Editorial Director
Michael Nuñez is the Editorial Director of VentureBeat, where he leads the coverage of artificial intelligence and enterprise data. He has been an editor at Forbes, Popular Science, Gizmodo, and Mashable, and has written extensively about the social and ethical implications of technology.
The San Francisco-based startup, which builds AI systems that identify cause-and-effect relationships rather than mere correlations, is using a significant portion of the capital to deploy what it claims is one of the fastest privately owned supercomputers ever built — an Nvidia NVL72 superPOD that will power its enterprise-grade causal AI models.
Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking research released Thursday by Upwork, the largest online work marketplace.
The company announced Tuesday at its Ignite conference that it is introducing native agent infrastructure directly into Windows 11, allowing AI agents — autonomous software programs that can perform complex, multi-step tasks on behalf of users — to discover tools, execute workflows, and interact with applications through standardized protocols while operating in secure, policy-controlled environments separate from user sessions.
In the winter of 2022, as the tech world was becoming mesmerized by the sudden, explosive arrival of OpenAI’s ChatGPT, Benjamin Alarie faced a pivotal choice. His legal tech startup, Blue J, had a respectable business built on the AI of a bygone era, serving hundreds of accounting firms with predictive models. But it had hit a ceiling.
As software systems grow more complex and AI tools generate code faster than ever, a fundamental problem is getting worse: Engineers are drowning in debugging work, spending up to half their time hunting down the causes of software failures instead of building new products. The challenge has become so acute that it's creating a new category of tooling — AI agents that can diagnose production failures in minutes instead of hours.
The model, dubbed ERNIE-4.5-VL-28B-A3B-Thinking, is the latest salvo in an escalating competition among technology companies to build AI systems that can understand and reason about images, videos, and documents alongside traditional text — capabilities increasingly critical for enterprise applications ranging from automated document processing to industrial quality control.
The new features combine AI-driven analysis with what Chronosphere calls a Temporal Knowledge Graph, a continuously updated map of an organization's services, infrastructure dependencies, and system changes over time. The technology aims to address a mounting challenge in enterprise software: developers are writing code faster than ever with AI assistance, but troubleshooting remains largely manual, creating bottlenecks when applications fail.
The San Francisco-based company announced Thursday the general availability of Baseten Training, an infrastructure platform designed to help companies fine-tune open-source AI models without the operational headaches of managing GPU clusters, multi-node orchestration, or cloud capacity planning. The move is a calculated expansion beyond Baseten's core inference business, driven by what CTO Amir Haghighat describes as relentless customer demand and a strategic imperative to capture the full lifecycle of AI deployment.
The announcement, made Thursday, centers on Ironwood, Google's latest custom AI accelerator chip, which will become generally available in the coming weeks. In a striking validation of the technology, Anthropic, the AI safety company behind the Claude family of models, disclosed plans to access up to one million of these TPU chips — a commitment worth tens of billions of dollars and among the largest known AI infrastructure deals to date.
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.
An international team of researchers has released an artificial intelligence system capable of autonomously conducting scientific research across multiple disciplines — generating papers from initial concept to publication-ready manuscript in approximately 30 minutes for about $4 each.
When researchers at Anthropic injected the concept of "betrayal" into their Claude AI model's neural networks and asked if it noticed anything unusual, the system paused before responding: "I'm experiencing something that feels like an intrusive thought about 'betrayal'."