Hybrid Intelligence
Dominik Dellermann, Philipp Ebel, Matthias Soellner, Jan Marco, Leimeister

TL;DR
Hybrid Intelligence combines human and artificial intelligence to leverage their complementary strengths, aiming to outperform either alone, especially in complex tasks where artificial general intelligence remains distant.
Contribution
This paper advocates for Hybrid Intelligence as a practical paradigm, emphasizing its potential to improve complex problem-solving by integrating human and machine capabilities.
Findings
Hybrid Intelligence enhances performance in complex tasks.
Current AI applications are mostly limited to laboratory settings.
Hybrid approaches outperform standalone AI or human efforts.
Abstract
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…
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