Four ways Google Research scientists have been using Empirical Research Assistance

AI’s capabilities to advance scientific discovery are growing every week, with outcomes that promise not just to enable breakthrough discoveries but to transform how science is done. In September, we released a preprint introducing Empirical Research Assistance (ERA) to help scientists generate expert-level empirical software. That included novel solutions to six diverse and challenging benchmark problems in fields ranging from cell biology to neuroscience.Since then, Google scientists and our academic collaborators have been developing and using ERA to test its capabilities and explore potential applications. These efforts go beyond proof-of-concept tests to real-world scenarios in epidemiology, geospatial analysis, and more, revealing how AI can democratize access to the power of computational modeling, find solutions to unsolved problems, unlock deeper insights from existing data collections, and go beyond black-box modeling to discover interpretable, mechanistically accurate solutions.It’s been inspiring to see the excitement of Google research scientists, visiting faculty researchers and academic collaborators as they experiment with ERA. We are thrilled to see these capabilities expand as it nears more widespread availability to support AI-assisted scientific discovery for global benefit.

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