Explore the results of years of academic research that powers decision augmentation and Hybrid Intelligence®.
One other approach beyond trying to replicate human level intelligence and related learning mechanisms is to combine human and artificial intelligence. The basic rational behind this is the combination of complementary heterogeneous intelligences (i.e. human and artificial agents) into a socio-technological ensemble that is able to overcome the current limitations of artificial intelligence. This approach is neither focusing on the human in the loop of AI nor automating simple tasks through machine learning but on solving complex problems using the deliberate allocation of tasks among different heterogeneous algorithmic and human agents. Both the human and the artificial agents of such systems can then co-evolve by learning and achieve a superior outcome on the system level.
The aim of this research is to develop a method to predict the probability of success of early stage start-ups. Therefore, I follow a DSR approach (Hevner 2007; Gregor and Hevner 2013) to develop a Hybrid Intelligence method that combines the strength of both machine intelligence such as ML techniques to access, process, and structure large amount of information as well as collective intelligence, which uses the intuition and creative potential of individuals while reducing systematic errors through statistical averaging in an ensemble approach (Shmueli and Koppius 2011). I, thus, intend to show that a hybrid approach improves predictions for the success of start-ups under extreme uncertainty compared to machine or human only methods.
Within this paper I propose a taxonomy for design knowledge for hybrid intelligence systems, which presents descriptive knowledge structured along the four meta-dimensions task characteristics, learning paradigm, human-AI interaction, and AI-human interaction. Moreover, I identified 16 sub-dimensions and a total of 50 categories for the proposed taxonomy. By following a taxonomy development methodology (Nickerson et al. 2013), I extracted interdisciplinary knowledge on human-in-the-loop approaches in ML and the interaction between human and AI. I extended those findings with an examination of seven empirical applications of hybrid intelligence systems.
In 1999, as Steven Spielberg was preparing to make the movie “Minority Report”, he assembled a team of 15 technology experts to help him depict the world as it would look in 2054, the year that the movie takes place. The result was an impressive and somewhat dystopic future scape where technology permeates our lives.It is too soon to say whether the vision of the future depicted in the movie will become reality, but 18 years after the film’s release, artificial intelligence (AI) and what is often called intelligent enterprise automation have had a profound impact in some areas.
In today's complex and uncertain world, something like those clearly defined industries no longer exists. That's why companies need to stop thinking in industries and rather move towards thinking about strategic fields (or “arenas” how Rita McGrath the professor of management at the Columbia Business School calls it). And this significantly changes how to think about competition.
Previous advances in research on artificial intelligence (AI) also raised issues related to security. A central goal of advanced autonomous systems is, therefore, to ensure that they are aligned with human values. Similar to our development approach, researchers at Open AI recently designed a hybrid avenue that integrates human preferences, debate and iterated amplification in the process of teaching machines and ensures that AI systems behave reliably on their intended goal. Our team highly appreciates the progress in this area and also their call on the social scientist for AI safety.
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