I'll explore data-related challenges, the increasing importance of a robust data strategy and considerations for businesses ...

Anders Fryxell, chief sales officer at atNorth, examines how the Nordic region has emerged as a global leader in A.I.-ready digital infrastructure, at a moment when hyperscalers and enterprises ...

In today’s AI gold rush, the startups that win aren’t just the ones with the best models—they’re the ones with the strongest data foundations. As AI-native companies race to productize intelligence, ...

Enterprise AI can’t scale without a semantic core. The future of AI infrastructure will be built on semantics, not syntax.

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Deloitte projects that by 2035, power needs for AI data centers in the US could surge more than thirtyfold. The demand for computing power has turned data centers into one of the fastest-moving ...

India plans to treat data as a national resource, something that can power the next wave of artificial intelligence (AI) ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

“AI readiness is not fundamentally a model problem. It is a data and metadata issue,” Garg said.

Agent Bricks uses technology developed by MosaicML’s research division to optimize the agent development process and help control the cost of running agents. Databricks has released a beta version of ...