Empirical Research Assistance (ERA): From Nature publication to catalyzing Computational Discovery
Empirical Research Assistance (ERA) is a Google AI system designed to help scientists develop expert-level computational models. Using Gemini, it searches literature, generates and evaluates code, and explores thousands of possible solutions through tree search. A Nature paper reports strong performance across scientific benchmarks, while new applications suggest ERA can accelerate research in health, climate, energy, and economics. ## How ERA Supports Scientific Coding - ERA starts with a scientific problem and a success metric. - It searches relevant research, combines methods, writes code, and iteratively tests and improves solutions. - Its tree-search process evaluates thousands of alternatives to optimize the resulting model. - Benchmarks in genomics, public health, satellite imagery, neuroscience, time-series forecasting, and mathematics showed expert-level performance. ## Applications to Open Scientific Problems - **Epidemiological forecasting** - Predicted U.S. hospital admissions up to four weeks ahead for flu, COVID-19, and RSV. - Forecasts ranked at or near the top of CDC leaderboards. - The techniques can potentially be adapted to other countries and diseases. - **California water-supply forecasting** - Produced seasonal runoff predictions for snow-fed river basins. - Delivered more accurate early forecasts than California’s official Bulletin 120 outlook. - Improved predictions could support water management and agriculture. - **Atmospheric carbon dioxide monitoring** - Combined geostationary weather-satellite data with other inputs to estimate CO₂ concentrations every 10 minutes across broad areas. - Captured urban emissions, plant-driven daytime absorption, and other atmospheric cycles. - Provides higher spatial and temporal coverage than measurements from satellites such as Orbiting Carbon Observatory-2. - **Solar-energy design** - Combined ERA with Google Antigravity to optimize three-dimensional solar-panel geometries. - Identified a 500-triangle volumetric fan design that could capture scattered radiation without backward shading. - **Retail forecasting** - Used economic indicators, Google Trends, historical patterns, and consumer sentiment. - Matched or exceeded commercial consensus forecasts and the Chicago Fed’s monthly retail forecast. ## Computational Discovery - Google is gradually opening access to Computational Discovery through a trusted tester program in Google Labs. - The system combines ERA with AlphaEvolve to support computational scientific investigation. - It complements other Gemini for Science experiments: - **Hypothesis Generation**, built with AI Co-Scientist, supports developing scientific hypotheses. - **Literature Insights** supports research and literature analysis. ERA’s demonstrated value lies in automating the labor-intensive cycle of designing, testing, and refining scientific software. Its expanding applications indicate that AI-assisted computational research could broaden access to advanced modeling while helping experts investigate complex scientific problems more quickly.
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