Graph algorithms (the pre–deep learning era) Initial work in graph analysis often focused on developing methods to better understand the structure of graphs. They aimed to uncover hidden patterns, properties, and relationships within graphs (e.g., community structures or centrality within a network) and were concerned with gaining insights into the graph’s overall organization and meaning. […]
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Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient
During speech production, it is evident that language embeddings (blue) in the IFG peaked before speech embeddings (red) peaked in the sensorimotor area, followed by the peak of speech encoding in the STG. In contrast, during speech comprehension, the peak encoding shifted to after the word onset, with speech embeddings (red) in the STG peaking
Cluster management systems, such as Google’s Borg, run hundreds of thousands of jobs across tens of thousands of machines with the goal of achieving high utilization via effective load balancing, efficient task placement, and machine sharing. Load balancing is the process of distributing network traffic or computational workloads across multiple servers or computing resources, and
Acknowledgements The research described here is joint work across Google Research, Google Health, Google DeepMind, and partnering teams, including Consumer Health Research, Personal Safety, quality, regulatory, and clinical operations. The following researchers contributed to this work: Kamal Shah, Anran Wang, Yiwen Chen, Jitender Munjal, Sumeet Chhabra, Anthony Stange, Enxun Wei, Tuan Phan, Tracy Giest, Beszel
This web scraper runs entirely in your browser and is perfect for creating training data for AI models. It works by reading the website’s sitemap.xml file, making it particularly well-suited for modern platforms like Squarespace and Shopify that automatically generate sitemaps. The scraper preserves the structure of your content, including headings, paragraphs, lists, and tables,
Due to challenges in generating text while maintaining DP and computational efficiency, prior work focused on generating a small amount of data points (<10) to be used for in-context learning. We show that it’s possible to generate two to three orders of magnitude more data while preserving quality and privacy by solving issues related to
Models Published 12 March 2025 Authors Carolina Parada Introducing Gemini Robotics, our Gemini 2.0-based model designed for robotics At Google DeepMind, we’ve been making progress in how our Gemini models solve complex problems through multimodal reasoning across text, images, audio and video. So far however, those abilities have been largely confined to the digital realm.
In December we first introduced native image output in Gemini 2.0 Flash to trusted testers. Today, we’re making it available for developer experimentation across all regions currently supported by Google AI Studio. You can test this new capability using an experimental version of Gemini 2.0 Flash (gemini-2.0-flash-exp) in Google AI Studio and via the Gemini
