The home networking arm of CEA was hard at work, approving two new standards. CEA-851-A defines an IP-enabled network for connecting cluster networks to a whole-home broadband distribution backbone in order to facilitate integrated operation of appliances and networked components. Based on IEEE 1394, this network will accommodate Ethernet as an attached network via a bridge, and directly with the introduction of IEEE 1394c. Called the versatile home network, it provides a flexible and open network architecture and communications protocol specification for digital devices in the home. CEA-2027-A defines a user-to-machine interface method that allows a source of home-network services, such as a cable or terrestrial set-top box, digital VCR or DTV, to utilize the presentation capabilities in a network-attached renderer such as a DTV display or PC. The standard enables user control of networked devices (either local to the user or remote) via another device�s (e.g., DTV or PC) Web browser graphical user interface (GUI).
Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...