AI

This episode of Uncanny Valley covers the people resigning from AI companies and the humans getting hired by AI agents. Plus, we attend a soiree thrown by a conservative women’s magazine.

In precision medicine, researchers developing diagnostic tests for early disease detection face a critical challenge: datasets containing thousands of potential biomarkers but only hundreds of patient samples. This curse of dimensionality can determine the success or failure of breakthrough discoveries. Modern bioinformatics use multiple omic modalities—genomics, lipidomics, proteomics, and metabolomics—to develop early disease detection tests.

This blog post was co-authored with Johannes Maunz, Tobias Bösch Borgards, Aleksander Cisłak, and Bartłomiej Gralewicz from Hexagon. Hexagon is the global leader in measurement technologies and provides the confidence that vital industries rely on to build, navigate, and innovate. From microns to Mars, Hexagon’s solutions drive productivity, quality, safety, and sustainability across aerospace, agriculture,

This post is cowritten by Jeff Boudier, Simon Pagezy, and Florent Gbelidji from Hugging Face. Agentic AI systems represent an evolution from conversational AI to autonomous agents capable of complex reasoning, tool usage, and code execution. Enterprise applications benefit from strategic deployment approaches tailored to specific needs. These needs include managed endpoints, which deliver auto-scaling capabilities, foundation

You can avoid Google’s AI summaries in your search results by simply adjusting your query. Or just switch search engines altogether.

In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance

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?