论抽象科学的出现和机器知识学习的突破

Yingxu Wang
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引用次数: 4

摘要

最近的基础研究揭示了一种前所未有的现象,即抽象科学(AS)的出现[Wang & Tunstel, 2019]与经典的具体科学相对应。AS涵盖了数据、信息、知识、智能、数学和系统科学等当代学科。一般来说,人工智能带来了新的理论和技术,特别是机器知识学习(MKL) [Wang, 2016]。AS的最新发现表明,知识的基本单位是二进制关系(bir) [Wang, 2017],作为信息和数据的二进制关系。认知知识基础(Cognitive Knowledge Bases, CKB)和指称数学的突破将推动MKL提高人类的学习能力。MKL导致了机器学习的高级形式,它使认知机器成为人类不可或缺的助手,具有相互共享的知识库,从而实现集体知识学习。将介绍人工智能和认知系统中广泛的新应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Emergence of Abstract Sciences and Breakthroughs in Machine Knowledge Learning
Recent basic studies have revealed an unprecedented phenomenon of the emergence of Abstract Sciences (AS) [Wang & Tunstel, 2019] as a counterpart of classic concrete sciences. AS encompasses contemporary disciplines of data, information, knowledge, intelligence, mathematics, and system sciences. AS leads to novel theories and technologies for AI in general, and Machine Knowledge Learning (MKL) in particular [Wang, 2016]. The latest discovery in AS reveals that the basic unit of knowledge is a binary relation (bir) [Wang, 2017] as that of bit for information and data. MKL powered by the breakthroughs in Cognitive Knowledge Bases (CKB) and denotational mathematics will enhance human learning capability. MKL leads to advanced form of machine learning, which enables cognitive machines as an indispensable assistant to humans with mutually sharable knowledge bases towards collective knowledge learning. A wide range of novel applications in AI and cognitive systems will be presented.
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