After the invention of the computer in the 1940s, people realized that its capabilities were not limited only to numerical calculations. We could use it to perform many intellectual tasks that were usually solved by human beings. Such revelation motivated researchers like McCarthy, Minsky, Newell, and Simon to establish research centers investigating artificial intelligence (AI) with a double focus: one that tries to reduce human intelligence to symbolic manipulations and another that tries to imitate human neural behavior to create mathematical models. In both cases, artificial intelligence has been considered a problem-solving tool. However, nowadays, instead of solving problems, AI dissolves them. With the current abundance of AI algorithms and big data, everything becomes available, and, therefore, it also becomes dissolvable. 

With this introduction, we can assume that architects can take advantage of the abundance of AI algorithms and databases to create context-specific predictions or classifications without designing or training the algorithm from the beginning. Architects can effectively encode spectral and spatial information based on the data itself, allowing them to use this information to initiate an architectural or urban design process. We can use such an advantage to machine-generate various design solutions from learning big architectural data or to explore the vast space of possible design solutions for a specific design brief focusing on questions requiring human creativity rather than machine productivity. Therefore, given that such technologies are shaping the way we live, significantly shaping the way we work. Here at SHARE lab, we ask the following question: how might architects include such technologies in a design workflow while preserving their role as a creative entity?

AI excels at solving problems with clear answers, processing data, and performing repetitive tasks, freeing up time for architects to work on more open questions that require creativity. Therefore, the research SHARE lab will pursue a Human-Centered AI (HC) approach in architectural practices that will include the human in an artificial intelligence process, not only as a supervisor of the results but as a teacher of the algorithm, meaning that the artificial intelligence algorithm will be trained under the guidance of the human—producing a specific inference biased to the interest of that human. For the SHARE lab, the main focus will be on the collaboration between humans and machines. Technologies are used to expand capabilities, not only in a physical sense but also intellectually.