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Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 12, Pages 1551-1684 doi: 10.1631/FITEE.2100463
Keywords： 多重知识表达；人工智能；大数据
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
Challenges and opportunities: from big data to knowledge inAI 2.0 Review
Yue-ting ZHUANG,Fei WU,Chun CHEN,Yun-he PAN
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 3-14 doi: 10.1631/FITEE.1601883
Keywords： Deep reasoning Knowledge base population Artificial general intelligence Big data Cross media
Multiple Knowledge Representation of Artificial Intelligence
Yunhe Pan
Engineering 2020, Volume 6, Issue 3, Pages 216-217 doi: 10.1016/j.eng.2019.12.011
Ruben Foresti, Stefano Rossi, Matteo Magnani, Corrado Guarino Lo Bianco, Nicola Delmonte
Engineering 2020, Volume 6, Issue 7, Pages 835-846 doi: 10.1016/j.eng.2019.11.014
The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education.
Keywords： Smart maintenance Smart society Artificial intelligence Human-centered management system Big data scheduling Global standard method Society 5.0 Industry 4.0
Miniaturized five fundamental issues about visual knowledge Perspectives
Yun-he Pan,panyh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2040000
Keywords： 视觉知识表达；视觉识别；视觉形象思维模拟；视觉知识学习；多重知识表达
Strategies and Principles of Distributed Machine Learning on Big Data Review
Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei
Engineering 2016, Volume 2, Issue 2, Pages 179-195 doi: 10.1016/J.ENG.2016.02.008
The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area that lies between ML and systems.
Keywords： Machine learning Artificial intelligence big data Big model Distributed systems Principles Theory Data-parallelism Model-parallelism
Design and Application of Clinical Big Data Management System for Oncology
Ma Lin, Bao Chenlu, Li Qing,Wu Jingyi, Pan Hong’an, Li Pengfei, Zhang Luxia, Zhan Qimin
Strategic Study of CAE 2022, Volume 24, Issue 6, Pages 127-136 doi: 10.15302/J-SSCAE-2022.06.011
Cancer is a serious threat to human life and health. Along with the development of medical informatization in China, healthcare institutions have cumulated a great quantity of clinical data in oncology; however, these data have not been fully explored owing to the disunity of data standards and great difficulties in data management. Hence, establishing a national clinical big data management system for oncology based on artificial intelligence could potentially promote the application of clinical data in oncology, further improving the quality and efficiency of clinical management for oncology. This study conducted an in-depth analysis of the problems and challenges of clinical data management and application for oncology and presented the significant values of an oncology clinical data management system. Considering the complexity of multi-source and multi-modal data in oncology, we explored the possible mechanisms and pathways of applying artificial intelligence to the management and research of clinical data for oncology Furthermore, a full-circle solution was designed, and the construction framework and technology systems were promoted for the clinical data management system for oncology, including the development of common data models for oncology, data collection and security management, data standardization and structuring, data analysis and application, and data quality control. Besides, we validated the feasibility and benefits of the promoted system in clinical practice by taking the clinical data management for lung cancer in a tertiary hospital as an example. Finally, we proposed some suggestions on the research directions of the clinical big data management system for oncology.
Keywords： clinical big data management system oncology artificial intelligence common data model natural language processing
AI Assisted Clinical Diagnosis & Treatment, and Development Strategy
Kong Ming,He Qianfeng and Li Lanjuan
Strategic Study of CAE 2018, Volume 20, Issue 2, Pages 86-91 doi: 10.15302/J-SSCAE-2018.02.013
The integration, open accessing of healthcare data, and the use of artificial intelligence to organize and analyze fragmented medical information can improve medical and health services, promote the level of rational government decision-making, and reduce the inequality in the allocation of medical and health resources. This paper summarizes the current status of technologies and applications of artificial intelligence in the field of medical information semantic fusion and in the field of image analysis, and analyzes current problems and challenges. The first is the standardized representation and structural integration of medical information to merge national and widely-used clinical terminologies, which is key to realizing auxiliary diagnosis based on ‘big data’ artificial intelligent. The second is the use of massive medical knowledge to construct an intelligent diagnosis and treatment model with the ability to combine multimodal data analysis and structured knowledge reasoning. Thus, we propose a national-level healthcare open data cloud platform that can help open up new data markets, improve the integration of healthcare data, and provide the new service of knowledge discovery and services. We also suggest to establish some basic industry standards for medical and health information, to strengthen the research and development of domestic medical devices, to promote the development of intelligent medical devices and smart wearable devices, and to guide the industry to open up new markets on the combination of artificial intelligence and medical devices.
