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New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6, Pages 984-990 doi: 10.1631/FITEE.2100227
This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.
Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算
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 in Healthcare: Review and Prediction Case Studies Review
Guoguang Rong, Arnaldo Mendez, Elie Bou Assi, Bo Zhao, Mohamad Sawan
Engineering 2020, Volume 6, Issue 3, Pages 291-301 doi: 10.1016/j.eng.2019.08.015
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.
Keywords： Artificial intelligence Machine learning Deep learning Neural network Biomedical research Healthcare applications Epileptic seizure Urinary bladder filling
Visual interpretability for deep learning: a survey Review
Quan-shi ZHANG, Song-chun ZHU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 27-39 doi: 10.1631/FITEE.1700808
Keywords： Artificial intelligence Deep learning Interpretable model
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
Complexity at Mesoscales: A Common Challenge in Developing Artificial Intelligence Perspective
Li Guo, Jun Wu, Jinghai Li
Engineering 2019, Volume 5, Issue 5, Pages 924-929 doi: 10.1016/j.eng.2019.08.005
Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world. The emergence of big data and the enhancement of computing power, in conjunction with the improvement of optimization algorithms, are leading to the development of artificial intelligence (AI) driven by deep learning. However, deep learning fails to reveal the underlying logic and physical connotations of the problems being solved. Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems, and its capability for analyzing complex problems has been validated in different fields. This paper proposes a research paradigm for AI, which introduces the analytical principles of mesoscience into the design of deep learning models. This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved; the purpose is to promote the sustainable development of AI.
Keywords： Artificial intelligence Deep learning Mesoscience Mesoscale Complex system
Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8, Pages 1117-1142 doi: 10.1631/FITEE.2200314
Keywords： Artificial intelligence (AI) Machine learning (ML) Deep learning (DL) Optimization algorithm Hybrid algorithm Cyberspace security
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring Review
Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki
Engineering 2019, Volume 5, Issue 2, Pages 199-222 doi: 10.1016/j.eng.2018.11.030
Computer vision techniques, in conjunction with acquisition through remote cameras and unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure condition assessment. The ultimate goal of such a system is to automatically and robustly convert the image or video data into actionable information. This paper provides an overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment. In particular, relevant research in the fields of computer vision, machine learning, and structural engineering are presented. The work reviewed is classified into two types: inspection applications and monitoring applications. The inspection applications reviewed include identifying context such as structural components, characterizing local and global visible damage, and detecting changes from a reference image. The monitoring applications discussed include static measurement of strain and displacement, as well as dynamic measurement of displacement for modal analysis. Subsequently, some of the key challenges that persist towards the goal of automated vision-based civil infrastructure and monitoring are presented. The paper concludes with ongoing work aimed at addressing some of these stated challenges.
Keywords： Structural inspection and monitoring Artificial intelligence Computer vision Machine learning Optical flow
Intelligent Petroleum Engineering Perspective
Mohammad Ali Mirza, Mahtab Ghoroori, Zhangxin Chen
Engineering 2022, Volume 18, Issue 11, Pages 27-32 doi: 10.1016/j.eng.2022.06.009
Data-driven approaches and AI algorithms are promising enough to be relied on even more than physics-based methods; their main feed is data which is the fundamental element of each phenomenon. These algorithms learn from data and unveil unseen patterns out of it. The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology. As the oil and gas industry is in the transition phase to oilfield digitization, there has been an increased drive to integrate data-driven modeling and machine learning algorithms in different petroleum engineering challenges. ML has been widely used in different areas of the industry. Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry; however, lack of two main features is noticeable. Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable. Attention must be given to data itself and the way it is classified and stored. Although there are sheer volumes of data coming from different disciplines, they reside in departmental silos and are not accessible by consumers. In order to derive as much insight as possible out of data, the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.
