Machine invention systems: a (r)evolution of the invention process?
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Machine invention systems: a (r)evolution of the invention process? Dragos‑Cristian Vasilescu1 · Michael Filzmoser1 Received: 23 September 2020 / Accepted: 2 October 2020 © The Author(s) 2020
Abstract 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. Keywords Machine invention · Invention process · Automated invention · Creative systems · Machine learning
1 Introduction Artificial intelligence and machine learning are evolving technologies with the potential of significant influence on economy and society. Owing to technology cycles significant shortening (Weaver et al. 2017) and faster adoption rates, the society needs to pay increased attention to the impact of these technologies, as well as their implications (Linstone 2011). Computerization and digital transformation are expected to replace manual and unpleasant dull, dirty and dangerous (Lin et al. 2012) work – as robotics did in the past in the industrial production – but also to assume more complex mental activities. For example jobs in service and administrative areas—the type of work that has the potential of intrinsic motivation and self-actualization (Frey and Osborne 2017). The recent developments in gaming, such as OpenAI’s Dota 2 (OpenAI 2017), DeepMind’s AlphaGo (Silver et al. * Dragos‑Cristian Vasilescu [email protected] 1
Institute of Management Science, Vienna University of Technology, Vienna, Austria
2017) or AlphaGo Zero (Silver et al. 2017) suggest that one activity that could be automated in the future is the creation of new information, models and camera-ready content – i.e., invention. In the current under
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