​[[{“value”:”This paper was accepted at the Workshop on Regulatable ML (ReML) at NeurIPS 2025. Recent developments in AI governance and safety research have called for red-teaming methods that can effectively surface potential risks posed by AI models. Many of these calls have emphasized how the… Read More »PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming Apple Machine Learning Research

​[[{“value”:” Today, we are excited to announce support for DoWhile loops in Amazon Bedrock Flows. With this powerful new capability, you can create iterative, condition-based workflows directly within your Amazon Bedrock flows, using Prompt nodes, AWS Lambda functions, Amazon Bedrock Agents, Amazon Bedrock Flows inline… Read More »DoWhile loops now supported in Amazon Bedrock Flows Shubhankar Sumar Artificial Intelligence

​[[{“value”:” This post was written with Lucas Dahan, Dil Dolkun, and Mathew Ng from PropHero. PropHero is a leading property wealth management service that democratizes access to intelligent property investment advice through big data, AI, and machine learning (ML). For the Spanish and Australian consumer… Read More »How PropHero built an intelligent property investment advisor with continuous evaluation using Amazon Bedrock Adithya Suresh Artificial Intelligence

​[[{“value”:” In the benefits administration industry, claims processing is a vital operational pillar that makes sure employees and beneficiaries receive timely benefits, such as health, dental, or disability payments, while controlling costs and adhering to regulations like HIPAA and ERISA. Businesses aim to optimize the… Read More »Accelerate benefits claims processing with Amazon Bedrock Data Automation Saurabh Kumar Artificial Intelligence

​New era of physical agents will help robots perceive, plan, think, use tools and act to solve complex tasks. New era of physical agents will help robots perceive, plan, think, use tools and act to solve complex tasks.  Read More  

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​[[{“value”:” The productivity gains from AI tools are undeniable. Development teams are shipping faster, marketing campaigns are launching quicker, and deliverables are more polished than ever. But if you’re a technology leader watching these efficiency improvements, you might want to ask yourself a harder question:… Read More »Why AI Efficiency May Be Making Your Organization More Fragile Brinda Sarathy and Rajeshwari Ganesan AI & ML – Radar

​[[{“value”:” Meta FAIR released Code World Model (CWM), a 32-billion-parameter dense decoder-only LLM that injects world modeling into code generation by training on execution traces and long-horizon agent–environment interactions—not just static source text. What’s new: learning code by predicting execution? CWM mid-trains on two large… Read More »Meta FAIR Released Code World Model (CWM): A 32-Billion-Parameter Open-Weights LLM, to Advance Research on Code Generation with World Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We begin by preparing and modeling the diabetes dataset, then we dive into evaluation, feature importance, and partial dependence. Along the… Read More »How to Build an End-to-End Data Science Workflow with Machine Learning, Interpretability, and Gemini AI Assistance? Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Most RAG failures originate at retrieval, not generation. Text-first pipelines lose layout semantics, table structure, and figure grounding during PDF→text conversion, degrading recall and precision before an LLM ever runs. Vision-RAG—retrieving rendered pages with vision-language embeddings—directly targets this bottleneck and shows material end-to-end gains… Read More »Vision-RAG vs Text-RAG: A Technical Comparison for Enterprise Search Michal Sutter Artificial Intelligence Category – MarkTechPost