Heard of AI agents lately? We know many of you are itching to start building them! Here’s your chance with the Agent Development Kit Hackathon with Google Cloud. Everyone’s talking about AI agents, but the real magic happens when they collaborate to tackle complex tasks. Think: complex processes, data analysis, content creation, and customer support. …
This post was cowritten by Mulay Ahmed, Assistant Director of Engineering, and Ruby Donald, Assistant Director of Engineering at Principal Financial Group. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post. Principal Financial Group® is an integrated global …
At Google Cloud, we’re committed to providing the most open and flexible AI ecosystem for you to build solutions best suited to your needs. Today, we’re excited to announce our expanded AI offerings with Mistral AI on Google Cloud: Le Chat Enterprise on Google Cloud Marketplace: An AI assistant that offers enterprise search, agent builders, …
With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. However, communication overheads from popular distributed inference techniques such as Tensor Parallelism pose a significant challenge to achieve scalability and low latency. Therefore, we introduce a novel optimization technique, Sync-Point Drop …
Read more “SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models”
Improving response quality for user queries is essential for AI-driven applications, especially those focusing on user satisfaction. For example, an HR chat-based assistant should strictly follow company policies and respond using a certain tone. A deviation from that can be corrected by feedback from users. This post demonstrates how Amazon Bedrock, combined with a user …
Today, we’re expanding the choice of third-party models available in Vertex AI Model Garden with the addition of Anthropic’s newest generation of the Claude model family: Claude Opus 4 and Claude Sonnet 4. Both Claude Opus 4 and Claude Sonnet 4 are hybrid reasoning models, meaning they offer modes for near-instant responses and extended thinking …
Read more “Announcing Anthropic’s Claude Opus 4 and Claude Sonnet 4 on Vertex AI”
Training manipulation policies for humanoid robots with diverse data enhances their robustness and generalization across tasks and platforms. However, learning solely from robot demonstrations is labor-intensive, requiring expensive tele-operated data collection which is difficult to scale. This paper investigates a more scalable data source, egocentric human demonstrations, to serve as cross-embodiment training data for robot …
Authors: Sejoon Oh, Moumita Bhattacharya, Yesu Feng, Sudarshan Lamkhede, Ko-Jen Hsiao, and Justin Basilico Motivation Recommender systems have become essential components of digital services across e-commerce, streaming media, and social networks [1, 2]. At Netflix, these systems drive significant product and business impact by connecting members with relevant content at the right time [3, 4]. While …
Read more “FM-Intent: Predicting User Session Intent with Hierarchical Multi-Task Learning”
As companies increasingly adopt generative AI applications, AI agents capable of delivering tangible business value have emerged as a crucial component. In this context, integrating custom-built AI agents within chat services such as Slack can be transformative, providing businesses with seamless access to AI assistants powered by sophisticated foundation models (FMs). After an AI agent …
The explosion of digital content from social media, smartphones, and other sources has created a massive amount of unstructured data like images, videos, and documents. To help you analyze this data, BigQuery is connected with Vertex AI, Google Cloud’s powerful AI platform, so you can use advanced AI models, like Gemini 2.5 Pro/Flash, to understand …
Read more “Introducing AI.GENERATE_TABLE: creating structured data from gen AI models in BigQuery”