AI

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

OpenAI employees have donated more than $215,000 to a political effort opposing Leading the Future, a group backed by the company’s president, Greg Brockman.

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools.  Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with

Your prospects leave trails across multiple sources: a founder asks “What should I use for X?” in r/SaaS while their product launches on Hacker News. Stack Overflow questions spike. A GitHub repo crosses 2,400 stars. Each signal alone is noise, but correlated across sources, they reveal a prospect ready to buy. Multi-agent systems built with

Artificial intelligence has rapidly transformed software engineering. Generative AI and large language models (LLMs) can create huge volumes of code and documentation; machine-learning algorithms can monitor performance and detect security vulnerabilities. But when the task is to conceive, design, and make a complex physical system such as a jet engine, are those AI tools equally

Production quality assurance (QA) workflows require more than individual test execution. You must organize tests into regression suites that run as a batch, and integrate them into continuous integration and continuous delivery (CI/CD) pipelines so that test results gate deployments automatically. In a previous post, we introduced QA Studio, a reference solution for agentic QA

User experience (UX) testing faces multiple challenges that limit an organization’s ability to improve how users interact with their platforms. UX testing evaluates how easily and effectively users can navigate digital interfaces to complete intended tasks, such as finding products, creating accounts, or completing purchases. Unlike traditional Quality Assurance (QA) testing that focuses on functional

This post was written by Konstantin Lekh, Sasha Zinchuk, and Eugene Sergueev from Flo Health, and Liza (Elizaveta) Zinovyeva from AWS. In this post, we share how Flo Health’s engineering team turned a proof of concept (PoC) from the AWS Generative AI Innovation Center into a production-grade, AI-powered medical content review and generation system built

Healthcare organizations need efficient scheduling solutions, and ScienceSoft’s AI voice assistant, powered by Amazon Nova Sonic and Amazon Bedrock Guardrails, shows how responsible AI can deliver that. The AI patient scheduling software market is one of healthcare’s fastest-growing technology segments. According to Grand View Research, this market is growing rapidly, valued at approximately $260 million

A new study found that social media platforms are referring people to sites where they can create nonconsensual, sexually explicit deepfakes for as little as $1 an image.