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Current developments in fields such as quantum physics, fine arts, robotics, cognitive sciences or defense and security indicate the emergence of creative systems capable of producing new and innovative solutions through combinations of machine learning algorithms. These systems, called machine invention systems, challenge the established invention paradigm in promising the automation of – at least parts of – the innovation process. This paper’s main contribution is twofold. Based on the identified state-of-the-art examples in the above mentioned fields, key components for machine invention systems and their relations are identified, creating a conceptual model as well as proposing a working definition for machine invention systems. The differences and delimitations to other concepts in the field of machine learning and artificial intelligence, such as machine discovery systems are discussed as well. Furthermore, the paper briefly addresses the social and societal implications and limitations that come with the adoption of the technology. Because of their revolutionizing potential, there are widespread implications to consider from ethical and moral implications to policymaking and societal changes, like changes in the job structure. The discussion part approaches some of these implications, as well as solutions to some of the proposed challenges. The paper concludes by discussing some of the systemic benefits that can be accessed through machine invention.

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Building Information Modeling (BIM) related promises are numerous – reduction of the architecture, engineering and construction (AEC) industry fragmentation, construction cost, and delivery time, as well as lifecycle optimization have been advocated in both literature and practice. But so are the challenges of BIM adoption: establishment and standardization of BIM data structures or ensuring the necessary skills and competencies for planning process participants. In this paper we present ongoing research on the integration of BIM in education through student experiments, based on a BIM-supported integrated design studio (IDS). Thereby the various features of BIM technology adopted in multidisciplinary conceptual design stage are explored and evaluated. Quantitative and qualitative research, in form of questionnaires and focus group discussions, addresses the people and process related challenges in such collaborative BIMsupported building projects. The analysis of three cycles of such IDSs has shown that the participants appreciate the collaborative approach, and benefit from working with other disciplines by sharing knowledge; however BIM technology has not significantly contributed to the improvement of the design quality.