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​[[{“value”:” This blog post is co-written with Jacky Leybman from PagerDuty. As organizations scale their digital operations, they face unprecedented challenges in managing and extracting value from their vast data ecosystems, particularly when it comes to data accessibility and quality. The complexity of modern IT… Read More »Extend your Amazon Q Business with PagerDuty Advance data accessor Jacky Leybman AWS Machine Learning Blog

​[[{“value”:” In the context of distributed systems and microservices architecture, orchestrating communication between diverse components presents significant challenges. However, with the launch of Amazon Bedrock Agents, the landscape is evolving, offering a simplified approach to agent creation and seamless integration of the return of control… Read More »Innovate business logic by implementing return of control in Amazon Bedrock Agents Mohammed Asadulla Baig AWS Machine Learning Blog

​[[{“value”:” Home Table of Contents Video Understanding and Grounding with Qwen 2.5 Enhanced Video Comprehension Ability in Qwen 2.5 Models Dynamic Frame Rate (FPS) and Absolute Time Encoding Multimodal Rotary Position Embedding (MRoPE) Robustness Through Training Innovations Hands-On Qwen2.5 for Video Understanding Tasks Setting Up… Read More »Video Understanding and Grounding with Qwen 2.5 Puneet Mangla PyImageSearch

​Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand and explain their predictions. Machine learning workflows often involve a delicate balance: you want models that perform exceptionally well, but you also need to understand… Read More »How to Combine Scikit-learn, CatBoost, and SHAP for Explainable Tree Models Vinod Chugani MachineLearningMastery.com

​[[{“value”:” This is the last of four parts in this series. Part 1 can be found here, Part 2 here, and Part 3 here. 9. Future Directions and Wishlist for MCP The trajectory of MCP and AI tool integration is exciting, and there are clear… Read More »MCP: What It Is and Why It Matters—Part 4 Addy Osmani AI & ML – Radar

​[[{“value”:” Rethinking Audio-Based Human-Computer Interaction Machines that can respond to human speech with equally expressive and natural audio have become a major goal in intelligent interaction systems. Audio-language modeling extends this vision by combining speech recognition, natural language understanding, and audio generation. Rather than relying… Read More »StepFun Introduces Step-Audio-AQAA: A Fully End-to-End Audio Language Model for Natural Voice Interaction Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a… Read More »EPFL Researchers Unveil FG2 at CVPR: A New AI Model That Slashes Localization Errors by 28% for Autonomous Vehicles in GPS-Denied Environments Jean-marc Mommessin Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes… Read More »OThink-R1: A Dual-Mode Reasoning Framework to Cut Redundant Computation in LLMs Sana Hassan Artificial Intelligence Category – MarkTechPost

​This post is divided into five parts; they are: • Understanding Positional Encodings • Sinusoidal Positional Encodings • Learned Positional Encodings • Rotary Positional Encodings (RoPE) • Relative Positional Encodings Consider these two sentences: “The fox jumps over the dog” and “The dog jumps over… Read More »Positional Encodings in Transformer Models Adrian Tam MachineLearningMastery.com