Four ways Google Research scientists have been using Empirical Research Assistance

Empirical Research Assistance (ERA) is being used by Google researchers to tackle practical scientific problems rather than only benchmark exercises. Early applications span public-health forecasting, cosmology, and climate monitoring, showing that AI can improve prediction, solve difficult mathematical problems, and extract new value from existing data. The results suggest ERA could make advanced computational research more accessible while producing interpretable, scientifically grounded models.

Public Health Forecasting

• Google expanded ERA-based hospitalization forecasts from COVID-19 to influenza and RSV.
• The team submits weekly forecasts for every U.S. state, covering horizons of up to four weeks.
• Google forecasts have performed at or near the top of public CDC flu and COVID-19 leaderboards, with similarly strong internal results for RSV.
• Forecast accuracy is evaluated using the Weighted Interval Score on log-transformed hospitalization data.
• This approach could broaden access to epidemiological modeling and support forecasting for more diseases and regions.

Cosmology: Cosmic Strings

• Cosmic strings are theoretical spacetime defects that may emit gravitational radiation.
• Calculating their radiation spectrum is difficult because the governing equations contain singularities.
• Earlier work found only a partial solution for a square loop with an angle of 90 degrees.
• By combining ERA with Gemini Deep Think, researchers derived six general solutions and a concise formula for the asymptotic limit.
• The result demonstrates how AI systems can help explore advanced mathematical techniques and address previously unsolved cosmological problems.

Climate Monitoring with Weather Satellites

• Existing CO₂ satellites provide highly precise but infrequent and geographically limited measurements.
• Geostationary satellites such as GOES East scan large areas every 10 minutes, but were not designed to measure CO₂.
• Researchers used ERA to create a physics-guided neural network that combines:
  • 16 GOES East wavelength bands
  • Lower-troposphere meteorology
  • Solar angles
  • Time of year
• Trained using sparse OCO-2 and OCO-3 observations, the model estimated column-averaged CO₂ continuously across the satellite’s coverage area.
• Comparisons with independent satellite and ground-based observations showed that it captured real CO₂ variation.
• The work illustrates how AI can repurpose existing instruments and improve the value of expensive scientific datasets.

ERA’s early applications indicate that AI-assisted empirical software can support accurate forecasting, novel mathematical discovery, and higher-resolution environmental monitoring. Its greatest potential may lie in combining domain expertise with existing data and infrastructure to solve problems that would otherwise require substantial time and specialized resources.