Category: Network Computing

Today’s cyber adversaries no longer need advanced expertise to carry out attacks. Although highly skilled attackers have historically achieved greater success, modern technology, the widespread availability of sophisticated tools, and reduced costs have lowered the barrier to entry. Even less experienced actors can now exploit these resources to devise new and effective methods of breaching user accounts, penetrating infrastructure, moving laterally within networks, and extracting sensitive data—often within hours rather than days.

Category: Edge AI

Aquaculture operations face significant challenges in monitoring fish growth and welfare. Traditional manual inspections are labor-intensive, time-consuming, and prone to human error. Fluctuations in water quality, environmental stress, and undetected health issues can negatively impact fish growth, welfare, and overall yield. Operators need real-time, accurate insights to make data-driven decisions that optimize fish health, improve productivity, and reduce operational costs.

Category: Edge AI

Physical AI is redefining how robots operate in real-world environments. By combining AI vision, navigation, and real-time decision-making, autonomous machines can adapt to dynamic outdoor conditions and perform complex physical tasks with precision. This evolution goes beyond traditional automation, powering new applications in agriculture, logistics, inspection, and smart facility management.

Category: Edge AI

Security screening at airports, border checkpoints, and large public venues demands both speed and precision. Traditional 2D X-ray systems often struggle to detect complex or hidden threats due to limited depth perception, resulting in blind spots, false positives, and slower inspection processes. These inefficiencies can lead to delays, reduced throughput, and increased operational costs—while still leaving potential vulnerabilities in threat detection.

Category: SD-WAN

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.

Category: Edge AI

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.

Category: Power and Energy

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.

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