10 results on this page
NVIDIA introduces Cosmos 3 Edge, a compact world model designed for on-device robot control. The 4B omni-model includes a 2B NVIDIA Nemotron-based reasoner to support adaptive policies across sensors, environments, and tasks.
AWS outlines enterprise patterns for operating many agentic AI systems across diverse frameworks, models, and providers. The guidance focuses on preserving flexibility and enabling multi-agent systems to scale together without vendor lock-in.
AWS presents its portfolio of vector search capabilities embedded in existing databases and storage services, eliminating the need for a standalone vector database or data migration. The post compares six purpose-built services and provides a framework for selecting the right vector engine.
Generative recommenders are shifting recommendation objectives from embedding similarity toward predicting a user's next action or item. The approach uses user-history sequences and large catalogs to rethink how recommendation systems are trained and served at scale.
Amid lawsuits, executive departures, and preparations for an IPO, Greg Brockman has emerged as an increasingly powerful figure at OpenAI. The article analyzes how the co-founder and current president has consolidated influence over the company’s engineering and strategic direction.
AWS demonstrates how Amazon Bedrock can add context-aware security monitoring to healthcare FHIR APIs. The approach detects anomalous access patterns, classifies data sensitivity, and generates natural-language compliance reports without adding latency to clinical workflows.
The FDA has approved Aletta, a robot that can locate a patient's vein, insert a needle, and draw blood without direct human assistance. The system could help address shortages of trained phlebotomy staff.
Waymo has developed a custom chip designed to improve the performance of its robotaxis. The chip also reduces the company’s reliance on third-party suppliers such as Nvidia.
Chinese AI companies are optimising software to handle growing inference demand while access to high-end Nvidia processors remains limited. Domestic chips can support some inference workloads, but complex coding tasks still depend partly on scarce Nvidia capacity.
Apple Machine Learning studies how to mix scarce target-domain data with abundant generic data during language-model pretraining. Across more than 2,000 training runs, the research examines the trade-off between insufficient target exposure, repeated examples, diminishing returns, and overfitting.