Hybrid Intelligence

By Dominik Dellermann, P. Ebel, Matthias Söllner, Jan Marco Leimeister. Business & Information Systems Engineering, 2019

603 citations recorded by the source corpus.

Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go or identifying objects on images, it is doubted that the development of an artificial general intelligence (AGI) which is able to solve multiple tasks at the same time can be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the use of AI to solve complex business problems in organizational contexts occurs scarcely, and applications for AI that solve complex problems remain mainly in laboratory settings instead of being implemented in practice. Since the road to AGI is still a long one, we argue that the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence. This concept aims at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately (e.g., Kamar 2016).

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/37d7d4a37887b35fac4efebc69214bfdeb0fb8bd

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In Artificial Intelligence

Citations in the corpus

AI literacy and its implications for prompt engineering strategies (2024, background): “…of enhancing human-AI interactions constructively, indicating a hybrid-intelligent educational paradigm where students augment their human intelligence with intelligent technologies, enabling them to achieve more collectively (Baird & Maruping, 2021; Dellermann et al., 2019; Salomon et al., 1991).”
AI-Based Digital Assistants (2019, background): “Thus, human–AI interaction ranges from substitution (AI replaces humans), to augmentation (humans and AI augment one another), to assemblage (AI and humans are dynamically brought together to function as an integrated unit) (Dellermann et al. 2019; Rai et al. 2019).”
The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023, background): “Moreover, AI ’ s understanding of complex problems is time -dependent and dyna mic, requires a lot of domain knowledge , and has no specific ground truth (Dellermann et al. , 2019).”
How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents (2020, background): “…we expect that the user ’ s Figure 1 Step by step theoretical development perceptions of the PIA will change from initially perceiving it as a tool to eventually perceiving it as a collaborator in the process of task completion and decision-making (Seeber et al., 2019; Dellermann et al., 2019).”