AI Bibliography
AI Bibliography |
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ZHAI, P., ZHANG, L., DONG, Z., WAN, S., GUO, Y., & GAN, Z., et al.. (2020). Machine intuition. SCIENTIA SINICA Informationis, 50(10), 1475. |
| Resource type: Journal Article BibTeX citation key: ZHAI2020 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Decision Theory, General, Geopolitical Subcategories: China, Deep learning, Machine intelligence, Machine learning, Machine recognition, Neurosymbolic, Synthetic intelligence Creators: DAI, DONG, GAN, GUO, WAN, ZHAI, ZHANG Publisher: Collection: SCIENTIA SINICA Informationis |
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| Abstract |
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Currently, the problems that can be solved using deep learning-based artificial intelligence technology often require a large training data set for learning, and simultaneously, the information contained in the data set should be complete. However, in a real time-varying complex application environment, the collected data often contain significant noise, uncertainty, and only partial information of the environment, which limits the prospects of artificial intelligence applications based on deep learning. However, in a similar environment, humans can often make rapid and appropriate decisions based on intuition, providing inspiration to develop new artificial intelligence theories to solve the above problems. This article systematically discusses the concepts, mechanisms, categories, and other aspects of human intuition and analyzes the progress and shortcomings of existing research from different disciplines. Based on this analysis, machine intuition, a new cross-disciplinary research direction, is proposed, along with its basic criteria. The objective of machine intuition research is to facilitate machines with insight and creativity abilities to ultimately achieve intuitive intelligence similar or even superior to human instincts. Moreover, this paper attempts to design the general overall architecture of machine intuition and determines the basic principles and connotations of several main functional modules, such as holographic perception, intuitive cognition, intuitive decision-making, and game action. Lastly, from the viewpoint of cross-disciplinary research in brain science, cognitive science, and artificial intelligence, among others, the potential applications of machine intuition and future research directions are prospected, thus providing directional guidance for subsequent research on machine intuition.
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