数据与计算发展前沿 ›› 2019, Vol. 1 ›› Issue (2): 1-16.doi: 10.11871/jfdc.issn.2096-742X.2019.02.001

所属专题: “人工智能”专刊

• 人工智能专刊 •    下一篇

2019 年人工智能新态势与新进展

孙哲南1,2,3,*(),张兆翔1,2,3,王威1,刘菲1,谭铁牛1,2,3   

  1. 1. 中国科学院自动化研究所,智能感知与计算研究中心,北京 100190
    2. 中国科学院大学,人工智能学院,北京 100049
    3. 中国科学院脑科学与智能技术卓越创新中心,北京 100190
  • 收稿日期:2019-09-16 出版日期:2019-12-20 发布日期:2020-01-15
  • 通讯作者: 孙哲南 E-mail:znsun@nlpr.ia.ac.cn
  • 作者简介:孙哲南,1976年生,中国科学院自动化研究所,副总工程师,研究员,博士生导师,中国科学院大学人工智能学院岗位教授,天津中科智能识别产业技术研究院院长,国际模式识别学会会士IAPR Fellow和生物特征识别技术委员会主席,担任国际期刊IEEE Transactions on Biometrics, Behavior, and Identity Science编委。主要研究方向为生物特征识别、模式识别、计算机视觉,发表国际期刊和会议论文200多篇,SCI他引2000多次,H-index指数43,获得国家技术发明二等奖和中国专利优秀奖。本文负责第一章内容。
    Sun Zhenan was born in 1976. He is currently a professor in the Institute of Automation, Chinese Academy of Sciences (CASIA). He received the PhD degree in pattern recognition and intelligent systems from CASIA in 2006. His research interests include biometrics, pattern recognition, and computer vision. He has published more than 200 papers with more than 2000 citations from SCI papers. He serves as an Associate Editor of the IEEE Transactions on Biometrics, Behavior, and Identity Science. He is a fellow of the IAPR. He is the corresponding author of the paper and his contribution is writing Section 1.|张兆翔,1983年生,中国科学院自动化研究所模式识别国家重点实验室,研究员,博士生导师,中国科学院脑科学与智能技术卓越创新中心年轻骨干,中国科学院大学岗位教授,IEEE高级会员,中国计算机学会杰出会员、中国人工智能学会杰出会员,入选“国家第四批万人计划青年拔尖人才”。张兆翔博士致力于生物认知启发的视觉感知与理解的理论与方法研究,在可用信息建模和基于模型的物体识别问题上开展了系统工作,在面向国家公共安全和智慧城市监管需求的系统平台上取得成功应用,取得显著社会影响和经济效益,近五年来在国际主流学术期刊与会议上发表论文100余篇,SCI收录期刊论文40余篇,担任了AAAI、IJCAI、NIPS、ICPR等多个国际会议的Area Chair、Senior PC或者PC,SCI期刊《Pattern Recognition》编委、《Neurocomputing》编委、《IEEE Access》编委。在本文中承担基于类脑智能的计算机视觉部分。
    Zhang Zhaoxiang received his BSc degree in Department of Electronic Science and Technology from University of Science and Technology of China, and the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences in 2004 and 2009, respectively. From 2009 to 2015, he worked as a Lecturer, Associate Professor, and later the deputy director of Department of Computer Application Technology at the Beihang University. Since July 2015, Dr. Zhang has joined the National Laboratory of Pattern Recognition (NLPR) where he is currently a Professor. His major research interests include pattern recognition, computer vision, machine learning and bio-inspired visual computing. He has published more than 150 papers in reputable conferences and journals. He has won the best paper awards in several conferences and championships in international competitions. He has served as the area chair, senior PC or PC of many international conferences like CVPR, ICCV, AAAI, IJCAI. He is the associate editor or guest associate editor of Pattern Recognition, NeuroComputing, Pattern Recognition Letters, Cognitive Computation, IEEE Access and Frontiers of Computer Science. His contribution to this paper is the part of brain-like computer vision.
    E-mail: zhaoxiang.zhang@ia.ac.cn|王威,1983年生,中国科学院自动化研究所,副研究员。2005年获武汉大学自动化专业学士学位,2011年获中国科学院大学计算机应用技术博士学位,同年加入中国科学院自动化研究所模式识别国家重点实验室。主要从事计算机视觉、认知计算的研究工作,目前研究主要集中在视觉认知机制计算建模、视觉语言协同理解。发表国际期刊和会议论文30多篇(包括TPAMI、TIP、TMM、NIPS、ICCV、CVPR等,其中CVPR2010他引200余次,CVPR2015他引760多次),获得CVPR DeepVision Workshop 2014最佳论文奖、ICPR2014最佳学生论文奖,相关研究成果授权和申请专利10余项,主持国家自然科学基金项目(3项)、企业合作项目(国家电网、高德、富士通)等科研项目10余项。在本文中承担计算机视觉技术进展部分。
    Wang Wei received his B.E. degree in Department of Automation from Wuhan University in 2005, and Ph.D. degree in University of Chinese Academy of Sciences in 2011. He is currently an associate professor in National Lab of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA). His research interests focus on computer vision and machine learning, particularly on the computational modelling of visual attention and memory, vision and language understanding. He has published more than thirty papers in refereed international journals and conferences such as TPAMI, TIP, CVPR, ICCV and NIPS. His contribution to this paper is the part of progress of computer vision.
    E-mail: wei.wang@nlpr.ia.ac.cn|刘菲,1986年生,中国科学院自动化研究所智能感知与计算研究中心,博士后,2017年于中国科学院大学获得博士学位,主要研究方向为生物特征识别、计算成像、计算机视觉。在本文中参与第2章内容。
    Liu Fei is a Postdoc in Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), China. She received the Ph.D. degree in University of Chinese Academy of Sciences in 2017. Her research focuses on biometrics, computational imaging and computer vision. Her contribution to this paper is the part of Section 2.
    E-mail: fei.liu@nlpr.ia.ac.cn|谭铁牛,1964年生,中国科学院自动化研究所,研究员,中国科学院院士、发展中国家科学院院士、英国皇家工程院外籍院士、巴西科学院通讯院士、IEEE Fellow,主要研究方向为生物特征识别、模式识别、计算机视觉,出版著作11部,发表国际期刊和会议论文450篇。在本文负责论文总体架构和主要思路。
    Tan Tieniu is currently a professor with the Center for Research on Intelligent Perception and Computing, NLPR, CASIA, China. He has published more than 450 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. His research interests include biometrics, image and video understanding, information hiding, and information forensics. He is a fellow of the CAS, the TWAS, the BAS, the IEEE, the IAPR, the UK Royal Academy of Engineering, and the Past President of IEEE Biometrics Council. He is a fellow of the IEEE. His contribution to this paper include constructing manuscript structure and main ideas.
    E-mail: tnt@nlpr.ia.ac.cn
  • 基金资助:
    国家自然科学基金国家重大科研仪器研制项目“复杂场景中多模态生物特征获取设备”(61427811)

