Research@ 2026: Novel capabilities and intelligence

We’re sharing our latest milestones and some of the advanced AI capabilities and intelligence that we’re putting in the hands of experts.

Technology is supercharging scientific discovery and improving people's lives. At Google Research, we’re pioneering breakthroughs across domains and bringing our research to reality to benefit people everywhere.

Today at Research@ in Mountain View, we’re sharing our latest milestones and some of the advanced AI capabilities and intelligence that we’re putting in the hands of experts. Our latest breakthroughs help drive progress on the world’s most pressing challenges including healthcare, crisis resilience, and beyond. This momentum builds on our recent announcements from AMIE in the clinic, to Earth AI for public health, to Project Suncatcher in orbit.

From planetary intelligence to advanced medical workflows, we’re putting capabilities once out of reach in the hands of people worldwide.

We have evolved a Geospatial Reasoning agent from an early concept into a much more capable, experimental system.

Following successful deployments across 15,000+ submissions at STOC, ICML, NeurIPS, and ICLR, Deep Review is launching publicly.

We're helping public health leaders overcome reporting lags, forecast disease outbreaks, and deliver care to vulnerable communities.

A peer-reviewed study in The Lancet evaluates Google's AMIE medical AI in a real-world primary care clinic.

In collaboration with scientists and research partners, Google is helping to make disease more detectable, treatable, and preventable.

We’re asking what’s possible for health, natural disaster and weather resilience, learning, and economic opportunity.

Google’s science AI model was the best at forecasting flu-related hospital admissions, the Centers for Disease Control announced.

Learn about Project Suncatcher, how we’re testing hardware survival for space, designing cooling systems for AI chips, and more.

With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows.