AI

Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. You can find this checkout-free technology in over 180 third-party locations worldwide, including travel retailers, sports stadiums, entertainment venues, conference centers, […]

The pharmaceutical manufacturing industry has long struggled with the issue of monitoring the characteristics of a drying mixture, a critical step in producing medication and chemical compounds. At present, there are two noninvasive characterization approaches that are typically used: A sample is either imaged and individual particles are counted, or researchers use a scattered light

Evaluating your Retrieval Augmented Generation (RAG) system to make sure it fulfils your business requirements is paramount before deploying it to production environments. However, this requires acquiring a high-quality dataset of real-world question-answer pairs, which can be a daunting task, especially in the early stages of development. This is where synthetic data generation comes into

State and local agencies spend approximately $1.23 billion annually to operate and maintain signalized traffic intersections. On the other end, traffic congestion at intersections costs drivers about $22 billion annually. Implementing an artificial intelligence (AI)-powered detection-based solution can significantly mitigate congestion at intersections and reduce operation and maintenance costs. In this blog post, we show you

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. Data scientists face numerous challenges throughout this process, such as selecting appropriate tools, needing step-by-step instructions with code samples, and troubleshooting errors and issues. These iterative challenges can hinder progress and slow down projects.

With the rise of powerful foundation models (FMs) powered by services such as Amazon Bedrock and Amazon SageMaker JumpStart, enterprises want to exercise granular control over which users and groups can access and use these models. This is crucial for compliance, security, and governance. Launched in 2021, Amazon SageMaker Canvas is a visual point-and-click service

This post is co-written with Josh Zook and Alex Hamilton from Rocket Mortgage. Rocket Mortgage, America’s largest retail mortgage lender, revolutionizes homeownership with Rocket Logic – Synopsis, an AI tool built on AWS.  This innovation has transformed client interactions and operational efficiency through the use of Amazon Transcribe Call Analytics, Amazon Comprehend, and Amazon Bedrock.

“The path we’re on isn’t sustainable,” Cloudflare CEO Matthew Prince tells WIRED, in reference to rampant AI scraping. Here’s his plan to course-correct.

Amazon Bedrock Agents offers developers the ability to build and configure autonomous agents in their applications. These agents help users complete actions based on organizational data and user input, orchestrating interactions between foundation models (FMs), data sources, software applications, and user conversations. Amazon Bedrock agents use the power of large language models (LLMs) to perform

AI has scrambled our ability to tell what’s real and what’s synthetic. But there are tools and techniques to help stay grounded in truth.