At Google, AI is a soup-to-nuts endeavor. Obviously, we make leading AI models like Gemini and Nano Banana. We incorporate AI into the tools you use every day (think Gmail, BigQuery, AlloyDB, Google Cloud Code and Google Cloud Assist). We make software frameworks to help you build with AI, like Gemini Enterprise Agent Platform, JAX, …
Read more “What’s new in AI infrastructure and orchestration this month”
While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally. This graph encodes the data manifold in its original high-dimensional space, before the distortion that UMAP’s 2D projection introduces. We demonstrate the untapped potential of this internal representation, …
Read more “Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph”
Authors: Ying Li, Arjun Rao, Shradha Sehgal Introduction Recommendations sit at the heart of the Netflix experience. Our current production models rely on thousands of hand‑crafted features over users, items, and interactions, along with specialized architectures for sequence modeling, feature interactions, and multi‑task objectives. This stack has evolved over many years to support diverse content types …
Read more “GenRec: Towards LLM-Native Recommendation at Netflix”
Open weight models have become powerful enough to handle complex tasks such as multi-step agentic workflows, advanced reasoning, and long-horizon coding. However, as these models grow in capability, they also grow in size and hosting multi-trillion parameter architectures requires purpose-built infrastructure, high-end GPU compute, and optimized serving frameworks. On July 27, 2026, Moonshot AI released …
In today’s agentic era, modern cloud applications are evolving from a set of passive tools to fleets of autonomous digital workers that reason, plan, and take action across a wide range of tasks. For platform engineering teams designing these environments, the simplest approach is often to deploy an agent on to an open-source framework like …
Read more “Do more with less: How GKE can reduce your cost per agent by 75%”
Siri Expressive Voices synthesize rich, configurable speech in real time and entirely on device, powered by AFM 3 Core Advanced, Apple’s most powerful on-device foundation model. This work presents the memory-efficient audio synthesis architecture behind that capability: a detokenizer that converts the semantic audio tokens emitted by the foundation model into high-fidelity audio within the …
Read more “Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers”
Amazon Bedrock AgentCore Identity now supports Private Key JWT client authentication for agents. With Private Key JWT client authentication, your agents can authenticate to a downstream identity provider’s token endpoint using a signed JSON Web Token (JWT) client assertion instead of a shared OAuth 2.0 client secret. You can register a public key with your …
Read more “Authenticate with Private Key JWT using Amazon Bedrock AgentCore Identity”
Since we launched Gemini Enterprise Agent Platform a few months ago, we’ve seen inspiring progress from businesses and builders alike. To stir up development, we’ve also shared 13 demos that can walk you through the versatility and power of Agent Platform, and 20 questions you can ask your teams about building a solid agentic foundation. …
Increasing the adoption of generative AI across the enterprise requires you to do more than deploy a generic chatbot with a custom wrapper. Interacting with business-critical databases demands absolute trust, strict governance, and deep grounding in enterprise semantics. Over the last year, Conversational Analytics (CA) in Google Cloud has moved from isolated experiments to scaled, …
Read more “Bringing Conversational Analytics to your entire data ecosystem”
Systematic failures of vision models on semantically coherent subsets, known as error slices, reveal limitations in robustness and evaluation. Existing slice discovery approaches largely model slices as clusters in representation space or combinations of predefined attributes. While effective for image-level classification, such formulations are insufficient for instance-level tasks such as object detection and segmentation, where …
Read more “GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks”