machine learning itself. In particular, AI Scientist is lacking what most scientists would consider a crucial part of doing science — the ability to do lab work. “There’s still a lot of work to go from AI that makes a hypothesis to implementing that in a robot scientist,” says Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Laboratory in California. Still, Ceder adds, “if you look into the future, I have zero doubt in mind that this is where much of science will go”.
Automated experiments
AI Scientist is based on a large language model (LLM). It uses a machine-learning algorithm to search the literature for similar work. The team then used evolutionary computation, a technique inspired by the mutations and natural selection in Darwinian evolution. It applies small, random changes to an algorithm and selects the ones that improve the model’s efficiency.
To do so, AI Scientist conducts its own ‘experiments’ by running the algorithms and measuring how well they perform. At the end, it produces a paper, and evaluates it in a sort of automated peer review. After ‘augmenting the literature’ this way, the algorithm can then start the cycle again, building on its own results.
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Nature 633, 266 (2024)
doi: https://doi.org/10.1038/d41586-024-02842-3
References
Lu, C. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2408.06292 (2024).
Ghahramani, Z. Nature 521, 452–459 (2015).
Szymanski, N. J. et al. Nature 624, 86–91 (2023).
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