论文标题
理解理解：重新归一化小组启发了（人工）智能的模型
Understanding understanding: a renormalization group inspired model of (artificial) intelligence
论文作者
论文摘要
本文是关于在科学和人工智能系统中理解的含义。我们给出了对理解的数学定义，与共同的智慧相反，我们定义了输入集中的概率空间，并且我们将智能演员的转换视为信息的损失，而是将信息重新组织在新的坐标系统框架中。我们介绍了遵循物理重新归一化小组的思想，相关和无关的参数的概念，并讨论如何沿着这些概念解释不同的AI任务，以及如何描述学习过程。我们展示了科学理解如何适合该框架，并证明了科学任务和模式识别之间有什么区别。我们还引入了相关性的度量，这对于执行有损压缩非常有用。
This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be interpreted along these concepts, and how the process of learning can be described. We show, how scientific understanding fits into this framework, and demonstrate, what is the difference between a scientific task and pattern recognition. We also introduce a measure of relevance, which is useful for performing lossy compression.
