Category: News

University of California Los Angeles delivers an immersive theater experience with AWS generative AI servicesArtificial Intelligence In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences.

 In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences. Read More  

Optimizing Mobileye’s REM™ with AWS Graviton: A focus on ML inference and Triton integrationArtificial Intelligence In this post, we focus on one portion of the REM™ system: the automatic identification of changes to the road structure which we will refer to as Change Detection. We will share our journey of architecting and deploying a solution for Change Detection, the core of which is a deep learning model called CDNet. We will share real-life decisions and tradeoffs when building and deploying a high-scale, highly parallelized algorithmic pipeline based on a Deep Learning (DL) model, with an emphasis on efficiency and throughput.

 In this post, we focus on one portion of the REM™ system: the automatic identification of changes to the road structure which we will refer to as Change Detection. We will share our journey of architecting and deploying a solution for Change Detection, the core of which is a deep learning model called CDNet. We will share real-life decisions and tradeoffs when building and deploying a high-scale, highly parallelized algorithmic pipeline based on a Deep Learning (DL) model, with an emphasis on efficiency and throughput. Read More  

Evaluate models with the Amazon Nova evaluation container using Amazon SageMaker AIArtificial Intelligence This blog post introduces the new Amazon Nova model evaluation features in Amazon SageMaker AI. This release adds custom metrics support, LLM-based preference testing, log probability capture, metadata analysis, and multi-node scaling for large evaluations.

 This blog post introduces the new Amazon Nova model evaluation features in Amazon SageMaker AI. This release adds custom metrics support, LLM-based preference testing, log probability capture, metadata analysis, and multi-node scaling for large evaluations. Read More  

Beyond the technology: Workforce changes for AIArtificial Intelligence In this post, we explore three essential strategies for successfully integrating AI into your organization: addressing organizational debt before it compounds, embracing distributed decision-making through the “octopus organization” model, and redefining management roles to align with AI-powered workflows. Organizations must invest in both technology and workforce preparation, focusing on streamlining processes, empowering teams with autonomous decision-making within defined parameters, and evolving each management layer from traditional oversight to mentorship, quality assurance, and strategic vision-setting.

 In this post, we explore three essential strategies for successfully integrating AI into your organization: addressing organizational debt before it compounds, embracing distributed decision-making through the “octopus organization” model, and redefining management roles to align with AI-powered workflows. Organizations must invest in both technology and workforce preparation, focusing on streamlining processes, empowering teams with autonomous decision-making within defined parameters, and evolving each management layer from traditional oversight to mentorship, quality assurance, and strategic vision-setting. Read More  

Enhanced performance for Amazon Bedrock Custom Model ImportArtificial Intelligence You can now achieve significant performance improvements when using Amazon Bedrock Custom Model Import, with reduced end-to-end latency, faster time-to-first-token, and improved throughput through advanced PyTorch compilation and CUDA graph optimizations. With Amazon Bedrock Custom Model Import you can to bring your own foundation models to Amazon Bedrock for deployment and inference at scale. In this post, we introduce how to use the improvements in Amazon Bedrock Custom Model Import.

 You can now achieve significant performance improvements when using Amazon Bedrock Custom Model Import, with reduced end-to-end latency, faster time-to-first-token, and improved throughput through advanced PyTorch compilation and CUDA graph optimizations. With Amazon Bedrock Custom Model Import you can to bring your own foundation models to Amazon Bedrock for deployment and inference at scale. In this post, we introduce how to use the improvements in Amazon Bedrock Custom Model Import. Read More  

How Myriad Genetics achieved fast, accurate, and cost-efficient document processing using the AWS open-source Generative AI Intelligent Document Processing AcceleratorArtificial Intelligence In this post, we explore how Myriad Genetics partnered with the AWS Generative AI Innovation Center to transform their healthcare document processing pipeline using Amazon Bedrock and Amazon Nova foundation models, achieving 98% classification accuracy while reducing costs by 77% and processing time by 80%. We detail the technical implementation using AWS’s open-source GenAI Intelligent Document Processing Accelerator, the optimization strategies for document classification and key information extraction, and the measurable business impact on Myriad’s prior authorization workflows.

