The next-generation network (6G) has more strict requirements for the online learning ability and high interpretability of the learned systems. Machine learning is expected to be essential to assist in making the networks efficient and adaptable, but most promising methods often are treated as “black boxes” due to the deep structures and high nonlinearity. Therefore, this article attempts to study the interpretations of machine learning algorithms to make them more applicable to the 6G-enabled Internet of things (IoT) networks. Typically, this article focuses on the new model GBDT2NN, which distills the knowledge learned by gradient boosting decision tree (GBDT) into neural network (NN) models to retain the learning ability of numerical data and rise the ability of online learning at the same time, but it loses the interpretability. This article conducts an empirical study on explaining individual prediction of GBDT2NN by taking use of the feature importance learned from GBDT, and then further explores whether the explanation can improve the approximation process. In addition, this article proposes two methods to obtain the interpretations: 1) the independent method and 2) the joint method. The experiments on several data sets of IoT networks show that the proposed methods can achieve better performance on both explanations and predictions.

Introspection is a question-led process in which one builds on what one already knows to explore what is possible and plausible. In creative introspection, whether in art or in science, framing the right question is as important as finding the right answer. Presupposition-laden questions are themselves a source of knowledge, and in this paper we show how widely-held beliefs about the world can be dynamically acquired by harvesting such questions from the Web. We show how metaphorical reasoning can be modeled as an introspective process, one that builds on questions harvested from the Web to pose further speculative questions and queries. Metaphor is much more than a knowledge-hungry rhetorical device: it is a conceptual lever that allows a system to extend its model of the world.