DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT
Abstract
From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable achievements in recent years. Analyzing, comparing, and summarizing these achievements at the paradigm level is important for future AI innovation, but has not received sufficient attention. In this paper, we give an overview and perspective on machine learning paradigms. First, we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective. Accordingly, we give an overview on three basic and twelve extended learning paradigms, such as Ensemble Learning, Transfer Learning, etc., with figures in unified style. We further analyze three advanced paradigms, i.e., AlphaGo, AlphaFold and ChatGPT. Second, to enable more efficient and effective scientific discovery, we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science (DeSci) movement based on decentralized autonomous organization (DAO). To this end, we design the Hanoi framework, which integrates human factors, parallel intelligence based on a combination of artificial systems and the natural world, and the DAO to inspire AI innovations.
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Combination of different data types processed concurrently enables multimodal models to provide a holistic view, making them especially effective in applications requiring deep and multifaceted understanding of complex scenarios.
“The combination of different data types, processed concurrently, enables these models to provide a holistic view, making them especially effective in applications that require a deep and multifaceted understanding of complex scenarios [113], [161], [162], [160].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [161]
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Background
- Authors
- Miao, Q., Zheng, W., Lv, Y., Huang, M., Ding, W., & Wang, F.-Y.
- Affiliations
- University of Chinese Academy of Sciences (CN)
- Wuhan University of Technology (CN)
- Chinese Academy of Sciences (CN)
- Macau University of Science and Technology (MO)
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2023-04
- Volume
- 10
- Issue
- 4
- Pages
- 877-897
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W4361030007
- Cited by
- 78
- References
- 62
- Citations by year
- 2026: 2 · 2025: 22 · 2024: 23 · 2023: 31
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE/CAA Journal of Automatica Sinica (publishedVersion)
- Funders
- National Key Research and Development Program of China (2020YFB2104001)
- National Natural Science Foundation of China (62271485,61903363,U1811463)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- machine learning paradigms, AlphaGo, AlphaFold, ChatGPT, ensemble learning, transfer learning, decentralized science, decentralized autonomous organizations, scientific discovery, human-AI integration, parallel intelligence
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