Google Research Blog

Four ways Google Research scientists have been using Empirical Research Assistance

6.5内容质量
Four ways Google Research scientists have been using Empirical Research Assistance

TL;DR · AI 摘要

Google Research科学家正在利用Empirical Research Assistance进行多种研究，包括机器学习算法、系统计算和量子AI等领域。

核心要点

  • Empirical Research Assistance支持多类型研究。
  • 研究领域涵盖机器学习算法。
  • 研究领域包括系统计算和量子AI。

结构提纲

按章节快速跳转。

  1. §引言

    介绍Empirical Research Assistance及其应用领域。

  2. ·多类型研究支持

    Empirical Research Assistance支持不同时间尺度和风险水平的研究。

  3. ·研究领域

    涵盖机器学习算法、系统计算和量子AI等多个领域。

  4. ›机器学习算法

    包括算法理论、数据管理、数据挖掘与建模等。

  5. ›系统计算与量子AI

    涉及分布式系统、硬件架构、移动系统等。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • Google Research的应用

金句 / Highlights

值得收藏与分享的关键句。

  • 我们努力创造一个有利于多种类型研究的环境，这些研究跨越不同的时间尺度和风险水平。

    — 第 2 段

    ⬇︎ 下载 PNG𝕏 分享到 X
#Google Research#Empirical Research Assistance#机器学习#系统计算#量子AI
打开原文

Four ways Google Research scientists have been using Empirical Research Assistance

Jump to Content

[Research](https://research.google/ "Google Research")

[Research](https://research.google/ "Google Research")

  • Who we are

Back to Who we are menu

  • * *

Defining the technology of today and tomorrow.

  • ## Philosophy

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.

Learn more about our Philosophy Learn more Philosophy

  • ## People

Our researchers drive advancements in computer science through both fundamental and applied research.

Learn more about our People Learn more People

  • Research areas

Back to Research areas menu

  • * *

*

Research areas

Research areas

Back to Research areas menu

  • * *

*

Foundational ML & Algorithms

Foundational ML & Algorithms

Back to Foundational ML & Algorithms menu

  • * *

*

Computing Systems & Quantum AI

Computing Systems & Quantum AI

Back to Computing Systems & Quantum AI menu

  • * *

*

Science, AI & Society

Science, AI & Society

Back to Science, AI & Society menu

  • * *
  • Our work

Back to Our work menu

  • * *
  • ## Projects

We regularly open-source projects with the broader research community and apply our developments to Google products.

Learn more about our Projects Learn more Projects

  • ## Publications

Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.

Learn more about our Publications Learn more Publications

  • ## Resources

We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.

Learn more about our Resources Learn more Resources

  • Programs & events

Back to Programs & events menu

  • * *

Shaping the future, together.

Collaborate with us

  • ## Student programs

Supporting the next generation of researchers through a wide range of programming.

Learn more about our Student programs Learn more Student programs

  • ## Faculty programs

Participating in the academic research community through meaningful engagement with university faculty.

Learn more about our Faculty programs Learn more Faculty programs

  • ## Conferences & events

Connecting with the broader research community through events is essential for creating progress in every aspect of our work.

Learn more about our Conferences & events Learn more Conferences & events

Collaborate with us

Search

Image 1
Image 1
  1. Home
  2. Blog

Four ways Google Research scientists have been using Empirical Research Assistance

April 29, 2026

The Google Research Science team

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 capabilities to accelerate scientific discoveries.

Quick links

  • [](https://twitter.com/intent/tweet?text=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Share on Twitter")
  • [](https://www.facebook.com/sharer/sharer.php?u=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Share on Facebook")
  • [](https://www.linkedin.com/shareArticle?url=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/&mini=true "Share on LinkedIn")
  • [](mailto:[email protected]?subject=Check%20out%20this%20site&body=Check%20out%20https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Send via Email")
  • Copy link ×

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.

Public health: Hospitalization forecasts for flu, COVID-19, and RSV

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.

Image 2: ERAapp1_Forecasts
Image 2: ERAapp1_Forecasts

_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._

Cosmology: Cosmic strings and gravitational energy radiation

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 where values approach infinity and traditional models break down. Last fall, a paper used OpenAI’s GPT-5 to find a partial solution for the gravitational energy radiating from cosmic strings, but only for the simplest case of a square loop where the angle α = π/2, or 90 degrees. A unified exact solution — a single, complete mathematical formula that solved the integral perfectly — remained an open problem.

