AI

Researchers reduce bias in AI models while preserving or improving accuracy

Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on. For instance, a model that predicts the best treatment option for someone with a chronic disease may be trained using a dataset that contains mostly male patients. That model might make incorrect predictions

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Study: Some language reward models exhibit political bias

Large language models (LLMs) that drive generative artificial intelligence apps, such as ChatGPT, have been proliferating at lightning speed and have improved to the point that it is often impossible to distinguish between something written through generative AI and human-composed text. However, these models can also sometimes generate false statements or display a political bias.

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Pixtral 12B is now available on Amazon SageMaker JumpStart

Today, we are excited to announce that Pixtral 12B (pixtral-12b-2409), a state-of-the-art vision language model (VLM) from Mistral AI that excels in both text-only and multimodal tasks, is available for customers through Amazon SageMaker JumpStart. You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models

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Talk to your slide deck using multimodal foundation models on Amazon Bedrock – Part 3

In this series, we share two approaches to gain insights on multimodal data like text, images, and charts. In Part 1, we presented an “embed first, infer later” solution that uses the Amazon Titan Multimodal Embeddings foundation model (FM) to convert individual slides from a slide deck into embeddings. We stored the embeddings in a

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Automate actions across enterprise applications using Amazon Q Business plugins

Amazon Q Business is a generative AI-powered assistant that enhances employee productivity by solving problems, generating content, and providing insights across enterprise data sources. Beyond searching indexed third-party services, employees need access to dynamic, near real-time data such as stock prices, vacation balances, and location tracking, which is made possible through Amazon Q Business plugins.

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Enabling AI to explain its predictions in plain language

Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should trust a model’s predictions. These explanations are often complex, however, perhaps containing information about hundreds of model features. And they are sometimes presented as multifaceted visualizations that can be difficult

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