LAYER 0.1 AI - Artificial Intelligence
Artificial Intelligence is the field of computer science focusing on making machines “smart”. Meaning reaching a state where they are able to perform tasks that normally require human intelligence. These tasks include understanding language, recognizing images, solving problems, or making decisions. AI systems learn from data, adapt to new information, and can improve over time. Whether it is a voice assistant, a self-driving car, or a recommendation system, AI is what enables technology to act in ways that seem intelligent. The term “AI” collects the possibilities of machines (or computers) to perceive, synthesize and infer information in one word. Simply said: Non-biological intelligence.
Artificial Intelligence Relations (illustration inspired by IBM)
NODE 0.1.1 ML - Machine Learning
A subset of AI that teaches machines to learn from provided information and improve over time. Machine Learning is a possibility for computers to learn from data instead of being programmed with specific instructions. Just like humans learn from experience, a machine learning system looks at examples and finds patterns to make decisions or predictions. For instance, it can learn to recognize cats in photos by studying many pictures labeled “cat” or “not cat.” ML-algorithms improve through “experience” and the more data they get, the better the understanding and solving of similar tasks on their own becomes.
NODE 0.1.2 NLP - Natural Language Processing
A subfield of Machine Learning that focuses on how machines understand, interpret, and generate human language. Natural Language Processing (NLP) allows machines to read, listen, and even respond in ways that make sense to people. For example, NLP is what powers voice assistants, translates languages, or suggests words while you are typing on a keyboard. By analyzing the structure and meaning of words and sentences, NLP helps bridge the gap between how humans communicate and how computers process information. NLP provides the foundation for Large Language Models (LLMs), which utilize NLP techniques to understand and generate human-like text on a large scale.
NODE 0.1.3 LLM - Large Language Model
Large Language Models are advanced AI systems using NLP techniques that can understand and generate human-like text. They are trained on huge amounts of written content, such as books, websites, and articles, to learn how language works. Once trained, they can answer questions, write stories, translate languages, and even chat like a person. They do not "think" like humans, but they are very good at predicting what words should come next in a sentence, which makes them useful for many language-related tasks.
NODE 0.1.4 GPT - General Pre-trained Transformer
A specific type of LLM that uses a transformer architecture to generate human-like text based on the input it receives. Generative Pre-trained Transformer, is a type of artificial intelligence that can understand and create human-like text. It learns by reading large amounts of written content and then uses that knowledge to respond, write stories, or have conversations. The “pre-trained” part means it learns a lot before it is even used, and the “transformer” is the technology behind how it processes and understands language. A GPT is one of the most well-known examples of a Large Language Model (LLM), built using techniques from Natural Language Processing (NLP).
NODE 0.1.5 GenAI - Generative AI
Generative AI (GenAI) is a subset of artificial intelligence that focuses on creating new, synthetic yet seemingly authentic content, such as images, music, text, and other forms of media. Unlike discriminative AI models, which primarily classify or predict outcomes based on input data, generative models learn patterns and structures from training data to produce novel outputs. Techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models are commonly employed in generative AI. These technologies have wide-ranging applications, including art creation, drug discovery, personalized content generation, and enhancing creative industries by automating and augmenting the production of original content.
NODE 0.1.6 GANs - Generative Adversarial Networks
Generative Adversarial Networks are a subset of ML and used to create realistic images, sounds, or other data. They work by having two parts: a generator and a discriminator. The generator tries to create fake data that looks real, while the discriminator tries to tell the difference between real and fake data. These two parts compete with each other—as the generator gets better at fooling the discriminator, the discriminator gets better at spotting fakes. Over time, this back-and-forth helps the generator produce increasingly realistic results, such as lifelike photos or even deepfake videos. As an example in architecture, this was applied to generate new floor plans among other implications.(2)
Functionality of GANs (by Thalles Silva)
LAYER 0.2 IoT - The Internet of Things
The Internet of Things (IoT) is represented by physical devices that collect, send and interact with data through a network via the internet. The hardware base includes embedded processors, memories of different types, sensors, actuators, cloud servers, intermediate processing systems, network systems and gateways. The software base includes operating systems, databases and control applications for several application domains, to the very least. The combination of hardware and software components for control applications constitutes the base for the evolution of cyber-physical systems. The IoT is building a worldwide infrastructure of devices that influence all facets of our life. From agriculture to mining, from health services to manufacturing and building. It will provide the infrastructure over which the new emerging AI implementation will be based on. On the downside it represents a major risk as with every smart device the potential for hacks or data losses increases, presenting security and privacy issues. In case of a system bug or a hacker attack, all devices connected to a network might be affected. Furthermore, multiple data formats from different devices make it hard to handle big amounts of data that necessitates complex maintenance.(3)
LAYER 0.3 Big Data
Big Data refers to extremely large and complex datasets that require innovative methods and technologies to store, process, and analyze efficiently. Characterized by the four Vs (Volume, Velocity, Variety, and Veracity). Big Data encompasses data from diverse sources such as social media, IoT sensors, transaction records, and more. The analysis of Big Data enables organizations to uncover hidden patterns, correlations, and insights, leading to better decision-making, improved operational efficiency, and enhanced customer experiences.