To address this, we combined ERA with Gemini Deep Think. By systematically exploring mathematical techniques capable of navigating these singularities, we successfully derived six general solutions and a concise formula for the asymptotic limit, which we shared in March. This illustrates the powerful potential of pairing ERA with advanced LLMs to unlock precise, novel solutions at the frontier of cosmology.

play silent looping video pause silent looping video

unmute video mute video

_A computer simulation of cosmic strings in an expanding universe. The number in the lower right corner (e.g., 0.3654) is the__Hubble radius__, the size of the observable universe. White strings are longer than the observable universe. Red and yellow loops, like one at the upper right, are features that can pinch off of strings, oscillate, merge, and collapse, releasing gravitational energy that should be observable. Credit:__Chris Ringeval_

Climate and sustainability: Using weather satellites to monitor CO 2

Regular observations of carbon dioxide (CO 2) began at Hawaii’s Mauna Loa Observatory in the late 1950s, yielding the iconic Keeling Curve that documents rising global CO 2 concentrations in Earth’s atmosphere. Mapping human greenhouse gas emissions and understanding how plants, trees, soils and oceans absorb those emissions requires us to track how CO 2 varies across regions and over time. Current space-based CO 2 sensors, like NASA’s Orbiting Carbon Observatory-2 (OCO-2) were designed to make high-precision observations, but they only map a tiny fraction of the Earth’s surface and return to each location just once every 16 days. Geostationary satellites, such as the GOES East satellite designed to support weather forecasting, orbit the Earth from a much higher altitude and can scan an entire hemisphere every 10 minutes. However, none of the existing geostationary satellites were designed to map CO 2.

Google researchers used ERA to develop a single-pixel, physics-guided neural network to distill a column-averaged CO 2 signal from the existing GOES East observations. To do so, the model combines data from 16 wavelength bands from GOES-East with lower-troposphere meteorology, solar angles, and day of the year. After training on the sparse observations from OCO-2 and OCO-3, the model was then able to derive estimates of column-averaged CO 2 everywhere and every 10 minutes.

Research shared at the International Workshop on Greenhouse Gas Measurements from Space shows that the AI-developed model is able to leverage the high spatial and temporal density of the GOES East observations to track column-averaged CO 2 with unprecedented spatial and temporal resolution. Comparisons against independent data from additional years of OCO-2 observations, and from the ground-based total column carbon observing network, confirm the model’s ability to capture real CO 2 variability.

These results show how an AI algorithm can extract additional value from existing observational instruments, especially for resource-intensive satellite research missions. This project is among several questions related to climate and greenhouse gases that Google researchers are exploring using ERA.

Image 3: ERAapp3_CO2
Image 3: ERAapp3_CO2

_Atmospheric CO_ _2_ _concentration over the Los Angeles area on October 18, 2024, as seen from GOES-East (__left__) and the Orbiting Carbon Observatory-2 (__right__). The AI-developed model on the left takes GOES-East weather satellite data and combines it with other information to estimate column-averaged CO_ _2_ _concentration every 10 minutes at all locations to reveal spatial patterns of urban CO_ _2_ _emissions. The panel on the right shows the limited OCO-2 observations available for that same day._

Neuroscience: Discovering mechanisms of neural circuits

Although we can now map tens of thousands of neurons in living brains, untangling the functional circuits is the next step. Google researchers used ERA to tackle this challenge in both real and simulated zebrafish, a popular model organism for studying how a vertebrate detects stimuli, processes information and responds. In natural settings, light passing through ripples on the water’s surface creates patterns of light and dark stripes on the seafloor or riverbed. Zebrafish have evolved to instinctively respond to changes in those stripes in order to stay in shallow water and avoid getting swept away.

In a new study, we looked at the zebrafish neural circuit corresponding to this environmental stimulus. We provided ERA with the wiring diagram of simZFish, a simplified zebrafish body and brain simulator. Guided by this information — revealing what cellular connections exist, but omitting the mathematical rules that govern them — ERA was able to propose circuits that connect stimulus to neural activity to motor response. Testing these AI-hypothesized circuits against new visual stimuli showed that they were not just statistical shortcuts, but accurate neural mechanisms that generalize to other, similar situations.

