AI

This post is co-written with Rich Young from Arize AI. Agentic AI applications built on agentic workflows differ from traditional workloads in one important way: they’re nondeterministic. That is, they can produce different results with the same input. This is because the large language models (LLMs) they’re based on use probabilities when generating each token. […]

Long before ChatGPT, a group of AI luminaries gathered on an island to discuss the future of artificial intelligence. Their funder ultimately cast a shadow on all who attended.

IT teams face mounting challenges as they manage increasingly complex infrastructure and applications, often spending countless hours manually identifying operational issues, troubleshooting problems, and performing repetitive maintenance tasks. This operational burden diverts valuable technical resources from innovation and strategic initiatives. Artificial intelligence for IT operations (AIOps) presents a transformative solution, using AI to automate operational

Organizations can optimize their migration and modernization projects by streamlining the containerization process for legacy applications. With the right tools and approaches, teams can transform traditional applications into containerized solutions efficiently, reducing the time spent on manual coding, testing, and debugging while enhancing developer productivity and accelerating time-to-market. During containerization initiatives, organizations can address compatibility,

A “gooner” tells WIRED he became hooked on the cartoonish nature of AI porn. Several addictions experts say the genre could pose a problem for people prone to compulsive sexual behavior.

Picture this: your machine learning (ML) team has a promising model to train and experiments to run for their generative AI project, but they’re waiting for GPU availability. The ML scientists spend time monitoring instance availability, coordinating with teammates over shared resources, and managing infrastructure allocation. Simultaneously, your infrastructure administrators spend significant time trying to

Developers building AI applications face a common challenge: converting unstructured data into structured formats. Structured output is critical for machine-to-machine communication use cases, because this enables downstream use cases to more effectively consume and process the generated outputs. Whether it’s extracting information from documents, creating assistants that fetch data from APIs, or developing agents that

As organizations seek to derive greater value from their AWS Support data, operational teams are looking for ways to transform raw support cases and health events into actionable insights. While traditional analytics tools can provide basic reporting capabilities, teams need more sophisticated solutions that can understand and process natural language queries about their operational data.

The Amazon Strands Agents SDK is an open source framework for building AI agents that emphasizes a model-driven approach. Instead of hardcoding complex task flows, Strands uses the reasoning abilities of modern large language models (LLMs) to handle planning and tool usage autonomously. Developers can create an agent with a prompt (defining the agent’s role

Behind closed doors, Chinese researchers are laying the groundwork for a new global AI agenda—without input from the US.