Artificial Intelligence: Developments and Advances in 2019

Sun Zhenan1,2,3,*(),Zhang Zhaoxiang1,2,3,Wang Wei1,Liu Fei1,Tan Tieniu1,2,3   

  1. 1. Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
    3. CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing 100190, China
  • Received:2019-09-16 Online:2019-12-20 Published:2020-01-15
  • Contact: Sun Zhenan E-mail:znsun@nlpr.ia.ac.cn

摘要:

【目的】人工智能已经成为社会各界最为关注的热点技术方向,本文旨在让政府部门、科技人员、产业人员和社会大众了解人工智能领域的最新动态。【方法】本文重点梳理了 2019 年人工智能新闻事件和科技文献,对人工智能领域总体发展态势进行概括和总结,对人工智能重点技术进展进行综述和展望。【结果】2019 年人工智能发展更加理性务实,一方面呈现出“瓜熟蒂落、水到渠成”的繁荣景象,在万物互联、大数据、超级计算、深度学习时代实现了技术应用和产业落地,另一方面我们也可以看到基于深度神经网络的技术创新开始进入平稳期,科研人员开始探索增强学习、自动化机器学习、脑机接口、类脑智能、可解释人工智能等新方向,在人工智能创新触发期“芽苞初放,生机勃勃”。【局限】本文以介绍人工智能技术进展为主,没有对技术未来方向进行全面预测。【结论】2019 年人工智能在神经形态芯片、脑机融合、人机博弈、智能机器人、计算机视觉、自然语言理解等领域都取得重要技术进展,但是我们看到迈向通用人工智能仍然任重道远,人工智能领域亟待革命性的技术创新和突破。

关键词: 人工智能, 计算机视觉, 神经形态芯片, 脑智融合, 智能机器人

Abstract:

[Objective] Artificial Intelligence (AI) has become a hot technology topic in the society. This paper aims to provide updated information of AI developments and advances to government, research community, industry and public. [Methods] This paper mainly reviews AI news and scientific publications in 2019 to summarize the overall technology progresses of AI. [Results] The AI community has become more rational and pragmatic relatively in 2019. On one hand, AI technologies are getting mature with more and more successful applications in the era of Internet of Things, Big Data, Super-computing, and Deep Learning. On the other hand, there are more and more promising and new directions in AI researches such as reinforcement learning, automatic machine learning, brain-machine interface, brain-like intelligence, and interpretable artificial intelligence. [Limitations] This paper mainly introduces the current development status of AI with limited efforts in prediction of future AI directions. [Conclusions] Great progress has been achieved in the areas of neuromorphic chips, brain-machine interface, human-computer game, intelligent robot, natural language understanding, and computer vision in 2019. But it is still a challenging task towards general AI. Technology breakthroughs are needed to achieve a new generation of AI.

Key words: artificial intelligence, computer vision, neuromorphic chips, brain-machine integrated intelligence, intelligent robot