 In this post, we explore how Myriad Genetics partnered with the AWS Generative AI Innovation Center to transform their healthcare document processing pipeline using Amazon Bedrock and Amazon Nova foundation models, achieving 98% classification accuracy while reducing costs by 77% and processing time by 80%. We detail the technical implementation using AWS’s open-source GenAI Intelligent Document Processing Accelerator, the optimization strategies for document classification and key information extraction, and the measurable business impact on Myriad’s prior authorization workflows. Read More  

How CBRE powers unified property management search and digital assistant using Amazon BedrockArtificial Intelligence In this post, CBRE and AWS demonstrate how they transformed property management by building a unified search and digital assistant using Amazon Bedrock, enabling professionals to access millions of documents and multiple databases through natural language queries. The solution combines Amazon Nova Pro for SQL generation and Claude Haiku for document interactions, achieving a 67% reduction in processing time while maintaining enterprise-grade security across more than eight million documents.

 In this post, CBRE and AWS demonstrate how they transformed property management by building a unified search and digital assistant using Amazon Bedrock, enabling professionals to access millions of documents and multiple databases through natural language queries. The solution combines Amazon Nova Pro for SQL generation and Claude Haiku for document interactions, achieving a 67% reduction in processing time while maintaining enterprise-grade security across more than eight million documents. Read More  

Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomographycs.AI updates on arXiv.org arXiv:2510.25522v4 Announce Type: replace-cross
Abstract: Segmentation of liver structures in multi-phase contrast-enhanced computed tomography (CECT) plays a crucial role in computer-aided diagnosis and treatment planning for liver diseases, including tumor detection. In this study, we investigate the performance of UNet-based architectures for liver tumor segmentation, starting from the original UNet and extending to UNet3+ with various backbone networks. We evaluate ResNet, Transformer-based, and State-space (Mamba) backbones, all initialized with pretrained weights. Surprisingly, despite the advances in modern architecture, ResNet-based models consistently outperform Transformer- and Mamba-based alternatives across multiple evaluation metrics. To further improve segmentation quality, we introduce attention mechanisms into the backbone and observe that incorporating the Convolutional Block Attention Module (CBAM) yields the best performance. ResNetUNet3+ with CBAM module not only produced the best overlap metrics with a Dice score of 0.755 and IoU of 0.662, but also achieved the most precise boundary delineation, evidenced by the lowest HD95 distance of 77.911. The model’s superiority was further cemented by its leading overall accuracy of 0.925 and specificity of 0.926, showcasing its robust capability in accurately identifying both lesion and healthy tissue. To further enhance interpretability, Grad-CAM visualizations were employed to highlight the region’s most influential predictions, providing insights into its decision-making process. These findings demonstrate that classical ResNet architecture, when combined with modern attention modules, remain highly competitive for medical image segmentation tasks, offering a promising direction for liver tumor detection in clinical practice.

 arXiv:2510.25522v4 Announce Type: replace-cross
Abstract: Segmentation of liver structures in multi-phase contrast-enhanced computed tomography (CECT) plays a crucial role in computer-aided diagnosis and treatment planning for liver diseases, including tumor detection. In this study, we investigate the performance of UNet-based architectures for liver tumor segmentation, starting from the original UNet and extending to UNet3+ with various backbone networks. We evaluate ResNet, Transformer-based, and State-space (Mamba) backbones, all initialized with pretrained weights. Surprisingly, despite the advances in modern architecture, ResNet-based models consistently outperform Transformer- and Mamba-based alternatives across multiple evaluation metrics. To further improve segmentation quality, we introduce attention mechanisms into the backbone and observe that incorporating the Convolutional Block Attention Module (CBAM) yields the best performance. ResNetUNet3+ with CBAM module not only produced the best overlap metrics with a Dice score of 0.755 and IoU of 0.662, but also achieved the most precise boundary delineation, evidenced by the lowest HD95 distance of 77.911. The model’s superiority was further cemented by its leading overall accuracy of 0.925 and specificity of 0.926, showcasing its robust capability in accurately identifying both lesion and healthy tissue. To further enhance interpretability, Grad-CAM visualizations were employed to highlight the region’s most influential predictions, providing insights into its decision-making process. These findings demonstrate that classical ResNet architecture, when combined with modern attention modules, remain highly competitive for medical image segmentation tasks, offering a promising direction for liver tumor detection in clinical practice. Read More  

Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Roboticscs.AI updates on arXiv.org arXiv:2501.10100v4 Announce Type: replace-cross
Abstract: Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.

 arXiv:2501.10100v4 Announce Type: replace-cross
Abstract: Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications. Read More