In this evolving digital landscape, Quantum-related technologies are gaining significant attention in recent years in the field of cybersecurity. Understanding how quantum random number generators can bolster security at the edge is great news for any company concerned about protecting data integrity and confidentiality in an increasingly complex environment.
The use of AI in Edge Computing opens up exciting opportunities across industries, offering benefits like real-time decision-making, low latency inferencing, and enhanced data security. However, quantifying these benefits and demonstrating tangible returns on investment remains a challenge for many companies.
At Barbara, we believe that industry transformation can not be accomplished by a single company. Our view is of a Community of AI developers, hardware manufacturers, industrial system integrators and other technology partners, that join forces with us to make the most of our technology so that we can altogether respond with a comprehensive solutions to the most complex problems.
When we talk about "water sustainability", experts use some fundamental indicators. For us, the most important is the resilience of their infrastructures, i.e. how prepared they are for adverse situations, and the efficiency of their processes, measured as the number of resources to be invested in meeting a specific demand. AI plays a fundamental role in these processes.
Artificial Intelligence (AI) is the greatest catalyst for technological innovation in history, with countless applications in the energy sector. Key examples are the optimisation of production and storage, smart market trading, consumer savings and efficient consumption models for reducing carbon footprints, among others.
As the market moves from proof of concepts to large multi-application deployments that require scalability, different technological alternatives emerge at the Edge. In this article, we explore the foundation for a successful Edge Computing project.
Edge computing, or the ability to store and process data on servers close to the source or destination of that data, is becoming increasingly fashionable. The IT world has been using it for years with regional data centers that serve content more efficiently. What is new today is the beginning of its incursion into industrial environments.
Industrial IoT is transforming the way plant environments operate by enabling hyper-connected networks that provide value through smart factories, predictive maintenance, energy management, remote monitoring, and more. This technology empowers industries to optimize productivity, increase efficiency, and reduce costs.
The digital twin is one of the main topics of discussion about connected industry or industry 4.0. There are sectors where they are now not only a reality, but a key part of operations. Manufacturing uses digital twins in their day-to-day operations, helping to operate machinery, monitor material, predict behaviour, or plan tasks, using a virtual copy of the systems involved, and thus saving hundreds of field visits.
In this future context, where neither production nor distribution are optimally guaranteed, all eyes are on the so-called Active Demand Management or "Demand Response" mechanisms, which could be translated into English as "demand response".
What differentiates the industrial world (OT) from the IT world and why is the feeling of insecurity increasingly high in industrial companies? The answer lies in the inability of industrial companies to update software and firmware.