人工智能测量的嵌入式数学模型研究

Xiujian Zhang, Jing Sun, Zhonghao Cheng, Haoyi Chen
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引用次数: 0

摘要

鉴于人工智能的概念不清、基础理论薄弱,基础测量技术的瓶颈尚未得到解决。人工智能指标与基本认知量之间的关系极其复杂,科学构建其功能关系具有挑战性和不确定性。本文从揭示人工智能的内在机理、测量原理和本质特征入手,刻画了人工智能的内在物理规律,提出了基于智能熵的测量模型理论。人工智能测量溯源方法的多渠道探索,为人工智能测量指标与基准量之间关系模型的形成提供思路。建立了人工智能测量的设计验证方法和途径,保证了人工智能测量方法和数据的再现性、准确性和一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Embedded Mathematical Model of Artificial Intelligence Measurement
In view of the unclear concept and weak basic theory of artificial intelligence, the bottleneck of basic measurement technology has not been solved. The relationship between AI metrics and basic cognitive quantity is extremely complex, so it is challenging and uncertain to scientifically construct its functional relationship. This article starts from revealing the internal mechanism, measurement principle and essential characteristics of artificial intelligence, the embedded physical laws are characterized, and the measurement model theory based on intelligence entropy is proposed. Multi-channel exploration of artificial intelligence measurement traceability methods provides ideas for forming the relationship model between artificial intelligence measurement indicators and benchmark quantities. The design verification method and approach of artificial intelligence measurement are established to ensure the reproducibility, accuracy and consistency of artificial intelligence measurement method and data.
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