Statistical and symbolic constituents of Artificial Intelligence, based on Hoehndorf & Queralt-Rosinach (2017). Such applications are enabled by the two main areas constituting AI – statistical (i.e., machine learning, data mining, pattern recognition, probabilistic models) and symbolic (semantic data modelling, formal logic, automated reasoning, linguistics, ontologies) (Hoehndorf & Queralt-Rosinach, 2017)įigure 1. In other words, AI is a collective term for software applications that approach problem-solving in a way that approximates human intellect. However, despite the broad agreement that AI is here to stay, the mysticism surrounding the term and its implementations in the built environment still dominates the field.ĪI: teaching machines to read between the lines with semantics and statisticsĪI is concerned with designing intelligent systems that exhibit characteristics associated with human intelligence, i.e., understanding language, learning, reasoning, etc. Reducing negative environmental impact, increasing productivity, aiding energy transition, and enabling evidence-based approaches are just a few of the benefits of AI applications demonstrated in research and practice. Over the last decade, Building Information Modelling (BIM), AI, Internet of Things (IoT) and Construction Tech have been at the forefront of the (technological) developments in the built environment and have redefined the design, construction and operation of buildings and infrastructure. While some debate about what role AI could play in this domain has happened in the past, any doubt is now over. Yet, not without reason, the built environment is also being taken over by Artificial Intelligence (AI).

In the era of digitalization and an increasingly virtual world, the built environment and brick-and-mortar real estate are easily placed among the few tangible things left. To what extent is the future of AI human and the future of humans – AI?īuilding tangibility in the era of virtuality

Knowledge representation in ai