Image 4: ERAapp4_Zebrafish
Image 4: ERAapp4_Zebrafish

_a:__the experiment in which a simulated zebrafish responds to stripes.__b:__ERA iteratively builds and tests models to discover the underlying neural circuits.__c:__a comparison of effective connectivity matrices showing how neurons influence each other (blue = inhibitory, red = excitatory). The right panel compares the actual mechanisms of the simulation against AI models built with and without the structural information, demonstrating that the guided search successfully recovers the ground truth._

This builds on results from the preprint, which showed that AI-developed models could outperform baseline methods at predicting the activity of over 70,000 neurons captured in the Zebrafish Activity Prediction Benchmark, ZAPBench, a dataset of neural activity from experiments that mimic typical environmental stimuli.

While ZAPBench proved ERA's ability to find state-of-the-art predictive solutions, the simulated environment reveals how it can go beyond black-box modeling. Equipped with structural information, ERA discovered interpretable, mechanistically accurate solutions, providing a powerful blueprint for addressing scientific grand challenges in living brains.

Conclusion: AI-assisted science

These four projects are among a growing list of results that show how LLM-backed systems can advance science and accelerate the pace of discovery. These examples represent a range of fields and also types of problems, from theoretical math to data forecasting to analyzing data from observational instruments and simulation output. They also showcase the potential for AI-enabled science to solve open problems, democratize access to computational modeling, and maximize the utility of existing observational data. We’re excited about the progress being unlocked by ERA and other Google tools — including co-scientist and PAT — designed to accelerate scientific discovery.

Acknowledgments

_We’d like to thank our collaborators on developing__ERA__, and all the scientists who are among the early adopters. The epidemiological forecasting work is led by Zahra Shamsi, Sarah Martinson, Nicholas Reich, Martyna Plomecka, and Brian Williams. The cosmological paper is authored by Michael Brenner, Vincent Cohen-Addad, and David Woodruff. The research on carbon dioxide monitoring is led by Aarón Sonabend-W, Sean Campbell, Renee Johnston, Vishal Batchu, Carl Elkin, Christopher Van Arsdale, John Platt, and Anna Michalak. The paper on neural circuits was authored by Jan-Matthis Lückmann, Viren Jain, and Michał Januszewski. We also acknowledge leadership support from John Platt, Michael Brenner, Lizzie Dorfman, Vip Gupta, Alison Lentz, Erica Brand, Katherine Chou, Ronit Levavi Morad, Yossi Matias, and James Manyika._

Labels:* Data Mining & Modeling

Quick links

  • [](https://twitter.com/intent/tweet?text=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Share on Twitter")
  • [](https://www.facebook.com/sharer/sharer.php?u=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Share on Facebook")
  • [](https://www.linkedin.com/shareArticle?url=https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/&mini=true "Share on LinkedIn")
  • [](mailto:[email protected]?subject=Check%20out%20this%20site&body=Check%20out%20https%3A//research.google/blog/four-ways-google-research-scientists-have-been-using-empirical-research-assistance/ "Send via Email")
  • Copy link ×

Other posts of interest

  • ![Image 5 May 1, 2026 Catalyzing scientific impact through global partnerships and open resources * Data Mining & Modeling · * General Science · * Health & Bioscience · * Open Source Models & Datasets](https://research.google/blog/catalyzing-scientific-impact-through-global-partnerships-and-open-resources/)
  • ![Image 6 April 22, 2026 It's all about the angle: Your photos, re-composed * Generative AI · * Photography](https://research.google/blog/its-all-about-the-angle-your-photos-re-composed/)
  • ![Image 7 April 21, 2026 ReasoningBank: Enabling agents to learn from experience * Generative AI · * Machine Intelligence · * Natural Language Processing](https://research.google/blog/reasoningbank-enabling-agents-to-learn-from-experience/)

×❮❯

Image 8: ERAapp3_CO2
Image 8: ERAapp3_CO2
Image 9: ERAapp4_Zebrafish
Image 9: ERAapp4_Zebrafish
Image 10: ERAapp1_Forecasts
Image 10: ERAapp1_Forecasts

Follow us

  • [](https://x.com/GoogleResearch "Follow us on x")
  • [](https://www.linkedin.com/showcase/googleresearch/ "Follow us on linkedin")
  • [](https://www.youtube.com/c/GoogleResearch "Follow us on youtube")
  • [](https://github.com/google-research "Follow us on github")

[](https://www.google.com/ "Google")

×

Image 11
Image 11