趋势解读：Four ways Google Research scientists have been using，聚焦形式化数学证明能力

Since introducing Empirical Research Assistance in the fall, Google Research scientists have been using it to address real-world applications in epidemiology, cosmology, atmospheric monitoring, and neuroscience, providing a hint of AI’s transformational potential to accelerate scientific discoveries. 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. In the preprint , authors used ERA to predict U.S. hospitalizations for COVID-19, showing that it was able to retrospectively match or outperform existing tools from the Centers for Disease Control and Prevention (CDC) and leading research institutions. As a follow-on effort, the team has now expanded to generate forecasts not just for COVID, but also for influenza and respiratory syncytial virus (RSV), and has been submitting prospective forecasts in real time every week. When the CDC’s long-running flu forecast challenge opened in November for the 2025-26 season, Google began submitting weekly forecasts for every U.S. state and at all time horizons, up to four weeks in the future. Late last year Google also joined CDC’s year-round live forecasts for state-level COVID-19 hospitalizations , as well as CDC’s recently launched hub for forecasting RSV . Public leaderboards for flu and COVID-19 run by Nicholas Reich , a biostatistics professor at the University of Massachusetts Amherst and consultant on this project, show that Google has been performing at or near the top of both leaderboards during the time they have been submitting forecasts to each project (see figure). Although there is no public leaderboard for RSV, internal analyses show a similarly strong performance. An AI-powered tool that can meet or exceed the forecasting accuracy of leading public health agency tools promises huge public health benefit for tracking newer conditions and in broader locations, democratizing access to computational modeling for epidemiology for a wider range of infections and geographies. Left: The graphs show Google’s forecasted hospitalizations across California for flu, COVID-19 and RSV starting in November 2025. The black line shows actual hospitalizations. Right: Forecasts are ranked based on their Weighted Interval Score , a measure of forecast accuracy, computed on the log-transformed observed values. Google's forecasts are shown in pink. CDC-developed forecasts are shown as black bars, and other research groups are gray. Cosmic strings are theoretical defects in the fabric of spacetime, believed to have formed in the early universe and predicted to emit gravitational radiation. Calculating the spectrum of this emitted energy is an unsolved problem, largely because the governing equations contain singularities — mathematical points wher