In the 1660s, with the help of a simple, homemade light microscope that magnified samples more than 250 times, a Dutch fabric merchant named Antoine van Leeuwenhoek became the first person to document a close-up view of bacteria, red blood cells, sperm cells, and many other scientific sights. Since then, light microscopy has solidified its […]

We’ve seen developers doing amazing things with Gemini 2.5 Pro, so we decided to release an updated version a couple of weeks early to get into developers hands sooner. Today we’re excited to release Gemini 2.5 Pro Preview (I/O edition). This update features even stronger coding capabilities, for you to start building with before Google

Gemini-powered automatic evaluation and prompt refinement system In order to achieve our goals, we developed an automated approach leveraging Gemini models for evaluation of simplification quality and self-refinement of prompts. However, crafting prompts for nuanced simplification, where readability must improve without sacrificing meaning or detail, is challenging. An automated system addresses this challenge by enabling

Pilot data As part of the pilot, Makerere AI Lab and Google Research collected 8,091 annotated adversarial queries in English and six African languages (e.g., Pidgin English, Luganda, Swahili, Chichewa). The queries are adversarial in nature and have a high likelihood of producing unsafe responses from an LLM as a means of testing and mitigating

Acknowledgements The research described here is joint work across many teams at Google Research and Google DeepMind. We are grateful to all our co-authors: CJ Park, Tim Strother, Yong Cheng, Wei-Hung Weng, David Stutz, Nenad Tomasev, David G.T. Barrett, Anil Palepu, Valentin Liévin, Yash Sharma, Roma Ruparel, Abdullah Ahmed, Elahe Vedadi, Kimberly Kanada, Cìan Hughes,

Large language models (LLMs) have shown potential for medical and health question-answering across various health-related tests and spanning different formats and sources. Indeed we have been on the forefront of efforts to expand the utility of LLMs for health and medical applications, as demonstrated in our recent work on Med-Gemini, MedPaLM, AMIE, Multimodal Medical AI,

Whole-brain activity in a small vertebrate Traditionally, neuroscientists study neural activity by breaking complex behaviors into smaller parts. To study hunting, for example, they might look at the hunger-sensing capabilities of the cells and organs, the olfactory system that allows an animal to smell their prey, the visual system for tracking, and so on. But

Music AI Sandbox was developed by Adam Roberts, Amy Stuart, Ari Troper, Beat Gfeller, Chris Deaner, Chris Reardon, Colin McArdell, DY Kim, Ethan Manilow, Felix Riedel, George Brower, Hema Manickavasagam, Jeff Chang, Jesse Engel, Michael Chang, Moon Park, Pawel Wluka, Reed Enger, Ross Cairns, Sage Stevens, Tom Jenkins, Tom Hume and Yotam Mann. Additional contributions

1. Measurement: Understanding mobility patterns Accurately evaluating the current state of the transportation network and mobility patterns is the first step to improving mobility. This involves gathering and analyzing real-time and historical data from various sources to understand both current and historical conditions and trends. We need to track the effects of changes as we