GoKawiilWith the arrival of ‘AI scientists’, it’s as well to remember that human wisdom, empathy and sheer messiness are as much part of progress as are process and efficiency.
Training and mentoring the next generation of researchers is an important role for humans.Credit: Getty
Does humanity need science? The Nobel prizewinner Max Perutz posed this question in a landmark essay1 in 1989. His conclusion, unsurprisingly, was ‘yes’. Had he lived to see the era of artificial intelligence, he might have inverted his framing of the question: does science need humanity?
Read the paper: Accelerating scientific discovery with Co-Scientist
Two studies in Nature provide a glimpse of what some are interpreting as humanity’s shrinking role in scientific discovery in Perutz’s own field, molecular biology. Both describe a pivotal step towards truly AI-driven drug discovery, in which a system of connected AI agents is trained to autonomously navigate multi-step workflows. The system trawled scholarly literature, formed hypotheses, interpreted data and engaged in internal debate to arrive at candidate drugs to treat a particular disease.
The results are impressive, but also highlight something else: AI scientists can and should empower human researchers. They cannot and should not replace them.
Read the paper: A multi-agent system for automating scientific discovery
In one study, a team based at FutureHouse, a non-profit AI research laboratory in San Francisco, California, asked its AI system, called Robin, to find a treatment for the eye disorder dry age-related macular degeneration2. Robin’s agents searched the scientific literature to derive a therapeutic strategy, identified candidate molecules and selected assays to test them. The experiments Robin suggested were then handed over to humans, who conducted the studies and fed the results back to Robin for analysis, interpretation and the design of follow-up studies. The team estimates that Robin reduced the time needed for the project 200-fold compared with a typical human workflow.
A second group led by researchers at Google, based in Mountain View, California, used its AI-agent system, called Co-Scientist, to look for approved drugs that could be repurposed to treat a form of leukaemia, and to discover drug targets to treat liver fibrosis3. Humans provided input throughout the process, helping to prioritize hypotheses and approaches. The team also asked the agents to develop a hypothesis to explain why many species of bacterium share a particular suite of antibiotic-resistance genes. Scientists, including some of the project’s authors, had been investigating this microbial puzzle for around a decade, but had not yet published the results. Co-Scientist arrived at the same hypothesis as the researchers — within days.
Teams of AI agents boost speed of research
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