AI & Machine Learning

TL;DR An AI gateway is only useful in production when it has an operating model around it. The gateway can route model calls, enforce token limits, apply policy, collect telemetry, and control tool access, but those controls do not define themselves. The practical work is deciding who owns routes, who approves model and tool access,

The release of Muse Glimmer, a model that can be freely downloaded and modified, is likely to intensify a debate over whether A.I. should be restricted or not.

Meta’s founder casts OpenAI and Anthropic as foils in his pitch for powerful AI to become more freely available

As data centers gobble up memory chips, other industries that need the components, including electronics companies like Apple and medical device makers, are asking for government help.

A.I. has become a kind of social sealant, filling the gaps in young people’s relationships.

A wave of new services uses A.I. to monitor flights and hotel reservations to help you find lower prices. We look at how these tools work.

As colleges and students embrace virtual classes, the ease of A.I. cheating is raising questions about the value of an online degree.

TL;DR Microsoft Azure Arc extends the Azure management plane to supported servers, Kubernetes clusters, virtual infrastructure, data services, and multicloud resources that operate outside Azure. It can create a more consistent inventory, governance, security, monitoring, and lifecycle-management experience across a distributed estate. The image captures that mission-control vision well, but the dashboard is the final

TL;DR Boards should not approve “AI at scale” as a broad technology initiative. They should approve a bounded portfolio of AI use cases with measurable value, named owners, governed data, production-ready architecture, constrained authority, tested controls, workforce readiness, and a credible exit path. This scorecard gives CEOs and boards 12 questions to ask before enterprise