TechRepublic

Why Data, Not Models, Determines AI Success

Enterprise AI success depends on data readiness for AI, including scalable architecture and reliable data pipelines. Vector databases enable AI systems to retrieve relevant information from large ...
TDWI

From Pilot to Production: Why LLM Features Stall, and a Readiness Checklist for Data Leaders

Pilots that looked promising do not always survive the transition, and the failure pattern is consistent enough that data leaders can plan around it. This article describes three failure modes that ...
Ars Technica

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...
InfoWorld

MongoDB adds vector search to self-managed editions to power generative AI apps

With vector search now available in Enterprise Server and Community Edition, enterprises can streamline AI development and reduce operational overhead by avoiding fragmented stacks and external search ...
TechRepublic

Cisco Talos Researcher Reveals Method That Causes LLMs to Expose Training Data

In this TechRepublic interview, Cisco researcher Amy Chang details the decomposition method and shares how organizations can protect themselves from LLM data extraction. Cisco Talos AI security ...