Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

The results revealed a startling phenomenon - the “collaboration gap.” Models that solved the maze easily on their own often failed when paired with an identical copy. In many cases, smaller or ...

“Edge computing also means less data travels long distances, lowering the load on main servers and networks,” says Neel ...

Organisations like OpenAI, Google DeepMind, and Anthropic argue that bigger models bring predictable gains in reasoning and ...

Some clever networking hacks open the door AI search provider Perplexity's research wing has developed a new set of software ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

In the fast-changing digital era, the need for intelligent, scalable and robust infrastructure has never been so pronounced. Artificial intelligence is predicted as the harbinger of change, providing ...

How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems. While large language models are useful for chatbots, Q&A ...

The future of process automation and control combines the reliability of traditional DCS with the agility of cloud/IT ...

FORT LAUDERDALE, Fla., Nov. 7, 2025 /PRNewswire/ -- Intersignal, an independent AI startup based in Fort Lauderdale, today ...