Special issue on artificial intelligence 2.0

With the ever-growing popularization of the Internet, universal existence of sensors, emergence of big data, development of e-commerce, rise of the information community, and interconnection and fusion of data and knowledge in human society, physical space, and cyberspace, the information environment surrounding artificial intelligence (AI) development has changed profoundly, leading to a new evolutionary stage: AI 2.0. The emergence of new technologies also promotes AI to a new stage (Pan, 2016). The next-generation AI, namely AI 2.0, is a more explainable, robust, open, and general AI with the following attractive merits: It effectively integrates data-driven machine learning approaches (bottom-up) with knowledge-guided methods (top-down). In addition, it can employ data with different modalities (e.g., visual, auditory, and natural language processing) to perform cross-media learning and inference. Furthermore, there will be a step from the pursuit of an intelligent machine to the hybridaugmented intelligence (i.e., high-level man-machine collaboration and fusion). AI 2.0 will also promote crowd-based intelligence and autonomous-intelligent systems. In the next decades, AI2.0 will probably achieve remarkable progress in aforementioned trends, and therefore significantly change our cities, products, services, economics, environments, even how we advance our society. This special issue aims at reporting recent re-thinking of AI 2.0 from aforementioned aspects as well as practical methodologies, efficient implementations, and applications of AI 2.0. The papers in this special issue can be categorized into two groups. The first group consists of six review papers and the second group five research papers. In the first group, Zhuang et al. (2017) reviewed recent emerging theoretical and technological advances of AI in big data settings. The authors concluded that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI. Li W et al. (2017) described the concepts of crowd intelligence, and explained its relationship to the existing related concepts, e.g., crowdsourcing and human computation. In addition, the authors introduced four categories of representative crowd intelligence platforms. Peng et al. (2017) presented approaches, advances, and future directions in cross-media analysis and reasoning. This paper covers cross-media representation, mining, reasoning, and cross-media knowledge evolution. Tian et al. (2017) reviewed the state-of-the-art research of the perception in terms of visual perception, auditory perception, and speech perception. It also covered perceptual information processing and learning engines. Zhang et al. (2017) introduced the trends in the development of intelligent unmanned autonomous systems. It covered unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned Editorial: Frontiers of Information Technology & Electronic Engineering www.zju.edu.cn/jzus; engineering.cae. cn; www.springerlink. com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: jzus@zju.edu.cn