AI

Today, we’re excited to announce that Mistral-Small-24B-Instruct-2501—a twenty-four billion parameter large language model (LLM) from Mistral AI that’s optimized for low latency text generation tasks—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. Amazon Bedrock Marketplace is a new capability in Amazon Bedrock that developers can use to discover, test, and use over 100 […]

Claude 3.7, the latest model from Anthropic, can be instructed to engage in a specific amount of reasoning to solve hard problems.

Manufacturers already have the data. LLM-powered tools could help them make use of it.

Plus: Apple’s visionOS 2.4 makes it easier for guests to use the Vision Pro, Eero has two new Wi-Fi 7 routers, Oppo’s folding phone is the slimmest yet, and Omega drops a bronze Bond watch.

This post was written with Dian Xu and Joel Hawkins of Rocket Companies. Rocket Companies is a Detroit-based FinTech company with a mission to “Help Everyone Home”. With the current housing shortage and affordability concerns, Rocket simplifies the homeownership process through an intuitive and AI-driven experience. This comprehensive framework streamlines every step of the homeownership

Providing effective multilingual customer support in global businesses presents significant operational challenges. Through collaboration between AWS and DXC Technology, we’ve developed a scalable voice-to-voice (V2V) translation prototype that transforms how contact centers handle multi-lingual customer interactions. In this post, we discuss how AWS and DXC used Amazon Connect and other AWS AI services to deliver

Generative AI is revolutionizing enterprise automation, enabling AI systems to understand context, make decisions, and act independently. Generative AI foundation models (FMs), with their ability to understand context and make decisions, are becoming powerful partners in solving sophisticated business problems. At AWS, we’re using the power of models in Amazon Bedrock to drive automation of

Large language models (LLMs) excel at generating human-like text but face a critical challenge: hallucination—producing responses that sound convincing but are factually incorrect. While these models are trained on vast amounts of generic data, they often lack the organization-specific context and up-to-date information needed for accurate responses in business settings. Retrieval Augmented Generation (RAG) techniques

Fine-tuning a pre-trained large language model (LLM) allows users to customize the model to perform better on domain-specific tasks or align more closely with human preferences. It is a continuous process to keep the fine-tuned model accurate and effective in changing environments, to adapt to the data distribution shift (concept drift) and prevent performance degradation