Keywords： artificial intelligence assisted diagnosis and treatment knowledge graph medical ontology medical image analysis
Development of Key Technologies for Intelligent Research and Development of New Materials
Su Yanjing, Yang Mingli, Zhu Weili, Zhou Kechao, Xue Dezhen, Wang Hong, Xie Jianxin
Strategic Study of CAE 2023, Volume 25, Issue 3, Pages 161-169 doi: 10.15302/J-SSCAE-2023.03.015
The rapid development of key technologies for the intelligent research and development (R&D) of new materials has significantly promoted the R&D efficiency and industrialization of materials and attracted global attention. China’s development in this field lags behind the advanced international level in terms of key technologies and infrastructures, which has restricted the original innovation and industrial development of the material sector. This study summarizes the key technologies involving the intelligent R&D of new materials, explores the developing status of these key technologies in China and abroad, and analyzes the challenges faced by China. Moreover, the intelligent R&D technology system is summarized including intelligent computing and design technologies and software, autonomous/intelligent experiment technologies and equipment, artificial-intelligence-driven basic algorithms and technologies, digital twins, intelligent R&D platforms and collaborative innovation networks. Furthermore,countermeasures are proposed from the aspects of innovative ecology construction, industrial environment improvement, standards system establishment, talent training, and international cooperation.
Keywords： new materials artificial intelligence autonomous experimentation intelligent computing big data of materials
Intelligent Products and Equipment Led by New-Generation Artificial Intelligence
Tan Jianrong, Liu Zhenyu, Xu Jinghua
Strategic Study of CAE 2018, Volume 20, Issue 4, Pages 35-43 doi: 10.15302/J-SSCAE-2018.04.007
Intelligent products and equipment is the value carrier, technological prerequisite and material base of intelligent manufacturing and service. The intelligent products and equipment refers to two dialectical aspects: on the one hand, commercialization of intelligent technology, turning intelligence technology into products, which is mainly reflected in the comprehensive application of the Internet of Things, big data, cloud computing, edge computing, machine learning, deep learning, security monitoring, automation control, computer technology, precision sensing technology, and GPS positioning technology; On the other hand, the intelligent products and equipment refers to the intellectualization of traditional products. The new-generation artificial intelligence endows traditional products with higher intelligence and injects strong vitality and developmental motivation into traditional products in the aspect of intelligent manufacturing equipment, intelligent production, and intelligent management. Based on extensive scientific surveys and current researches, and combined with the ten major fields of Made in China 2025 and macro policies such as the Three-Year Action Plan for Artificial Intelligence, twelve major equipment fields of intelligent products and equipment are formulated. Researches show that the new-generation intelligent products and equipment focuses on knowledge engineering and is prominently characterized by self-sensing, self-adaptation, self-learning, and self-decision-making. Ten key technologies will be prioritized in future.
