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Ethical Principles and Governance Technology Development of AI in China Review
Wenjun Wu, Tiejun Huang, Ke Gong
Engineering 2020, Volume 6, Issue 3, Pages 302-309 doi: 10.1016/j.eng.2019.12.015
Ethics and governance are vital to the healthy and sustainable development of artificial intelligence (AI). With the long-term goal of keeping AI beneficial to human society, governments, research organizations, and companies in China have published ethical guidelines and principles for AI, and have launched projects to develop AI governance technologies. This paper presents a survey of these efforts and highlights the preliminary outcomes in China. It also describes the major research challenges in AI governance research and discusses future research directions.
Keywords： AI ethical principles AI governance technology Machine learning Privacy Safety Fairness
Artificial intelligence and statistics Perspective
Bin YU, Karl KUMBIER
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 6-9 doi: 10.1631/FITEE.1700813
Keywords： Artificial intelligence Statistics Human-machine collaboration
Development Strategy for the Core Software and Hardware of Artificial Intelligence in China
Gao Lei, Fu Yongquan, Li Dongsheng, Liao Xiangke
Strategic Study of CAE 2021, Volume 23, Issue 3, Pages 90-97 doi: 10.15302/J-SSCAE-2021.03.008
Artificial intelligence (AI) is an important enabling technology for promoting global digital development, and it is leading a new round of technological revolution and industrial transformation. Promoting AI core software and hardware technologies and industry is strategically significant for the national development, industrial upgrading, and productivity improvement in China. In this study, we summarize the development status of AI core hardware and software in China and abroad from the aspects of technology, industry, and policy, and analyze the problems faced by China’s development. Subsequently, we present the development ideas of China’s AI hardware and software technology and industry, propose the strategic objectives for 2025 and 2035, and summarize the key tasks for future development from the aspects of AI core hardware, AI core software, and AI-related basic technology. To provide references for the sustainable development of AI core hardware and software in China, we suggest that AI core software and hardware technologies should be included in the national top-level planning for science and technology innovation to acquire increased scientific research investment; AI open source platforms should be constructed and demonstrated; the research and development of AI key generic technologies should be promoted to achieve collaborative innovation; the AI industrial base should be reengineered to upgrade the industrial chain; and the AI innovative talent training system should be improved.
Keywords： artificial intelligence (AI) core software and hardware AI chip basic intelligent algorithm new enabling technology
Heading toward Artificial Intelligence 2.0
Yunhe Pan
Engineering 2016, Volume 2, Issue 4, Pages 409-413 doi: 10.1016/J.ENG.2016.04.018
With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.
Keywords： Artificial intelligence 2.0 Big data Crowd intelligence Cross-media Human-machine hybrid-augmented intelligence Autonomous-intelligent system
Yujia Peng,Jiaheng Han,Zhenliang Zhang,Lifeng Fan,Tengyu Liu,Siyuan Qi,Xue Feng,Yuxi Ma,Yizhou Wang,Song-Chun Zhu
Engineering 2024, Volume 34, Issue 3, Pages 12-22 doi: 10.1016/j.eng.2023.07.006
The release of the generative pre-trained transformer (GPT) series has brought artificial general intelligence (AGI) to the forefront of the artificial intelligence (AI) field once again. However, the questions of how to define and evaluate AGI remain unclear. This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions (DEPSI). More specifically, we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system. The Tong test describes a value- and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI, allowing for infinite task generation. We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized, quantitative, and objective benchmarks and evaluation of AGI.
Keywords： Artificial general intelligence Artificial intelligence benchmark Artificial intelligence evaluation Embodied artificial intelligence Value alignment Turing test Causality
All Set and Artificial Intelligence
Zhang Jiang,Lin Hua,He Zhongxiong
Strategic Study of CAE 2002, Volume 4, Issue 3, Pages 40-47
This paper presents a brand new set theory, All Set theory, which is the united set form of the current set theories including crisp set, fuzzy set, extension set, vague set, rough set, set pair analysis, FHW (fuzzy gray matter - element)，FEEC(fuzzy extension economic control) and so on. The operation of the all set is also discussed in detail. A kind of style of the human being' s intelligence can be described by a kind of set form, thus all set is the united form. An all set is comprised of four parts, that is ( A, B, F, J ). A is the universe of the problem discussed. One of the elements in A can be described by an element of B. F is the map from A to B. And J restricts F. From this model, the concept of subjection that is the basic conception of human´s intelligence can be simulated. Hence the wide application of all set theory in the field of artificial intelligence including pattern recognition, clustering, logic, machine learning, intelligent decision, etc. , can be developed. Especially the relation among all set, logic and human intelligence style is illustrated in the paper. The theory of all set can not only unify and summarize the current theories but also provide the primary method for establishing new set theory and new logic.
