AI

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 […]

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

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.

The opportunity for artificial intelligence to actually do some good has arrived—if it can be redirected toward where it’s needed most.

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

Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory and MIT professor of electrical engineering and computer science, was recently named a co-recipient of the 2024 John Scott Award by the board of directors of City Trusts. This prestigious honor, steeped in historical significance, celebrates scientific innovation at the very location where American

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker, users want a seamless and secure way to experiment with and select the models that deliver the most value for their business. In the initial stages of an ML

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual,

People use AI pals for all sorts of reasons. Here’s what happens when you take one on a solo trip to Japan.

AI can tackle your email mountain so you don’t have to. Here’s how to decide whether Gmail’s new productivity feature makes sense for your workflow.