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Abstract: In July 2017, the Chinese government issued a guideline on developing artificial intelligence (AI), namely, the ‘New-Generation Artificial Intelligence Development Plan’ through 2030 to the public, setting a goal of becoming a global innovationcenter in this field by 2030.According to the development plan, breakthroughs should be made in basic theoriesof AI in terms of big data intelligence, cross-media computing, human-machine hybrid intelligence,collective intelligence, autonomous unmanned decision-making, brain-like computing, and quantum intelligent computing.The next-generation AI would be never-ending (self) learning from data andexperience, intuitive reasoning and adaptation (Pan, 2016, 2017). From the perspectiveof overcoming the limitation of existing AI, it is generally recognized that the cross-disciplinary collaboration is a keyforAI having real impact on the world.
Thanks for the efforts from researchers in computer science, statistics, robotics,and psychiatry, the topics in this special issue consist mainly of five subjects:(1) fundamental issues in AI such as interpretable deep learning and unsupervisedlearning (i.e., domain adaptation and generative adversarial learning); (2) brain-like learning such as spiking neural network and memory-augmented reasoning; (3) human-in-the-loop learning such as crowdsourcing design and digital brain with crowd power; (4) creativeapplications such as social chatbots (i.e., XiaoICe) and automatic speech recognition;(5) Dr. Raj Reddy from CMU shared his view on the new-generation AI, Prof. Bin Yufrom UC Berkeley advocated that AI should use statistical concepts through human–machine collaboration, and researchers from the Chinese Academy of Sciences surveyedthe acceleration of deep neural networks. All ofinterview, perspective, survey, and research papers targetrethinking the appropriate ways for a general scenario or a specific application.

2018人工智能2.0特刊：理论与应用

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