# Hybrid Intelligence

**Authors:** Dominik Dellermann, Philipp Ebel, Matthias Soellner, Jan Marco Leimeister  
**arXiv:** [2105.00691](https://arxiv.org/abs/2105.00691)  
**Date:** 2021-05-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2105.00691v1/

## Key points

- The debate on whether statistical methods or the human brain is superior in predicting certain outcomes has been ongoing for a long time.
- Artificial intelligence (AI) technology, such as deep-learning algorithms and computational intelligence methods, has sparked new debates on when machines will ultimately replace humans.
- AI performs well in some clearly defined tasks like playing chess or identifying objects on images, but it is unlikely that an artificial general intelligence capable of solving multiple tasks simultaneously can be achieved in the near future.
- AI applications that solve complex business problems remain mainly in laboratory settings instead of being implemented in practice.
- Hybrid Intelligence aims to use the complementary strengths of human intelligence and AI to perform better than each could separately.
- Hybrid Intelligence can help organizations achieve better results by combining human creativity and problem-solving skills with machine learning algorithms' speed and accuracy.
- Designing hybrid systems that consider ethical implications while ensuring transparency and accountability is crucial.

## Summary

The debate on whether statistical methods or the human brain is superior in predicting certain outcomes has been ongoing for a long time. The advancement of artificial intelligence (AI) technology, such as deep-learning algorithms and computational intelligence methods, has sparked new debates on when machines will ultimately replace humans. While AI performs well in some clearly defined tasks like playing chess or identifying objects on images, it is unlikely that an artificial general intelligence capable of solving multiple tasks simultaneously can be achieved in the near future. Moreover, AI applications that solve complex business problems remain mainly in laboratory settings instead of being implemented in practice. Therefore, the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence, which aims to use the complementary strengths of human intelligence and AI to perform better than each could separately. This concept has been proposed by researchers Dominik Dellermann, Philipp Ebel, Matthias Soellner, and Jan Marco Leimeister in their article "Hybrid Intelligence" published in Bus Inf Syst Eng 61(5):637–643 (2019). They argue that while the road to AGI is still long, Hybrid Intelligence can help organizations achieve better results by combining human creativity and problem-solving skills with machine learning algorithms' speed and accuracy. The authors also highlight the importance of designing hybrid systems that consider ethical implications while ensuring transparency and accountability. In conclusion, Hybrid Intelligence offers a promising approach for leveraging both human and machine intelligence's strengths to tackle complex problems effectively.