As enterprises embrace distributed cloud environments, SD-WAN is evolving beyond traditional automation. The next frontier is AI-driven SD-WAN, powered by Agentic AI that are not only intelligent, but autonomous, context-aware, and capable of making real-time decisions.

The emergence of Large Language Models (LLMs) has ignited significant discussion within the legal community, especially concerning their role in preparing patent applications. Driven by the fast-paced evolution of artificial intelligence and machine learning, this conversation reflects broader changes reshaping numerous sectors—including the legal field. With their ability to automate and improve legal tasks like patent drafting, LLMs offer substantial potential that is increasingly hard to overlook.

The global transition to renewable energy and the exponential growth of AI datacenters are placing new demands on smart power grid infrastructure. While renewable sources offer a sustainable path forward, they also bring variability and unpredictability to power generation. Simultaneously, the rise of generative AI and machine learning workloads is driving up electricity demand significantly. According to the International Energy Agency (IEA), the power consumption of U.S. AI datacenters is projected to reach approximately 34 GW by 2030, doubling from around 17 GW in 2023. This dual challenge—unstable renewable generation and energy-hungry AI infrastructure—is catalyzing large-scale modernization across global power substations and transmission systems.

Operating in remote and rugged environments is challenging on its own, but inconsistent network connectivity adds a serious layer of risk, jeopardizing both worker safety and operational efficiency. A petroleum refinery found itself grew increasingly dependent on connected devices to maintain smooth and safe operations; unreliable connectivity, however, frequently disrupted workflows and hindered performance.

In today’s competitive manufacturing landscape, ensuring consistent assembly quality and maintaining workplace safety are top priorities. Manual processes or hybrid human-machine workflows often introduce variability and risk that traditional monitoring systems fail to catch in real time.

Cryptanalytically relevant quantum computing (CRQC) refers to quantum computers powerful enough to break all currently used public-key cryptographic systems.

While this threat, commonly known as Q-Day, has not yet materialized due to current hardware limitations, many experts agree it's only a matter of time. In response, governments and proactive organizations are already preparing for the shift to post-quantum cryptography (PQC).

Urban mobility is increasingly strained by congestion, traffic violations, and inefficient parking—challenges that demand smarter, real-time traffic management. Lanner, together with DataFromSky, offers an advanced AI-powered solution that turns standard IP cameras into intelligent traffic sensors. By processing video at the edge, directly inside traffic controllers, this joint solution delivers immediate insights and automated control capabilities for modern cities.

第 1 頁,共 53 頁