AI

Generative AI continues to transform numerous industries and activities, with one such application being the enhancement of chess, a traditional human game, with sophisticated AI and large language models (LLMs). Using the Custom Model Import feature in Amazon Bedrock, you can now create engaging matches between foundation models (FMs) fine-tuned for chess gameplay, combining classical […]

The new limits, which are expected to be announced Monday, are intended to slow China’s ability to build large and powerful AI models.

Large language models (LLMs) have witnessed an unprecedented surge in popularity, with customers increasingly using publicly available models such as Llama, Stable Diffusion, and Mistral. Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. Although

Generative AI agents are designed to interact with their environment to achieve specific objectives, such as automating repetitive tasks and augmenting human capabilities. By orchestrating multistep workflows that adapt to evolving goals in real time, these agents increase productivity, reduce errors, and deliver more personalized experiences. To manage these complex workflows effectively, agents rely on

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that best suits your use case. With the Amazon Bedrock serverless experience, you can get started quickly, privately customize

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset. To demonstrate,

Thousands of companies worldwide use Salesforce to manage their sales, marketing, customer service, and other business operations. The Salesforce cloud-based platform centralizes customer information and interactions across the organization, providing sales reps, marketers, and support agents with a unified 360-degree view of each customer. With Salesforce at the heart of their business, companies accumulate vast

Hallucinations in large language models (LLMs) refer to the phenomenon where the LLM generates an output that is plausible but factually incorrect or made-up. This can occur when the model’s training data lacks the necessary information or when the model attempts to generate coherent responses by making logical inferences beyond its actual knowledge. Hallucinations arise

With the rise of large language models (LLMs) like Meta Llama 3.1, there is an increasing need for scalable, reliable, and cost-effective solutions to deploy and serve these models. AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently

The use of large language models (LLMs) and generative AI has exploded over the last year. With the release of powerful publicly available foundation models, tools for training, fine tuning and hosting your own LLM have also become democratized. Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance