Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery

One of AI’s greatest potential benefits to humanity is increasing the speed and scope of scientific discovery. Empirical Research Assistance (ERA), a Google-developed research tool that uses Gemini to write and optimize scientific code, addresses one of the most time-consuming parts of scientific research: iteratively testing and refining computational experiments. It is described in "AI system designed to help scientists write expert-level empirical software”, published today in the journal Nature.

As part of our wider science announcements at I/O today, we are also making this technology accessible as a tool that can begin to help scientists around the world. ERA is one of the systems used to build Computational Discovery, a new experimental tool that is starting to roll out more broadly today through Gemini for Science.

Introducing ERA as a versatile tool for scientific coding

We first shared the design and performance of ERA in the fall, when the preprint was released. Given a scientific problem and a measure of success, ERA can search scientific literature, write code, explore solutions, combine techniques and evaluate the results. ERA considers thousands of options, using a tree search approach to optimize its output code against its given goal.

Our Nature publication describes testing ERA on benchmark problems spanning a variety of disciplines: genomics, public health, satellite imagery analysis, neuroscience prediction, a general time-series forecasting benchmark, and mathematics. Results show ERA achieves expert-level performance across all of these benchmarks, potentially democratizing future access to expert-level computational modeling and expanding the capabilities of current experts.