Building machine learning models in high-stakes contexts like finance, healthcare, and critical infrastructure often demands robustness, explainability, and other domain-specific constraints.
We recently announced the support for Agent-to-Agent (A2A) protocol on Amazon Bedrock AgentCore Runtime. With this addition, agents can discover peers, share capabilities, and coordinate actions across platforms using standardized communication. Amazon Bedrock AgentCore Runtime provides a secure, serverless environment designed for deploying AI agents and tools. It works with any framework and model, supports …
Read more “Introducing agent-to-agent protocol support in Amazon Bedrock AgentCore Runtime”
Embeddings are a crucial component at the intersection of data and AI. As data structures, they encode the inherent meaning of the data they represent, and their significance becomes apparent when they are compared to one another. Vector search is a technique that uncovers the relative meaning of those embeddings by evaluating the distances between …
Read more “BigQuery under the hood: How Google brought embeddings to analytics”
Baidu Inc., China’s largest search engine company, released a new artificial intelligence model on Monday that its developers claim outperforms competitors from Google and OpenAI on several vision-related benchmarks despite using a fraction of the computing resources typically required for such systems. The model, dubbed ERNIE-4.5-VL-28B-A3B-Thinking, is the latest salvo in an escalating competition among …
Read more “Baidu just dropped an open-source multimodal AI that it claims beats GPT-5 and Gemini”
Nike’s high-end recovery sneakers are on sale—just in time for ski season.
Imagine you’re watching a movie, in which a character puts a chocolate bar in a box, closes the box and leaves the room. Another person, also in the room, moves the bar from a box to a desk drawer. You, as an observer, know that the treat is now in the drawer, and you also …
Read more “Mind readers: How large language models encode theory-of-mind”
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When large language models first came out, most of us were just thinking about what they could do, what problems they could solve, and how far they might go.
Extracting structured data from documents like invoices, receipts, and forms is a persistent business challenge. Variations in format, layout, language, and vendor make standardization difficult, and manual data entry is slow, error-prone, and unscalable. Traditional optical character recognition (OCR) and rule-based systems often fall short in handling this complexity. For instance, a regional bank might …
Read more “Fine-tune VLMs for multipage document-to-JSON with SageMaker AI and SWIFT”
As Large Language Models (LLMs) evolve, Reinforcement Learning (RL) is becoming the crucial technique for aligning powerful models with human preferences and complex task objectives. However, enterprises that need to implement and scale RL for LLMs are facing infrastructure challenges. The primary hurdles include the memory contention from concurrently hosting multiple large models (such as …
Read more “Running high-scale reinforcement learning (RL) for LLMs on GKE”