U. S. Hunger's mission goes beyond delivering meals, focusing on understanding families' needs. President Rick Whitted's personal connection drives the organization's growth and innovative use of Amazon Quick for data exploration.
The Adaptive Agentic Intelligence Framework (AAIF) uses small language models (SLMs) for routine tasks and cloud models for complex requests, reducing costs and latency. AAIF's three layers - smart orchestration, local processing, and cloud processing - optimize AI processing for different task types, showcasing a flexible and cost-effective approach.
Connect on-premises data securely to Amazon Quick with private network connectivity, reducing attack surface and ensuring compliance. Amazon Quick offers AI-powered search, analytics, and automation in a private, secure environment for user and data-fetching traffic.
Train Qwen3 three ways on a single trn2. 48xlarge using torchtitan on AWS Trainium. Customize model implementation and kernels for your workload with Neuron Kernel Interface (NKI) MoE kernels.
Establishing cryptographic identity through X.509 PKI is crucial for AI agents accessing sensitive data. Keyfactor EJBCA and AWS services provide secure authentication for multi-agent AI frameworks, preventing unauthorized access and ensuring trust in machine identities.
My C# GBR matches scikit-learn's results, predicting income based on sex, age, city, height, and psychology with high accuracy. The C# implementation allows for detailed prediction explanations, showcasing its effectiveness and transparency in machine learning regression.
i14 developed Laplace 3B, a linear attention model, to address the scalability issues of traditional LLMs. Working with AWS, they optimized the model for Trainium2, achieving high training speeds and fixed memory usage for inference.
Canadian organizations utilizing Amazon Bedrock can now access the latest OpenAI models, including GPT-6, from Canadian AWS Regions with cross-Region inference. The models available in Canada offer a range of capabilities, from strong reasoning to fast, low-cost inference, supporting text and image inputs with a 1-million-token context window.
GPT-OSS-20B reasoning model thinks in English, struggles with multilingual reasoning. Fine-tuning with SFT and GRPO on Amazon SageMaker HyperPod shows promising results.
Amazon Bedrock now supports structured outputs for all Anthropic Claude models, ensuring schema-compliant JSON responses. This eliminates the need for custom parsing and post-processing, making tasks like data extraction and contract analysis more efficient and reliable.
Amazon SageMaker JumpStart offers a curated catalog of AI models, templates, and notebooks, streamlining ML model selection to deployment. Leading providers like Alibaba, NVIDIA, and Google contribute diverse models for model discovery, deployment, and customization.
TwelveLabs releases Pegasus 1.5 on Amazon Bedrock, a video language model transforming video into structured metadata. Pegasus 1.5 offers time-based metadata, multimodal prompting, and on-the-fly processing, enhancing video analysis capabilities.
German Chancellor Friedrich Merz and NVIDIA CEO Jensen Huang discuss Germany's AI roadmap at Kraftwerk, emphasizing AI applications in manufacturing and automotive. Germany aims to merge engineering expertise with AI technology, collaborating with NVIDIA to advance industrial AI models and establish a cutting-edge ecosystem in Europe.
Deepgram leverages AWS AI chips to enhance efficiency, processing 11-70% more audio per dollar for customers. Deepgram and AWS collaborate to optimize Nova-3 on Trainium2, improving inference for speech-to-text models.
Researchers at MIT and Boston Children’s Hospital developed E(3)-Pose, a deep learning model that tracks fetal brain motion during MRI scans to improve image quality and accuracy. The model embeds physics principles to enhance brain orientation identification, aiding in early detection of abnormalities in fetal brain development.