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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.
Miao et al. (Tue,) studied this question.