# Co-evolutionary hybrid intelligence

arXiv: https://arxiv.org/abs/2112.04751 · HTML: https://mlsanity.com/paper/2112.04751/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 9 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution](https://mlsanity.com/paper/1801.04016/) (2018)
- [There is no Artificial General Intelligence](https://mlsanity.com/paper/1906.05833/) (2019)

## Built on by (most cited first, 4 of 4)

- [Cognitive Architecture for Co-Evolutionary Hybrid Intelligence](https://mlsanity.com/paper/2209.12623/) (2022)
- [AI in HCI Design and User Experience](https://mlsanity.com/paper/2301.00987/) (2023)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review](https://mlsanity.com/paper/2506.06324/) (2025)

## Abstract (arXiv)

Artificial intelligence is one of the drivers of modern technological development. The current approach to the development of intelligent systems is data-centric. It has several limitations: it is fundamentally impossible to collect data for modeling complex objects and processes; training neural networks requires huge computational and energy resources; solutions are not explainable. The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.