How Artificial Intelligence and Machine Learning transform smart buildings into sustainable intelligent communities
Mark Cosyn, Director of Digital Innovation & Intelligent Real Estate
Smart devices, homes, and buildings are increasingly part of our daily experience. Like a streamlined, automated factory, smart buildings and cities work better, faster, and cheaper than traditional builds. Smart cities are evolving, but there is a distinct difference between what is smart and what is intelligent. While smart buildings and cities focus on technology to improve the way they operate, intelligent real estate leverages human-machine collaboration to solve complex challenges and improve the individualized experience of interacting with buildings and spaces.
Intelligence enables buildings to understand their purpose and relationships with a community, interacting and learning with humans to improve the ways they benefit the community. Artificial Intelligence and Machine Learning (AI/ML) connect data from smart buildings and other sources in real-time to assist individuals based on their personal interactions across single or multiple buildings and spaces real estate owners improve economic, environmental, and social performance through access to contextual intelligence gathered from data reflected in geographically dispersed locations, asset types and conditions, changing compliance standards and timeframes, procurement processes, life safety, and customer experience.
The shift to smart and intelligent environments has created new opportunities for optimizing environmental and social well-being around the world. However, there are now less than ten years left to achieve the UN Sustainable Development Goals (SDGs, also known as the Global Goals) set out by governments, businesses, and other stakeholders and we are woefully off-track. Incremental change is no longer an option. Achieving the UN Global Goals by 2030 will require radically more sustainable ways of managing products and services over their entire lifecycle, from design to use and end-of-life.
The successful performance of AI/ML solutions for intelligent communities depends on the knowledge and future vision of the individuals who develop and evolve their computational models. This raises one of the most important questions in technology today: what are the right ways for humans and machine learning algorithms to interact to solve problems?