Keywords： Artificial intelligence Machine learning Intelligent reservoir engineering Text mining Intelligent geoscience Intelligent drilling engineering
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats Perspective
Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem
Engineering 2021, Volume 7, Issue 9, Pages 1201-1211 doi: 10.1016/j.eng.2021.03.019
Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
Keywords： Artificial intelligence Machine learning Reaction engineering Process engineering
Communicative Learning: A Unified Learning Formalism Review
Luyao Yuan, Song-Chun Zhu
Engineering 2023, Volume 25, Issue 6, Pages 77-100 doi: 10.1016/j.eng.2022.10.017
In this article, we propose a communicative learning (CL) formalism that unifies existing machine learning paradigms, such as passive learning, active learning, algorithmic teaching, and so forth, and facilitates the development of new learning methods. Arising from human cooperative communication, this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning. The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data, such as intentional messages given by a helpful teacher. More specifically, in CL, a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge. Each agent has a mind, which includes the agent's knowledge, utility, and mental dynamics. To establish effective communication, each agent also needs an estimation of its partner's mind. We define expressive mental representations and learning formulation sufficient for such recursive modeling, which endows CL with human-comparable learning efficiency. We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon's communication limit. Finally, we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.
Keywords： Artificial intelligencehine Cooperative communication Machine learning Pedagogy Theory of mind
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
Pieter P. Plehiers, Steffen H. Symoens, Ismaël Amghizar, Guy B. Marin, Christian V. Stevens, Kevin M. Van Geem
Engineering 2019, Volume 5, Issue 6, Pages 1027-1040 doi: 10.1016/j.eng.2019.02.013
Chemical processes can benefit tremendously from fast and accurate effluent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these fields, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning artificial neural networks (DL ANNs) has been developed for the largest chemicals production process—steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker effluent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed characterization of a naphtha is predicted from three points on the boiling curve and PIONA (paraffin, isoparaffin, olefin, naphthene, and aromatics) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the effluent prediction is 0.1 wt%. When combining all networks—using the output of the previous as input to the next—the effluent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major benefit is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of difficult-to-access process parameters and for the envisioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed networks drops significantly for naphthas that are highly dissimilar to those in the training set.
Keywords： Artificial intelligence Deep learning Steam cracking Artificial neural networks
A review of computer graphics approaches to urban modeling from a machine learning perspective Review Article
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 915-925 doi: 10.1631/FITEE.2000141
Keywords： 城市建模；计算机图形学；机器学习；深度学习
Smart grid dispatch powered by deep learning: a survey Review Article
Gang HUANG, Fei WU, Chuangxin GUO,huanggang@zju.edu.cn,wufei@zju.edu.cn,guochuangxin@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5, Pages 763-776 doi: 10.1631/FITEE.2000719
Keywords： Artificial intelligence Deep learning Power dispatch Smart grid
Title Author Date Type Operation
New directions for artificial intelligence: human, machine, biological, and quantum intelligence
Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO
Journal Article
Ethical Principles and Governance Technology Development of AI in China
Wenjun Wu, Tiejun Huang, Ke Gong
Journal Article
Artificial Intelligence in Healthcare: Review and Prediction Case Studies
Guoguang Rong, Arnaldo Mendez, Elie Bou Assi, Bo Zhao, Mohamad Sawan
Journal Article
Strategies and Principles of Distributed Machine Learning on Big Data
Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei
Journal Article
Complexity at Mesoscales: A Common Challenge in Developing Artificial Intelligence
Li Guo, Jun Wu, Jinghai Li
Journal Article
Artificial intelligence algorithms for cyberspace security applications: a technological and status review
Jie CHEN, Dandan WU, Ruiyun XIE,chenjie1900@mail.nwpu.edu.cn,wudd@cetcsc.com
Journal Article
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
Billie F. Spencer Jr.,Vedhus Hoskere,Yasutaka Narazaki
Journal Article
Intelligent Petroleum Engineering
Mohammad Ali Mirza, Mahtab Ghoroori, Zhangxin Chen
Journal Article
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben Van de Vijver, Christian V. Stevens, Kevin M. Van Geem
Journal Article
Intelligent Products and Equipment Led by New-Generation Artificial Intelligence
Tan Jianrong, Liu Zhenyu, Xu Jinghua
Journal Article
Artificial Intelligence in Steam Cracking Modeling: A Deep Learning Algorithm for Detailed Effluent Prediction
Pieter P. Plehiers, Steffen H. Symoens, Ismaël Amghizar, Guy B. Marin, Christian V. Stevens, Kevin M. Van Geem
Journal Article
A review of computer graphics approaches to urban modeling from a machine learning perspective
Tian Feng, Feiyi Fan, Tomasz Bednarz,t.feng@latrobe.edu.au
Journal Article
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