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Presented by Arm AI is no longer confined to the cloud or data centers. Increasingly, it’s running directly where data is created — in devices, sensors, and networks at the edge. This shift toward on-device intelligence is being driven by latency, privacy, and cost concerns that companies are confronting as they continue their investments in…

Google Cloud has introduced a big update in a bid to keep AI developers on its Vertex AI platform for concepting, designing, building, testing, deploying and modifying AI agents in enterprise use cases. The new features, announced today, include additional governance tools for enterprises and expanding the capabilities for creating agents with just a few…

The latest big headline in AI isn’t model size or multimodality — it’s the capacity crunch. At VentureBeat’s latest AI Impact stop in NYC, Val Bercovici, chief AI officer at WEKA, joined Matt Marshall, VentureBeat CEO, to discuss what it really takes to scale AI amid rising latency, cloud lock-in, and runaway costs. Those forces,…

Presented by Elastic Logs set to become the primary tool for finding the “why” in diagnosing network incidents Modern IT environments have a data problem: there’s too much of it. Organizations that need to manage a company’s environment are increasingly challenged to detect and diagnose issues in real-time, optimize performance, improve reliability, and ensure security…

The intelligence of AI models isn’t what’s blocking enterprise deployments. It’s the inability to define and measure quality in the first place. That’s where AI judges are now playing an increasingly important role. In AI evaluation, a “judge” is an AI system that scores outputs from another AI system.  Judge Builder is Databricks’ framework for…

When the transformer architecture was introduced in 2017 in the now seminal Google paper “Attention Is All You Need,” it became an instant cornerstone of modern artificial intelligence. Every major large language model (LLM) — from OpenAI’s GPT series to Anthropic’s Claude, Google’s Gemini, and Meta’s Llama — has been built on some variation of…

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can’t answer basic analytical questions about their document repositories. The culprit isn’t model quality but architecture: Traditional retrieval augmented generation (RAG) systems were designed to retrieve and summarize, not analyze and aggregate across large document sets. Snowflake…

Market researchers have embraced artificial intelligence at a staggering pace, with 98% of professionals now incorporating AI tools into their work and 72% using them daily or more frequently, according to a new industry survey that reveals both the technology’s transformative promise and its persistent reliability problems. The findings, based on responses from 219 U.S….

SAP aims to displace more general large language models with the release of its own foundational “tabular” model, which the company claims will reduce training requirements for enterprises.  The model, called SAP RPT-1, is a pre-trained model with business and enterprise knowledge out of the box. SAP calls it a Relational Foundation Model, meaning it…

A quiet revolution is reshaping enterprise data engineering. Python developers are building production data pipelines in minutes using tools that would have required entire specialized teams just months ago. The catalyst is dlt, an open-source Python library that automates complex data engineering tasks. The tool has reached 3 million monthly downloads and powers data workflows…