Improving tropical cyclone prediction can help communities better prepare... & stay safe. Developed with Google DeepMind, we're launching Weather Lab to share our AI weather prediction models, starting with the new experimental cyclone prediction model. 🧵show more

We're excited to announce #AlphaFold 3 with Google DeepMind... in nature: our new AI model for predicting biomolecule structures with unprecedented breadth and accuracy. Expanding beyond proteins to tackle DNA, RNA, small molecules to fuel advances in biology & drug design 🧵show more

Prediction markets are entering the x402 era. We're excited... to share that PayGo's infrastructure is currently being integrated at the technical level with 1024EX , bringing HTTP 402 experiences to AI-native prediction markets. Infrastructure meets intelligence. 🌐 🌐 #PayGo #x402 #PredictionMarketsshow more

300+ devices. Millions of real-time signals. One decentralized weather... oracle. We’re partnering with Nubila Network (mainnet arc) to bring hyperlocal, verifiable climate data to Nexus—fueling prediction markets, AI models, and on-chain protocols.show more

Please be aware that flights are temporarily suspended due... to the weather conditions. We're coordinating with airlines and closely monitoring the weather as our dedicated teams work to prepare for a safe resumption of service as soon as the weather permits.show more

Our development team is breaking new ground in market... prediction accuracy. Just two weeks ago we shared a video showcasing $YAI Oracle's training model with an accuracy rate of 0.6703. #Today marks another milestone as we proudly announce a significant leap in predictive precision with a remarkable 23.83% increase. Propelling the Oracle's prediction accuracy to an astounding high of 0.830. 1/show more

Today at #GoogleIO we've announced: 🤖 Project Astra: our... vision for AI assistants 🎨 Imagen 3 & Veo: our new image and video gen models ♊ Gemini 1.5 Flash: a light-weight multimodal model with long context & 1.5 Pro with 2M tokens... "Tuesday" as we call it Google DeepMindshow more

ESMFold2 and the ESM-C family, now available for use!... We’ve partnered with biohub (the ESM team’s new home), to provide day 1 access to their newly open-sourced series of models. The family of models show best-in-class results for structure prediction, de novo design, and protein-language model tasks.show more

The laws of physics apply everywhere. ⚛️ Co-developed by... NVIDIA, Google DeepMind and Disney Research, Walt Disney Imagineering, the new Newton Beta, managed by the The Linux Foundation, now also runs with Isaac Lab and with MuJoCo Warp, which delivers warp speed for robot learning. Learn how to train a quadruped and simulate multiphysics in our tech blog. 🔗 #CoRL2025show more

PERFECT EXAMPLE OF HOW TO TURN $126 INTO $1,400... WITH ONE PREDICTION This trader earn over $30,000 just by betting on the weather market There’s a trader on Polymarket who specializes in weather markets and trades every single day like a pro. Over his career, he’s made more than 1,300 predictions with impressive accuracy. In just the last month, he’s racked up over $17,000 in profits. He focuses on short-term forecasts (1–2 days ahead), where weather models are most reliable, and places dozens of bets daily. Yesterday, for example, he scored huge wins in Seoul and Dallas, and he also trades temperatures in New York, London, Chicago, and Taipei. check his profile here: you can copy him via Ares:show more

Very proud to see the FourCastNet model that I... helped build at NVIDIA in collaboration with Berkeley Lab and other universities be highlighted in Jensen's keynote at #GTC24 - FourCastNet was the very first high-resolution #AI model to show competitive performance for weather forecasting and is part of Earth-2 services. It is now running at ECMWF and you can get 2-week forecast there in realtime. Hear Bill Collins from Berkeley Lab leverage FourCastNet for studying extreme weather events, offering accurate and efficient results from huge ensembles at a lower computational cost.show more

1/ We're thrilled to share our preprint on Deep3P... - a new approach that takes us on a deep dive into the world of brain tumors, using three-photon microscopy and #AI. This exciting endeavor is brought to life together with the amazing Prevedel Lab !show more

1/ Gemini 2.5 is here, and it’s our most... intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on Arena by a significant margin. With a model this intelligent, we wanted to get it to people as quickly as possible. Find it on Google AI Studio and in the Google Gemini for Gemini Advanced users now – and in Vertex in the coming weeks. This is the start of a new era of thinking models – and we can’t wait to see where things go from here.show more

Today we’re introducing Google AI Threat Defense - a... comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.show more

Today we're dropping the "beta" tag from Adaptyv, launching... our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the experimental data they're trained on and the hypotheses they can test in the real world. Now, after a year of working with many great partners, we’ve scaled our infrastructure to the point that we're now open to anyone who wants to use our platform! Overall, this year, over 30 companies started using Adaptyv to validate their protein designs - from some of the biggest pharmas to frontier AI labs to many, many techbio startups. We've run hundreds of experiments, tested well over 10,000 proteins this year and are generating the data that validates the best AI models currently in development.show more

Most of what I actually need help with, I... never think to tell a model. But why is it on me to remember? Our new paper asks: what if AI could proactively specialize to individuals and the tasks they’re carrying out at this very moment? 🧵show more

Today we’re releasing SAIR, the Structurally Augmented IC50 Repository.... SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQshow more

Introducing VL-JEPA: Vision-Language Joint Embedding Predictive Architecture for streaming,... live action recognition, retrieval, VQA, and classification tasks with better performance and higher efficiency than large VLMs. • VL-JEPA is the first non-generative model that can perform general-domain vision-language tasks in real-time, built on a joint embedding predictive architecture. • We demonstrate in controlled experiments that VL-JEPA, trained with latent space embedding prediction, outperforms VLMs that rely on data space token prediction. • We show that VL-JEPA delivers significant efficiency gains over VLMs for online video streaming applications, thanks to its non-autoregressive design and native support for selective decoding. • We highlight that our VL-JEPA model, with an unified model architecture, can effectively handle a wide range of classification, retrieval, and VQA tasks at the same time. by Delong Chen (陈德龙) Mustafa Shukor Théo Moutakanni Willy Jade Lei Yu Tejaswi Kasarla Allen Bolourchi Yann LeCun Pascale Fungshow more

📢 Breaking news: Google opens access to Bard, its... AI-powered chatbot. Bard seems like Google's answer to Microsoft's new Bing although it's not clear if it offers any features that are significantly different of better than Bing. With the new Bing, Microsoft wants to increase its share of the internet search market, currently dominated by Google. More than 80% internet searches done in the world are done on Google. The word "google" has entered the dictionary as a verb. Google doesn't have to do anything flashy to retain their market share. If their Bard matches Bing, people will stay on Google out of habit.show more