Using a system called BootLoops, scientists produced 36 papers in three months. Paired with experts, the tool could pollinate ideas across fields.

Some major scientific advances involve taking a technique developed in one field and applying it to another, but interdisciplinary research is tricky and time-consuming. Now a new AI research system has automated the process, generating 36 papers in three months.

Most researchers work within a narrow specialty, and few have time to comb through unrelated fields for tools that might help them. As a result, researchers can end up banging their heads against a problem scientists in another discipline have already solved.

But Harvard physicist Matthew Schwartz stumbled across a potential solution while working as a visiting researcher at AI firm Anthropic. After getting the company’s AI, Claude, to develop a suite of tools for mathematical physics, Schwartz discovered they could help solve quantitative problems in a host of other areas.

Called BootLoops, the tools, which are model agnostic, help AI search scientific literature, write code, and solve mathematical problems. Working with 19 coauthors, Schwartz used BootLoops to produce 36 manuscripts on topics ranging from ecology and linguistics to health insurance and sunspots.

“Once Claude had BootLoops, it kept noticing the same pattern: many fields have problems that a technique from mathematics, physics, or computer science would solve outright if anyone knew it existed,” Schwartz wrote in an Anthropic blog post about the system.

Schwartz first started using Claude as a general-purpose research assistant last December, and while he was able to do some useful work with it, he says it required constant handholding.

Source link