[[{“value”:” We can’t not talk about power these days. We’ve been talking about it ever since the Stargate project, with half a trillion dollars in datacenter investment, was floated early in the year. We’ve been talking about it ever since the now-classic “Stochastic Parrots” paper.… Read More »Megawatts and Gigawatts of AI Mike Loukides AI & ML – Radar
[[{“value”:” Alibaba Cloud’s Qwen team unveiled Qwen3-ASR Flash, an all-in-one automatic speech recognition (ASR) model (available as API service) built upon the strong intelligence of Qwen3-Omni that simplifies multilingual, noisy, and domain-specific transcription without juggling multiple systems. Key Capabilities Multilingual recognition: Supports automatic detection and… Read More »Alibaba Qwen Team Releases Qwen3-ASR: A New Speech Recognition Model Built Upon Qwen3-Omni Achieving Robust Speech Recogition Performance Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Why Do Sequential LLMs Hit a Bottleneck? Test-time compute scaling in LLMs has traditionally relied on extending single reasoning paths. While this approach improves reasoning for a limited range, performance plateaus quickly. Experiments on DeepSeek-R1-distill-Qwen-1.5B show that increasing token budgets beyond 32K (up to… Read More »ParaThinker: Scaling LLM Test-Time Compute with Native Parallel Thinking to Overcome Tunnel Vision in Sequential Reasoning Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” We are excited to announce the general availability of fine-grained compute and memory quota allocation with HyperPod task governance. With this capability, customers can optimize Amazon SageMaker HyperPod cluster utilization on Amazon Elastic Kubernetes Service (Amazon EKS), distribute fair usage, and support efficient resource allocation across… Read More »Maximize HyperPod Cluster utilization with HyperPod task governance fine-grained quota allocation Siamak Nariman Artificial Intelligence
[[{“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