Keywords： all set artificial intelligence set theory operation logic
Cyber security meets artificial intelligence: a survey Review Article
Jian-hua LI
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 12, Pages 1462-1474 doi: 10.1631/FITEE.1800573
There is a wide range of interdisciplinary intersections between cyber security and artificial intelligence (AI). On one hand, AI technologies, such as deep learning, can be introduced into cyber security to construct smart models for implementing malware classification and intrusion detection and threating intelligence sensing. On the other hand, AI models will face various cyber threats, which will disturb their sample, learning, and decisions. Thus, AI models need specific cyber security defense and protection technologies to combat adversarial machine learning, preserve privacy in machine learning, secure federated learning, etc. Based on the above two aspects, we review the intersection of AI and cyber security. First, we summarize existing research efforts in terms of combating cyber attacks using AI, including adopting traditional machine learning methods and existing deep learning solutions. Then, we analyze the counterattacks from which AI itself may suffer, dissect their characteristics, and classify the corresponding defense methods. Finally, from the aspects of constructing encrypted neural network and realizing a secure federated deep learning, we expatiate the existing research on how to build a secure AI system.
Keywords： Cyber security Artificial intelligence (AI) Attack detection Defensive techniques
Development of Content Security Based on Artificial Intelligence
Zhu Shiqiang, Wang Yongheng
Strategic Study of CAE 2021, Volume 23, Issue 3, Pages 67-74 doi: 10.15302/J-SSCAE-2021.03.004
Content security refers to the protection of information content and that the information content meets the requirements at political, legal, and moral levels. The recent development of artificial intelligence (AI) has had a very important impact on content security. In this article, we summarize the research status and development trends of AI-based content security in China and abroad based on the major strategic demand therefor, and presents the key technical issues regarding AI-based content security. This study proposes to build the world’s leading AI-based content security system through a three-step strategy. Innovation and breakthroughs should be made in areas such as adversarial machine learning, explainable AI, hybrid enhanced intelligence, and knowledge-driven content security. Meanwhile, the construction of policies, regulations, and regulatory mechanisms should be emphasized. Furthermore, major content security infrastructure such as cyber ranges for content attack and defense and large-scale social system simulation devices for public opinion attack and defense should be established.
Keywords： artificial intelligence (AI)，content security，system construction
Jiang Changjun, Wang Junli
Strategic Study of CAE 2018, Volume 20, Issue 6, Pages 93-100 doi: 10.15302/J-SSCAE-2018.06.015
Artificial Intelligence (AI) aims to simulate information storage and processing mechanisms and other intelligent behaviors of a human brain, so that the machine has a certain level of intelligence. With the rapid development of the new generation of information technology, such as the Internet, big data, cloud computing, and deep learning, researches and applications of AI have made and are making important progresses. In this paper, the historical integration and evolution of computer science, control science, brain-inspired intelligence, human brain intelligence, and other disciplines or fields closely related to AI are analyzed in depth; then it is pointed out that the research results on the structure and functional mechanism of brain from neuroscience, brain science and cognitive science provide some important inspirations for the construction of an intelligent computing model. Moreover, the drives and developments of AI are discussed from the aspects of logic model and system, neuron network model, visual nerve hierarchy mechanism, etc. Finally, the development trend of AI is prospected from the following five aspects: the computational theory of the Internet, the integration of AI calculus and computation, the model and mechanism of brain-inspired intelligence, the impetus of AI to neuroscience, and the algorithm design of feedback computation and the energy level of the control system.
Keywords： artificial intelligence human brain intelligence brain-inspired intelligence intelligence development discipline evolution
From Brain Science to Artificial Intelligence Review
Jingtao Fan, Lu Fang, Jiamin Wu, Yuchen Guo, Qionghai Dai
Engineering 2020, Volume 6, Issue 3, Pages 248-252 doi: 10.1016/j.eng.2019.11.012
Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.
Keywords： Artificial intelligence Brain science
Shuai Mao, Bing Wang, Yang Tang, Feng Qian
Engineering 2019, Volume 5, Issue 6, Pages 995-1002 doi: 10.1016/j.eng.2019.08.013
Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.
