By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an engineering challenge and also exhibit undesirable traits.
Deep insights for enterprise AI, data, and security leaders
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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.
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.
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.
For more than three decades, modern CPUs have relied on speculative execution to keep pipelines full. When it emerged in the 1990s, speculation was hailed as a breakthrough — just as pipelining and superscalar execution had been in earlier decades. Each marked a generational leap in microarchitecture. By predicting the outcomes of branches and memory loads, processors could avoid stalls and keep execution units busy.
Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.
Integrating AI models directly into extended detection and response (XDR) platforms is delivering breakthrough improvements in SOC investigation speed and accuracy.
Every SOC leader knows the feeling: drowning in alerts, blind to the real threat, stuck playing defense in a war waged at the speed of AI.
OpenAI has introduced Aardvark, a GPT-5-powered autonomous security researcher agent now available in private beta.
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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.
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.
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.
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I’m thrilled to announce a fantastic new addition to our leadership team: Karyne Levy is joining VentureBeat as our new Managing Editor. Today is her first day.
The buzzed-about but still stealthy New York City startup Augmented Intelligence Inc (AUI), which seeks to go beyond the popular "transformer" architecture used by most of today's LLMs such as ChatGPT and Gemini, has raised $20 million in a bridge SAFE round at a $750 million valuation cap, bringing its total funding to nearly $60 million, VentureBeat can exclusively reveal.
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.
The recent controversy surrounding Google’s Gemma model has once again highlighted the dangers of using developer test models and the fleeting nature of model availability.
