Tag: faang

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”

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”

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. …

Read more “What’s new in Gemini Enterprise Agent Platform”

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”