Keywords： intelligent products and equipment knowledge engineering intelligent state sensing intelligent variation adaptation intelligent knowledge learning intelligent control decision
Development and Prospect of Big Data Knowledge Engineering
Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun
Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 208-220 doi: 10.15302/J-SSCAE-2023.02.018
Big Data Knowledge Engineering is the infrastructure of artificial intelligence, a common requirement faced by various industries and fields, and the inevitable path for the digitalization to intelligence. In this paper, we firstly elaborate on the background and connotation of big data knowledge engineering and propose a research framework of “data knowledgeization, knowledge systematization, and knowledge reasoning”. Secondly, we sort out the key technologies of knowledge acquisition and fusion, knowledge representation, and knowledge reasoning and introduce engineering applications in typical scenarios such as smart education, tax risk control, and smart healthcare. Thirdly, we summary the challenges faced by big data knowledge engineering and predict the future research directions including complex big data knowledge acquisition, knowledge+data hybrid learning, and brain-inspired knowledge coding and memorizing. Finally, several suggestions are given by the research: guiding interdisciplinary integration and establishing major and key R&D projects to promote the basic theory and technological breakthroughs of big data knowledge engineering; strengthening communication and cooperation between enterprises and research institutions as well as promoting cutting-edge research results to form application demonstrations, so as to establish an industry-standard system for big data knowledge engineering; exploring school-enterprise cooperation in line with market demands, orienting towards major application needs, and accelerating the landing application of big data knowledge engineering technology in the country's important industries.
Keywords： Big Data Knowledge Engineering Knowledge Acquisition Knowledge Fusion Knowledge Representation Knowledge Reasoning
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 221-231 doi: 10.15302/J-SSCAE-2023.07.005
Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical values. In this study, following an analysis of the development status and challenges facing China’s intelligent taxation field, we proposed a big data knowledge engineering approach that emphasizes data knowledgeization, knowledge systematization, and knowledge reasonability, and developed a five-layer technical architecture that consists of knowledge sources, knowledge extraction, knowledge mapping, knowledge reasoning, and application layers. After elaborating the representative application scenarios including knowledge-driven tax preference calculation, interpretable tax risk identification, intelligent decision support for tax policies, and smart tax questioning,we investigated the limitations of the proposed approach and further discussed the directions for future research. Furthermore, we proposed the following development suggestions in terms of data, technology, and ecology: (1) standardizing tax-related information and improving the national data sharing, opening, and guarantee system; (2) integrating the achievements of various information disciplines and improving the application system of big data knowledge engineering for smart taxation; and (3) promoting talent training and the development of technical standards for big data knowledge engineering.
Keywords： smart taxation knowledge engineering big data knowledge graph knowledge reasoning
Title Author Date Type Operation
Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
Yi Yang, Yueting Zhuang, Yunhe Pan,yangyics@zju.edu.cn,yzhuang@zju.edu.cn,panyh@zju.edu.cn
Journal Article
Challenges and opportunities: from big data to knowledge inAI 2.0
Yue-ting ZHUANG,Fei WU,Chun CHEN,Yun-he PAN
Journal Article
Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance
Ruben Foresti, Stefano Rossi, Matteo Magnani, Corrado Guarino Lo Bianco, Nicola Delmonte
Journal Article
Miniaturized five fundamental issues about visual knowledge
Yun-he Pan,panyh@zju.edu.cn
Journal Article
Strategies and Principles of Distributed Machine Learning on Big Data
Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei
Journal Article
Design and Application of Clinical Big Data Management System for Oncology
Ma Lin, Bao Chenlu, Li Qing,Wu Jingyi, Pan Hong’an, Li Pengfei, Zhang Luxia, Zhan Qimin
Journal Article
AI Assisted Clinical Diagnosis & Treatment, and Development Strategy
Kong Ming,He Qianfeng and Li Lanjuan
Journal Article
Development of Key Technologies for Intelligent Research and Development of New Materials
Su Yanjing, Yang Mingli, Zhu Weili, Zhou Kechao, Xue Dezhen, Wang Hong, Xie Jianxin
Journal Article
Intelligent Products and Equipment Led by New-Generation Artificial Intelligence
Tan Jianrong, Liu Zhenyu, Xu Jinghua
Journal Article
Development and Prospect of Big Data Knowledge Engineering
Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun
Journal Article
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Journal Article
2020 年大数据、人工智能与物联网工程国际会议（ICBAIE2020）
12 Jun 2020
Conference Information
2020 年大数据、人工智能与软件工程国际会议
23 Oct 2020
Conference Information
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