Bravida – Bravida wins contract for new data center in Norway worth approximately NOK 4.5 billion

EMR Analysis

More information on Bravida: See the full profile on EMR Executive Services

More information on Mattias Johansson (Chief Executive Officer and Group President, Bravida): See the full profile on EMR Executive Services

More information on Petra Vranjes (Chief Financial Officer, Bravida): See the full profile on EMR Executive Services

 

More information on Tore Bakke (Head of Division Norway, Bravida + Chairman, DC Nordic AS): See the full profile on EMR Executive Services

 

 

 

More information on Green Mountain: https://greenmountain.no/ + Green Mountain designs, builds and operates highly secure, innovative and sustainable data centers in Norway and the UK. The data centers are powered by low-cost, 100 percent renewable power and are world-leading on energy efficiency.

We operate data centers across key locations in Europe. In Norway, our facilities include SVG-Rennesøy near Stavanger, TEL-Rjukan in Telemark and the OSL-Enebakk data center about 20 km outside of Oslo. We have also completed and are now operating Norway’s largest data center campus in Hamar, OSL-Hamar.

Expanding our international footprint, we established LON-East in London through the acquisition of an existing company.

Further strengthening our European presence, we have partnered with German power company KMW to develop a new 54 MW data center site in the Frankfurt region, FRA-Mainz.

Existing customers include large international companies within cloud, AI, banking/finance, HPC, automotive and more.

Moreover, Green Mountain holds both a ISO 9001 Quality management and ISO 14001 Environmental management certifications.

More information on Truls Dishington (Managing Director, Nordics, Green Mountain): https://greenmountain.no/about-us/ + https://www.linkedin.com/in/trulsdishington/ 

 

 

 

More information on DC Nordic (JV Bravida & Backe Industri): No official website + Because it operates strictly as a corporate joint venture between the installation firm Bravida and the construction company Backe Industri, its operations and contract announcements are managed directly through its parent companies’ channels.

More information on Tore Bakke (Head of Division Norway, Bravida + Chairman, DC Nordic AS): See the full profile on EMR Executive Services

 

 

 

More information on Backe Group: https://backe.no/ + Ever since its establishment in 1946, Backe has been guided by the same values ​​and driven by the commitment to creating positive changes in society – for employees, customers and users. It will continue to influence us going forward.

Backe is one of Norway’s largest construction contractors and a solid player in project development.

Backe is a Norwegian, family-owned construction and property development company dedicated to creating and building the best commercial buildings and homes. Backe focuses heavily on digitalization, expertise and sustainability and wants to help develop the construction industry of the future.

More information on Eirik Gjelsvik (Group President, Backe Group): https://backe.no/selskaper/as-backe + https://www.linkedin.com/in/eirik-gjelsvik-92b1907a/ 

More information on Backe Industri by Backe Group: https://backe.no/selskaper/backe-industri-backe-industry + Backe Industri develops and implements complex construction projects with high professional expertise. The company has solid experience from demanding concrete structures, the power industry, data centers and land-based fish farms. With a solid financial group behind us, we cover all of Norway and deliver tailored solutions that meet our customers’ needs.

More information on Skjalg Lund (Chief Executive Officer, Backe Industri, Backe Group): https://backe.no/selskaper/backe-industri-backe-industry + https://www.linkedin.com/in/skjalglund/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud services typically include IaaS, PaaS, and SaaS service models.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.
    • Edge devices act as the interface between the physical world (data generation) and digital networks.

 

• Hybrid Computing: 
  • A hybrid cloud integrates private, on-premises infrastructure with public cloud services, offering flexibility to distribute workloads between these environments. Hybrid models often incorporate edge computing, allowing organizations to run critical workloads locally at the edge while using the cloud for other tasks, thereby optimizing performance, cost, and data management for various business needs.
• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.
  • HPC is typically used for simulation, scientific computing, AI training, and complex modeling.
• Data Centers – Physical Infrastructure:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers – Physical Infrastructure:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
  • Hyperscale Data Centers are typically operated by large cloud providers (e.g., hyperscalers) and designed for horizontal scalability.
• White Space and Grey Space in Data Centers – Physical Infrastructure:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.
• Colocation in Data Centers – Physical Infrastructure:
  • A colocation data center is a facility where businesses rent space, power, and cooling to house their own servers and networking hardware, rather than maintaining them in-house. It offers a cost-effective way to access high-level security, internet connectivity, and 24/7 technical support while retaining control of the equipment.
• Edge & Cloud Services – Integrated Architecture (Edge-to-Cloud): 
  • Edge services perform data processing on local devices and servers near the data source, reducing latency for time-sensitive operations, while cloud services centralize large computations and storage in remote datacenters, offering massive scalability and flexibility for general workloads.
  • Most organizations use both, creating an “edge-to-cloud” architecture where edge devices handle immediate tasks, and the cloud manages large-scale data processing and complex applications, providing a seamless and efficient experience.

 

 

• AI – Artificial Intelligence:
  • Artificial Intelligence (AI) is the broad field of computer science focused on building systems that perform tasks requiring human-like intelligence, such as learning, reasoning, perception, and decision-making.
  • AI systems typically:
    • ingest large datasets
    • identify patterns
    • make predictions or decisions
  • AI is an umbrella term that includes machine learning, deep learning, and other approaches (rule-based systems, optimization, etc.), not just Machine Learning (ML).
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. Most modern AI systems. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Research stage. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. Does not yet exist. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state.
  • Machine Learning (ML):
    • Subset of AI that enables systems to learn from data without explicit programming.
    • ML uses historical data to detect patterns and make predictions.
    • ML is the dominant paradigm in modern AI, replacing most rule-based systems.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:
      • supervised learning,
      • unsupervised learning,
      • semi-supervised learning and
      • reinforcement learning.
  • Deep Learning (DL):
    • Subset of ML using multi-layered neural networks to learn complex representations.
    • DL is not always “more sophisticated” in all contexts—it is more powerful for unstructured data (images, text, audio), but classical ML can outperform it in structured/tabular data.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition. Face recognition is a good example.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • AI systems that generate new content (text, images, code, audio, etc.) based on learned patterns.
    • GenAI is typically powered by large deep learning models (e.g., transformers), not a separate paradigm.
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Broad AI field for interpreting visual data.
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • lndustrial application of Computer Vision. MV is a subset of CV, not a parallel category.
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates multiple data types (text, image, audio, video).
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
    • The defining feature of an agent is not just decision-making, but the ability to take actions toward a goal in an environment.
  • Agentic AI:
    • Agentic AI is a system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multi-agent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • AI executed locally on devices (IoT, sensors, cameras) instead of centralized cloud.
    • Edge AI refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. It is an infrastructure trend (AI data centers / GPU clusters), not a distinct AI category. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs.
    • While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of AI that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments via sensors and actuators.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy (still niche and mostly experimental.)
  • AI Factories:
    • AI Factories are specialized, high-performance computing centers designed to train, tune, and deploy artificial intelligence models at scale.
    • Companies and organizations involved in AI factory infrastructure and development include Nvidia, AWS, Microsoft, OpenAI, CoreWeave, Lambda, Nebius, Supermicro, and HPE. The European Union is also establishing AI Factories through its EuroHPC Joint Undertaking to foster regional innovation.
    • “AI factory” is a conceptual term (not standardized), referring to industrial-scale AI production systems.