This work presents a formalization of analogy on numbers that relies on generalized means. It is motivated by recent advances in artificial intelligence and applications of machine learning, where the notion of analogy is used to infer results, create data and even as an assessment tool of object representations, or embeddings, that are basically collections of numbers (vectors, matrices, tensors). This extended analogy use asks for mathematical foundations and clear understanding of the notion of analogy between numbers. We propose a unifying view of analogies that relies on generalized means defined in terms of a power parameter. In particular, we show that any four increasing positive real numbers is an analogy in a unique suitable power. In addition, we show that any such analogy can be reduced to an equivalent arithmetic analogy and that any analogical equation has a solution for increasing numbers, which generalizes without restriction to complex numbers. These foundational results provide a better understanding of analogies in areas where representations are numerical.

Learning is the most important ability and attribute of a Intelligent System. A system which acquires knowledge by experience, trial-and-error or through coaching, exhibits early traces of intelligence. This post explains how ANNs learn. In the previous post, 'Layman's Intro to AI', we explored a simple analogy of how a Artificial Neural Network or ANN gains to understand the'knowledge weight' of a Cat (or what we termed as the Catiness). 'w' is the knowledge weight that the network needs to learn (about the Catiness of a Cat) The '*' operator is a function called the Activation Function, which was introduced in the post titled "Mathematical foundation for Activation Functions".