| Review Articles |
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| Crowd intelligence in AI 2.0 era |
| Wei Li, Wen-jun Wu, Huai-min Wang, Xue-qi Cheng, Hua-jun Chen, Zhi-hua Zhou, Rong Ding |
| State Key Laboratory of Software Development, Beihang University, Beijing 100191, China; National Laboratory for Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410073, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
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Abstract The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence (AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-to-offline (O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.
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Received: 23 December 2016
Published: 20 January 2017
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