JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2018 Vol.19 No.1 P.1-2

2018 special issue on artificial intelligence 2.0: theories and applications

Author(s): Yun-he PAN
Affiliation(s): Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s):
Key Words:

Share this article to： More \|Next Article >>>

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

概要：2017年7月，中国政府发布了人工智能（AI）开发指南，即《新一代人工智能发展规划》。该计划拟分三步走，推进中国人工智能研发，其目标是在2030年，中国人工智能理论、技术和应用总体达到世界领先水平，成为世界主要的人工智能创新中心。
根据新一代人工智能发展规划，要瞄准大数据智能、跨媒体计算、人机混合智能、群体智能、自主无人化决策、类脑计算、量子智能计算等人工智能基础理论进行深入研究，取得突破。
下一代人工智能具有从数据和经验中无限（自我）学习、直觉推理、自适应等特点（Pan, 2016, 2017）。为克服现有人工智能的局限，人们普遍认识到，跨学科领域合作是人工智能真正影响世界的关键因素。
感谢来自计算机科学、统计学、机器人和精神病学等领域的研究人员为本期特刊撰写高质量论文。本期特刊主要涵盖如下5个方面主题：（1）人工智能基本理论问题，如可解释性深度学习和无监督学习（即领域自适应学习和生成对抗性学习）；（2）类脑学习，如脉冲神经网络和记忆增强推理；（3）人在回路智能学习，如众包设计和数字大脑；（4）创意智能应用，如社交聊天机器人（即小冰）和自动语音识别；（5）卡耐基梅隆大学RajReddy博士分享了他对新一代人工智能的看法，加州大学伯克利分校郁彬教授主张在人机协作中使用统计概念以提升智能，中国科学院程健研究员等综述了深度神经网络加速方法。

关键词：

Reference

[1]Cheng J, Wang PS, Li G, et al., 2018. Recent advances in efficient computation of deep convolutional neural networks. Front Inform Technol Electron Eng, 19(1):64-77.

[2]Duan XY, Tang SL, Zhang SY, et al., 2018. Temporality- enhanced knowledge memory network for factoid question answering. Front Inform Technol Electron Eng, 19(1):104-115.

[3]FITEE editorial staff, 2018. An interview with Dr. Raj Reddy on artificial intelligence. Front Inform Technol Electron Eng, 19(1):3-5.

[4]Li S, Song SJ, Wu C, 2018. Layer-wise domain correction for unsupervised domain adaptation. Front Inform Technol Electron Eng, 19(1):91-103.

[5]Ma YQ, Wang ZR, Yu SY, et al., 2018. A novel spiking neural network of receptive field encoding with groups of neurons decision. Front Inform Technol Electron Eng, 19(1): 139-150.

[6]Pan YH, 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[7]Pan YH, 2017. Special issue on artificial intelligence 2.0. Front Inform Technol Electron Eng, 18(1):1-2.

[8]Qian YM, Weng C, Chang XK, et al., 2018. Past review, current progress, and challenges ahead on the cocktail party problem. Front Inform Technol Electron Eng, 19(1):40- 63.

[9]Shum HY, He XD, Li D, 2018. From Eliza to XiaoIce: challenges and opportunities with social chatbots. Front Inform Technol Electron Eng, 19(1):10-26

[10]Wang HG, Li X, Zhang T, 2018. Generative adversarial network based novelty detection using minimized reconstruction error. Front Inform Technol Electron Eng, 19(1): 116-125.

[11]Xiang W, Sun LY, You WT, et al., 2018. Crowdsourcing intelligent design. Front Inform Technol Electron Eng, 19(1): 126-138.

[12]Xu D, Dai F, Lu Y, 2018. A platform of digital brain using crowd power. Front Inform Technol Electron Eng, 19(1): 78-90.

[13]Yu B, Kumbier K, 2018. Artificial intelligence and statistics. Front Inform Technol Electron Eng, 19(1):6-9.

[14]Zhang QS, Zhu SC, 2018. Visual interpretability for deep learning: a survey. Front Inform Technol Electron Eng, 19(1):27-39.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Similar articles

- Go to

2018人工智能2.0特刊：理论与应用

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference