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​[[{“value”:” Basic AI chat isn’t enough for most business applications. Institutions need AI that can pull from their databases, integrate with their existing tools, handle multi-step processes, and make decisions independently. This post demonstrates how to quickly build sophisticated AI agents using Strands Agents, scale… Read More »Build and scale adoption of AI agents for education with Strands Agents, Amazon Bedrock AgentCore, and LibreChat Changsha Ma Artificial Intelligence

​[[{“value”:” This post was written with Lior Heber and Yarden Ron of Skai. Skai (formerly Kenshoo) is an AI-driven omnichannel advertising and analytics platform designed for brands and agencies to plan, launch, optimize, and measure paid media across search, social, retail media marketplaces and other… Read More »Skai uses Amazon Bedrock Agents to significantly improve customer insights by revolutionized data access and analysis Lior Heber, Yarden Ron Artificial Intelligence

​[[{“value”:” This post was written with Felipe Monroy, Ana Jaime, and Nikita Gordeev from Snoonu. Managing a massive product catalog in the ecommerce space has introduced new hurdles for retailers who are trying to efficiently connect customers with the items they truly want. Traditional one-size-fits-all… Read More »The power of AI in driving personalized product discovery at Snoonu Felipe Monroy, Ana Jaime, Nikita Gordeev Artificial Intelligence

​[[{“value”:” Home Table of Contents Post Training Qwen3 for Math Reasoning Using GRPO Group Relative Policy Optimization (GRPO) Challenges with Proximal Policy Optimization (PPO)? Computational Overhead and Memory Requirements Value Function Instability and Representation Collapse Hyperparameter Sensitivity and Training Instability Bias in Value Function Estimation… Read More »Post Training Qwen3 for Math Reasoning Using GRPO Puneet Mangla PyImageSearch

​Validating machine learning models requires careful testing on unseen data to ensure robust, unbiased estimates of their performance. Validating machine learning models requires careful testing on unseen data to ensure robust, unbiased estimates of their performance.  Read More  

​[[{“value”:” I’ve been in a few recent conversations about whether to use Apache Beam on its own or run it with Google Dataflow. On the surface, it’s a tooling decision. But it also reflects a broader conversation about how teams build systems. Beam offers a… Read More »A “Beam Versus Dataflow” Conversation Aaron Black AI & ML – Radar

​[[{“value”:” Table of contents What is catastrophic forgetting in foundation models? Why does online reinforcement learning forget less than supervised fine-tuning? How can forgetting be measured? What do experiments on large language models reveal? How does RL compare to SFT in robotics tasks? What insights… Read More »A New MIT Study Shows Reinforcement Learning Minimizes Catastrophic Forgetting Compared to Supervised Fine-Tuning Michal Sutter Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Table of contents Why is long context such a bottleneck for LLMs? How does REFRAG compress and shorten context? How is acceleration achieved? How does REFRAG preserve accuracy? What do the experiments reveal? Summary FAQs A team of researchers from Meta Superintelligence Labs, National… Read More »Meta Superintelligence Labs Introduces REFRAG: Scaling RAG with 16× Longer Contexts and 31× Faster Decoding Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) very often generate “hallucinations”—confident yet incorrect outputs that appear plausible. Despite improvements in training methods and architectures, hallucinations persist. A new research from OpenAI provides a rigorous explanation: hallucinations stem from statistical properties of supervised versus self-supervised learning, and their… Read More »From Pretraining to Post-Training: Why Language Models Hallucinate and How Evaluation Methods Reinforce the Problem Asif Razzaq Artificial Intelligence Category – MarkTechPost