Google DeepMind

Developing Gemini Argon 4 responsibly and safely has been at the core of our core mission at Google DeepMind. Read more about our approach to security and safety here: https://lnkd.in/gAz46B4t

For decades, decoding disease meant years of trial and error. Here’s how our AI models are helping researchers move faster from the lab to real-world care: 🔹 AlphaGenome Atlas: Helped University of Exeter researchers analyze genomic data from 54,000+ people to pinpoint rare genetic variants linked to aging and cellular health. 🔹 AlphaProtein Novo: Designed custom, new-to-nature enzymes from scratch, validated in lab tests to help synthesize key drug building blocks. 🔹 AlphaFold: Screened 30,000 compounds with Barcelona Institute for Global Health (ISGlobal) ultimately helping their researchers identify existing FDA-approved drugs for Chagas disease 🔹 AlphaProteo & AlphaFold: Supporting University of Pennsylvania researchers to help design novel cellular therapies for solid tumors and complex diseases. 🔹 AlphaFold & AlphaMissense: Helping the Infectious Disease Institute at Makerere University forecast malaria drug resistance mutations. A breakthrough only matters when it reaches the people who need it. Find out more about how we’re partnering with the scientific community to accelerate biomedical progress → https://goo.gle/4e0yfDx

Last week, Hurricane Isaias made landfall in the Florida Panhandle. Our thoughts are with everyone affected and with the recovery efforts still underway. Earlier and more accurate forecasting can help protect communities by giving them more time to prepare, and we are grateful that Google’s WeatherNext model was able to assist the National Hurricane Center's expert forecasters. Based on initial evaluations, here's how our model supported the response to Isaias: - Tracking the Hurricane’s Path: WeatherNext was the most accurate model on the storm’s track. While traditional physics models predicted landfall closer to New Orleans, WeatherNext correctly predicted a path further east. This allowed NHC forecasters to highlight the potential for wind and storm surge impacts in the Florida Panhandle from their very first forecast. - More Time to Prepare: Up to three days before landfall, our model consistently predicted its eventual location in the Florida Panhandle to within 60 km or less, supporting the NHC as they honed in on a track toward the Florida Panhandle and issued hurricane watches and warnings to give officials in Florida, Alabama, and Mississippi more time to prepare. - Early Indications about Intensity: Early on, WeatherNext accurately predicted the intensification to a major hurricane. Alongside the regional hurricane models HWRF and HMON, WeatherNext’s intensity forecasts helped NHC forecasters to predict that the Isaias would approach the coast as a category 2 hurricane from the very first advisory. Giving communities more time and more accurate warnings is critical for protecting lives and livelihoods. While model performance varies from storm to storm, Isaias reflects our broader findings. In our recent Nature paper co-authored with NHC forecasters, evaluating 200+ global tropical cyclones, we found our model provides ~1 day of additional lead time for both track and intensity. It’s been an honor for the Google DeepMind and Google Research teams to work alongside NHC forecasters. By combining AI, physics models, satellite data, and the crucial expertise of meteorologists, we're excited to keep pushing the boundaries of forecasting to help protect communities.

SynthID has been named one of TIME’s Best Inventions of 2026. With over 180 billion images and videos already watermarked, it’s making it easier for people to distinguish AI-generated content from other media → https://goo.gle/47V1BPV

Here’s how researchers are using Gemini 4 Argon to accelerate scientific discovery. ⚛️ Quantum computing: The model optimized our real-time AlphaQubit error-correction decoder to run with nearly twice the throughput. It also designed a routine to compute arcsin, a core building block for quantum simulations, that cuts computing costs by more than 100x ☀️ Nuclear fusion: Advancing limitless clean fusion energy requires optimizing the complex 3D magnetic fields inside fusion machines called stellarators. Alongside University of Maryland professor Matt Landreman, our team found a broader class of solutions to long-standing math problem Grad’s conjecture - once thought impossible to solve - giving reliable benchmarks for stellarator simulation codes. 🧬 Enzyme design: Argon reduced months of screening to a matter of hours - curating data, tuning models, and applying biochemistry filters to generate five novel enzymes - all confirmed active in wet-lab testing. To further the pace of research, we’re offering 10,000 scientists free access via Google Antigravity for 6 months. Find out more → https://goo.gle/4yFTUce

Over half of reported crimes in Singapore use sophisticated social engineering. With GovTech Singapore, we’re building on-device fraud detection using our open model Gemma. By fine-tuning it on local scam tactics, it identifies threats in real time without compromising personal privacy → https://goo.gle/4yu2WIE

Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught Apptronik’s Apollo humanoid to sink 7 throws in a row. By turning video into a training ground for physical AI, Argon compresses weeks of manual modeling into just a few hours. Find out more → https://goo.gle/4etktJu

Gemini 4 Argon is now globally available. Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense. Here’s how you can try it: 🔵 Everyday users: In the Gemini app and AI Mode in Search for Google AI Pro and Ultra subscribers. 🔵 Developers: Gemini API paid tier, plus Antigravity and Google AI Studio for Google AI Ultra subscribers. 🔵 Scientists: Apply for the top usage tier in Antigravity free of charge. 🔵 Enterprises: Gemini for business, the API, and in Antigravity. Find out more → https://goo.gle/4yFTUce

In the latest essay as part of our DeepMind Institute work, my colleague Alex Imas and I argue that AI has the potential to bend the curve of scientific discovery in a way that drives not only scientific progress, but also economic growth and societal progress, but only if the global scientific enterprise adapts systems and institutions and clears bottlenecks and enables scientists everywhere.  This comes at a particularly helpful time, when the type of scientific progress that drives economic growth and societal progress is facing headwinds; sustaining historical rates of innovation now requires exponentially larger research teams and capital expenditure.  AI enables and assists the work of scientists in ways that can substantially accelerate scientific productivity (i.e. more science and faster). We see a strong case that AI is not only a general purpose technology, but also an invention of a method of invention (IMI) like a microscope or telescope. What’s exciting about IMIs is that they permanently shift the production function of discovery itself.  However to fully realize AI’s potential as an IMI and driver of economic growth, scientific progress cannot be simply increasing the volume of research output (and without validation). Instead it must be leveraged by scientists to expand the boundaries of what is scientifically possible and extend the knowledge frontier. We offer ideas on how this might be accomplished, drawing in part on recent research on how scientists are using AI in their work. As with all DeepMind Institute essays, we hope sharing our thinking serves as a springboard for further discussion and solutions. You can read our full essay on the DeepMind Institute’s site: https://lnkd.in/ejUryJ8r