Keywords： Process industry Smart manufacturing Green manufacturing Artificial intelligence
Research Group for Research on New Mode and Business Model of Manufacturing Led by New-Generation Artificial Intelligence Technology
Strategic Study of CAE 2018, Volume 20, Issue 4, Pages 66-72 doi: 10.15302/J-SSCAE-2018.04.011
Led by the development of artificial intelligence technology, the production technology, organization mode, and competitive strategy of the manufacturing industry are facing major changes, which also provides opportunities for formation of new modes and business models of manufacturing. Driven by the new generation of artificial intelligence technology, new modes and business models generated by convergence of the service industry and the manufacturing industry have emerged in the practice and thus serve as the focus of this study. The study has analyzed the development trends, typical types, and key platform technologies of the new modes and business models of manufacturing, and proposed the development guidelines, goals, and approaches for the new modes and business models. According to the basis and present situation of manufacturing in China, the study has selected the remote operation & maintenance service and mass customization as examples, and put forward the development directions, goals, and policy suggestions for the two business models in the related fields.
Keywords： artificial intelligence manufacturing new mode new business model
Li Ruiqi, Wei Sha, Cheng Yuhang, Hou Baocui
Strategic Study of CAE 2018, Volume 20, Issue 4, Pages 112-117 doi: 10.15302/J-SSCAE-2018.04.018
In terms of the artificial intelligence (AI) application in intelligent manufacturing, this paper analyzes the system realization form of intelligent manufacturing based on the definition of enterprise's key performance indicators (KPI), and further discusses the main role of AI in intelligent manufacturing. Based on the typical application scenarios of AI in intelligent manufacturing, this paper puts forward the application map of AI in intelligent manufacturing from the life cycle dimension, summarizes the common technologies in AI application to intelligent manufacturing, and illustrates the influence of AI on enterprises by taking production as an example. Finally, this paper puts forward the standards system of AI in intelligent manufacturing.
Keywords： artificial intelligence intelligent manufacturing enterprise’s KPI standardization
Mathematical Reasoning Challenges Artificial Intelligence
Sean O’Neill
Engineering 2019, Volume 5, Issue 5, Pages 817-818 doi: 10.1016/j.eng.2019.08.009
Artificial Intelligence Ethics Supervision in China: Demand Analysis and Countermeasures
Liu Lu, Yang Xiaolei, Gao Wen
Strategic Study of CAE 2021, Volume 23, Issue 3, Pages 106-112 doi: 10.15302/J-SSCAE-2021.03.006
The rapid development of artificial intelligence (AI) industry is accompanied by various social ethical risks, and the consequences of these risks have gradually emerged. Allowing humans to securely enjoy the major benefits of AI technology has become an important task of AI supervision. In this article, we first analyze the focus issues of AI ethics, including the distribution of machine and human rights, social trust crisis of AI, security of data and algorithms, as well as confirmation of rights and attribution of liability. Subsequently, we summarize the development paths and policy status of AI industry in China and abroad, and analyze the demand for AI ethical supervision in China from multiple dimensions. Furthermore, we suggest that the supervision scope of AI should be extended in a staged manner based on the technical progress of AI. To create better global development opportunities for China’s AI industry, a multi-dimensional supervision framework that combines ethics, law, and policy should be established, a sufficient social discussion space should be provided for all stakeholders to participate in public discussion on AI security, scientific and technological ethics supervision organizations of multiple levels should be improved in an orderly manner, and China should actively participate in the formulation of AI international rules.
Keywords： artificial intelligence (AI) governance structure ethical supervision ethics committee
Title Author Date Type Operation
Ethical Principles and Governance Technology Development of AI in China
Wenjun Wu, Tiejun Huang, Ke Gong
Journal Article
Development Strategy for the Core Software and Hardware of Artificial Intelligence in China
Gao Lei, Fu Yongquan, Li Dongsheng, Liao Xiangke
Journal Article
The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions
Yujia Peng,Jiaheng Han,Zhenliang Zhang,Lifeng Fan,Tengyu Liu,Siyuan Qi,Xue Feng,Yuxi Ma,Yizhou Wang,Song-Chun Zhu
Journal Article
Development of Content Security Based on Artificial Intelligence
Zhu Shiqiang, Wang Yongheng
Journal Article
Intelligence Originating from Human Beings and Expanding in Industry— A View on the Development of Artificial Intelligence
Jiang Changjun, Wang Junli
Journal Article
From Brain Science to Artificial Intelligence
Jingtao Fan, Lu Fang, Jiamin Wu, Yuchen Guo, Qionghai Dai
Journal Article
Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry
Shuai Mao, Bing Wang, Yang Tang, Feng Qian
Journal Article
Research on New Mode and Business Model of Manufacturing Led by New-Generation Artificial Intelligence Technology
Research Group for Research on New Mode and Business Model of Manufacturing Led by New-Generation Artificial Intelligence Technology
Journal Article
Research on Typical Application Scenarios and Standards System of Artificial Intelligence in Intelligent Manufacturing
Li Ruiqi, Wei Sha, Cheng Yuhang, Hou Baocui
Journal Article
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