“The data-driven world will be always on, always tracking, always monitoring, always listening and always watching – because it will be always learning.”
David Reinsel, John Gantz, and John Rydning, “The Digitization of the World”
The internet of 2025 is considered to consist of about 149 Zettabytes and to download the entire 2025 Global Datasphere at an average of 120 Mbits/s, (2023 average connection speed across Western Europe) it would take one person 1.888.610.034 (One billion, eight hundred eighty-eight million, six hundred ten thousand thirty-four) years to do so. Or if every person in the world (ca. 8 billion) could help and never rest, then you could get it done in 86 days.
LAYER 0.4 CAD - Computer Aided Design
Computer Aided Design (CAD) enables the creation, modification, analysis, and optimization of digital spatial designs using computer software. CAD systems are widely employed across various industries, including engineering, architecture, manufacturing, and product design. They facilitate the development of precise 2D and 3D models, allowing professionals to visualize and simulate concepts before physical prototyping. CAD tools aim to enhance productivity, accuracy, and collaboration, enabling more efficient and innovative design processes.
LAYER 0.5 CAAD - Computer Aided Architectural Design
Computer Aided Architectural Design (CAAD) works based on individual digital drawing like sections, plans, elevations or digital 3D models. In the early phases systems applied two dimensional lines and surfaces often hitting real-life problems such as the lack of adequate scanners, and plotters or struggling with software related issues. As of today it mainly extends pencil, ink, paper and physical models in fields working with technical drawings or visualizations in a multi-dimensional world. It aims to enhance quality, efficiency and communication through documentation providing a database for manufacturing or building.(4) The world's connectivity also leads to a global computer aided design language spoken around the globe, possibly appearing in different “dialects” based on their regions or software.
LAYER 0.6 BIM - Building Information Modeling
In comparison to CAD systems, Building Information Modelling (BIM) is a process aiming to collect and organize information in one rich, accurate and detailed semantic model (or several interlinked models) about itself and every component in it. It does not represent one software or tool rather than a digital process covering the whole lifespan of a project gathering all contributors around one shared knowledge source. It enables collaborative management and planning by having access to information occurring from the same origin. Stakeholders are able to insert, extract, update or modify information in the BIM depending on the roles of that stakeholder. BIM presents architects with an opportunity to communicate and collect different types of value but needs strategic leadership and “effective communication”.(5) This object-based workflow’s most common data formats are Industry Formation Classes (IFC).(6) Reflecting on BIM compared to traditional processes, it pushes decision making and detailed planning towards early project phases. In the future, BIM processes might be augmented by AIs to optimize building processes, reduce errors and improve the project's quality and time-handling. BIM processes utilize multiple dimensions (Fig. 04) referring to the levels of information and detail incorporated into a BIM model, typically categorized as follows:
3D: Spatial and geometric information of a building.
4D (Time): Scheduling and sequencing information.
5D (Cost): Cost-related information, estimation or manement.
6D (Sustainability/Lifecycle): Information about the lifecycle, energy.
7D (Facility Management): Operational phase, maintenance, repairs.
…
BIM Dimensions
LAYER 0.7 GIS - Geographic Information System
The Geographic Information System (GIS) enables professionals to capture, organise, analyse, and present spatial data. Together with Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS), the Internet, and user-provided data, it aims to provide a deeper understanding of the world by uncovering hidden connections and giving insights through vast datasets.(7) With an increasing relevance over the last years GISs are implemented in multiple technologies, processes, techniques and methods architects use on a daily basis, often without recognition. In the future, BIM, CAD and GIS are expected to further merge over the years as they will interact better.
LAYER 0.8 IDE - Immersive Digital Environment
Immersive Digital Environment (IDE) refers to advanced technological spaces that engage users in highly interactive and realistic digital experiences. These environments leverage technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a sense of presence and immersion. IDEs are utilized across various fields, including education, training, entertainment, and healthcare, to enhance learning, simulation, and user engagement. By providing multi-sensory feedback and interactive content, IDEs enable users to explore, manipulate, and interact with digital information in a natural and intuitive manner.
__________________________________
2. Stanislas Chaillou, “Semanticism | Stanislas ,” accessed March 10, 2025, https://stanislaschaillou.com/semanticism.html.
3. Dimitrios Serpanos and Marilyn Wolf, Internet-of-Things (IoT) Systems (Cham: Springer M. M.M. Sarcar, K. Mallikarjuna Rao, and K. Lalit Narayan, Computer Aided Design and Manufacturing (PHI Learning Pvt. Ltd., 2008).
4. M. M. M. Sarcar, K. Mallikarjuna Rao, and K. Lalit Narayan, Computer Aided Design and Manufacturing (PHI Learning Pvt. Ltd., 2008).
5. Andrew Dainty, David Moore, and Michael Murray, Communication in Construction: Theory and Practice (London: Routledge, 2007), 7, https://doi.org/10.4324/9780203358641.
6. “IFC 4.3.2.,” GitHub, accessed February 18, 2025, https://github.com/buildingSMART/IFC4.3.x-development/blob/master/content/cover.md.Git
7. “History of GIS | Timeline of the Development of GIS,” accessed February 18, 2025, https://www.esri.com/en-us/what-is-gis/history-of-gis.History