AI

In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate […]

Anthropic doesn’t want its AI used in autonomous weapons or government surveillance. Those carve-outs could cost it a major military contract.

Amazon Quick supports Model Context Protocol (MCP) integrations for action execution, data access, and AI agent integration. You can expose your application’s capabilities as MCP tools by hosting your own MCP server and configuring an MCP integration in Amazon Quick. Amazon Quick acts as an MCP client and connects to your MCP server endpoint to access

Massive data centers for generative AI are bad for the Earth. How about launching them into orbit?

Presearch’s “Doppelgänger” is trying to help people discover adult creators rather than use nonconsensual deepfakes.

Large language models (LLMs) have been championed as tools that could democratize access to information worldwide, offering knowledge in a user-friendly interface regardless of a person’s background or location. However, new research from MIT’s Center for Constructive Communication (CCC) suggests these artificial intelligence systems may actually perform worse for the very users who could most

The Boston startup uses AI to translate and verify legacy software for defense contractors, arguing modernization can’t come at the cost of new bugs.

The AI search startup once predicted advertising would be a massive business. Now it’s betting on a smaller, more valuable audience.

By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they’re far from simple answer-generators; they can also express abstract concepts, such as certain tones, personalities, biases, and moods. However, it’s not obvious exactly how these models represent abstract concepts to begin with from the knowledge they contain. Now

As artificial intelligence and machine learning (AI/ML) workflows grow in scale and complexity, it becomes harder for practitioners to organize and deploy their models. AI projects often struggle to move from pilot to production. AI projects often fail not because models are bad, but because infrastructure and processes are fragmented and brittle, and the original