Translation The following government-issued white paper describes China's approach to standards-setting for artificial intelligence. Appendices list all of China's current (as of January 2018) and planned AI standardization protocols, and provide examples of applications of AI by China's leading tech companies. Title Artificial Intelligence Standardization White Paper (2018 Edition) 2018 Author China Electronics Standardization Institute (CESI; ; ) is the "compiling unit" () for this white paper. The 2nd Industrial Department () of the Standardization Administration of China (SAC; ) is the "guidance unit" () for this white paper. Source CESI website, January 24, 2018. CESI is a think tank subordinate to the PRC Ministry of Industry and Information Technology (MIIT; ); CESI is also known as the 4th Electronics Research Institute (; ) of MIIT. SAC is a component of the PRC State Administration for Market Regulation (), a ministry-level agency under China's cabinet, the State Council. The Chinese source text is available online at: http://www.cesi.cn/images/editor/20180124/20180124135528742.pdf Translation Date Translator Editor May 12, 2020 Etcetera Language Group, Inc. Ben Murphy, CSET Translation Lead Contributing institutions (in no particular order) China Electronics Standardization Institute Shanghai Development Center of Computer (CESI) Software Technology Institute of Automation, Chinese Academy Shanghai Xiao-i Robot Technology Co., Ltd. of Sciences Beijing Institute of Technology Beijing iQIYI Technology Co., Ltd. Tsinghua University Beijing Yousheng Zhiguang Technology Co., Ltd. (mavsyin) Peking University Extreme Element (Beijing) Intelligent Technology Co., Ltd. Renmin University of China Beijing ByteDance Technology Co., Ltd. Beihang University Beijing Sensetime Technology Development Co., Ltd. iFLYTEK Co., Ltd. Zhejiang Ant Small and Micro Financial Services Group Co., Ltd. Huawei Technologies Co., Ltd. Baidu Netcom Science and Technology Co., Ltd. IBM China Co. Ltd. Intel (China) Co., Ltd. Alibaba Cloud Computing Ltd. IBM China Company Limited Panasonic Corporation of China Chongqing Kaize Technology Co., Ltd. (t jydin) China Telecom Corporation Limited Haier Industry Intelligence Research Institute Co., Ltd. Tencent Internet Plus (Shenzhen) Co., Ltd. Chongqing CloudWalk Technology Co. Ltd. Alibaba Network Technology Co., Ltd. Beijing DeepGlint Technology Limited Contents REFACE ESEARCHBACKROUN ESEARCHOBJECTIVESANSINIFICANCE UTINEOF/RTIFICIA5NTEIENCE/5 ( 4ISTORANCONCEPTOF/5( RIINSANHISTOROF/5( 1ONCEPTOF/5 3EATURESOF/5 ( /5REFERENCEARCHITECTURE, ( 1URRENT/5SITUATIONANTRENS ( 7E/5TECHNOOIES 8ACHINEEARNIN 7NOEERAPHS 9ATURAANUAEPROCESSIN( 4UMANCOMPUTERINTERACTION) 1OMPUTERVISION 0IOMETRICFEATURERECONITION- IRTUAREAITAUMENTEREAIT. RENSIN/5TECHNOOEVEOPMENT ( 1URRENTSITUATIONANTRENSOFTHE/5INUSTR 5NTEIENTINFRASTRUCTURE 5NTEIENTINFORMATIONANATA( 5NTEIENTTECHNOOSERVICES( 5NTEIENTPROUCTS) 5NUSTRIAAPPICATIONSOF/5 /5INUSTRTRENS. 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ECURITETHICSSTANARS ( TANARSTHATURENTNEETOBEFORMUATEINTHENEARTERM 7ERECOMMENATIONSFOR/5STANARIATIONORK) II 1 Preface 1.1 Research background The concept of artificial intelligence (AI) was born in 1956. Over the course of more than a half century of development, AI technology and applications have experienced many ups and downs due to the influence of intelligent algorithms, computing speeds, data storage levels, and other factors. Since 2006, tremendous success has been achieved in fields like machine vision and speech recognition using machine learning algorithms represented by deep learning. The great improvements in recognition accuracy have once again made AI the object of widespread attention across academia and industry. At the same time that technologies like cloud computing and big data are boosting calculation speeds and lowering computing costs, they are also providing rich data resources for AI development, helping to train more intelligentized (l) algorithm models. The AI development model has also undergone a gradual transformation from seeking to "use computers to simulate artificial intelligence" to enhanced hybrid intelligent systems combining machines and humans. These use combinations of machines, humans, and networks to form collective intelligence systems, and use combinations of machines, humans, networks, and things to form more complex intelligent systems. As the core driving force behind a new round of industrial transformation, AI is spawning new technologies and new products. At the same time, AI is also playing a powerful empowering role for traditional industries. It has the potential to induce major changes in the industrial structure and to bring about an overall leap in social productive forces. AI can free humans from monotonous labor, as more and more simple, repetitive and dangerous tasks are being completed by AI systems. While reducing labor inputs and increasing work efficiency, AI systems can also do things faster and more accurately than humans. AI can find broad applications in such fields as education, medical treatment, eldercare, environmental protection, city operation, and justice services, and it is able to greatly improve the precision level of public services and increase the quality of people's lives across the board. AI can also help humanity accurately perceive, predict, and warn of major situations in the operation of infrastructure and public security, promptly grasp changes in collective consciousness and psychology, take the initiative to make decisions in reaction to events, and significantly improve social governance capabilities while safeguarding public security. Since AI is a future-shaping strategic technology, the world's developed nations are all striving for dominance in a new round of international competition, and issuing plans and policies centered around AI. They are making deployments for core AI technologies, top AI talents, and AI standards and norms, and are accelerating the development of AI technologies and industries. The major technology companies are constantly enlarging their investments of money and manpower to seize the high ground in AI development. In 2017, China released the New Generation Artificial Intelligence Development Plan (Guo Fa [2017] No. 35), the Three Year Action Plan to Promote the Development of the New Generation Artificial Intelligence Industry, 2018-2020 (No. 231 [2018] of the Ministry of Industry and Information Technology) and other policy documents to promote the R&D and industrialized development of AI technology. Currently, there is a certain technological and industrial foundation for AI development in China, with an array of AI companies concentrated in the microchip, data, platform, and applications fields. They have achieved good initial results in some directions 1 and moved towards commercialized development. For example, applications have been achieved in such fields as the financial, security, and service industries; and in specific tasks, the degree of accuracy and effectiveness of semantic recognition, speech recognition, facial recognition, image recognition technologies already far exceed those of humans. Standardization work plays fundamental, supportive, and guiding roles for AI and its industrial development. It represents both a key lever for promoting the development of industry innovation, as well as the commanding heights of industry competition. At present, while AI-related products and services in China are growing increasingly abundant, problems with inadequate degrees of standardization are also surfacing. AI involves numerous fields. Although some fields already have a certain foundation of standardization, these scattered standardization efforts are insufficient to fully support the entire AI field. In addition, AI is an emerging field that is just beginning to take off. On a global scale, standardization work is still in its infancy, and a complete system of standards has yet to take shape. With China and the rest of the world basically on the same starting line, a window of opportunity exists for breakthroughs. As long as we take aim at that opportunity and deploy rapidly, it will be entirely possible to seize the commanding heights of standards innovation. Otherwise, a good opportunity may be lost. Consequently, there is an urgent need to take advantage of the opportunity at hand, accelerate research on AI technology and industry development, systematically sort through and develop systems of standards for various AI fields, clarify the relationships of dependency and constraint between standards, establish unified and complete systems of standards, and use