Google Cloud is introducing what it calls its most powerful artificial intelligence infrastructure to date, unveiling a seventh-generation Tensor Processing Unit and expanded Arm-based computing options designed to meet surging demand for AI model deployment — what the company characterizes as a fundamental industry shift from training models to serving them to billions of users. …
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, …
The march towards agentic enterprises continues as companies battle to keep developers on their platforms throughout the entire agent lifecycle. Google Cloud has updated its Agent Builder on Vertex AI, introducing additional governance tools for enterprises and expanding the capabilities for creating agents with just a few lines of code. Agent Builder, released last year …
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 …
From logs to insights: The AI breakthrough redefining observability Read More »
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 …
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. …
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 …
Presented by Zendesk Agentic AI is currently transforming three key areas of work — creative, coding, and support — says Shashi Upadhyay, president of engineering, AI, and product at Zendesk. But he notes that support presents a distinct challenge. “Support is special because you’re putting an autonomous AI agent right in front of your customer,” …
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 …
Forget Fine-Tuning: SAP’s RPT-1 Brings Ready-to-Use AI for Business Tasks Read More »