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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
Machine Learning Produces Superhuman Chip Designs
Robert Pollie,
Engineering 2022, Volume 10, Issue 3, Pages 7-9 doi: 10.1016/j.eng.2022.01.006
Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints Article
Kun Li, Max Q.-H. Meng
Engineering 2015, Volume 1, Issue 1, Pages 79-84 doi: 10.15302/J-ENG-2015024
For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.
Keywords: personalized robot habit learning behavioral footprints
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
Li Sun, Fengqi You
Engineering 2021, Volume 7, Issue 9, Pages 1239-1247 doi: 10.1016/j.eng.2021.04.020
Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the "5-TYs”), respectively. Finally, an outlook on future research and applications is presented.
Keywords: Smart power generation Machine learning Data-driven control Systems engineering
A machine learning approach to query generation in plagiarism source retrieval Article
Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10, Pages 1556-1572 doi: 10.1631/FITEE.1601344
Keywords: Plagiarism detection Source retrieval Query generation Machine learning Learning to rank
Chao Shang、 Fengqi You
Engineering 2019, Volume 5, Issue 6, Pages 1010-1016 doi: 10.1016/j.eng.2019.01.019
Safe, efficient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is influencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning models. By analyzing the gap between practical requirements and the current research status, promising future research directions are identified.
Keywords: Big data Machine learning Smart manufacturing Process systems 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
Learning Rat-Like Behavior for a Small-Scale Biomimetic Robot
Zihang Gao, Guanglu Jia, Hongzhao Xie, Qiang Huang, Toshio Fukuda, Qing Shi
Engineering 2022, Volume 17, Issue 10, Pages 232-243 doi: 10.1016/j.eng.2022.05.012
Existing biomimetic robots can perform some basic rat-like movement primitives (MPs) and simple behavior with stiff combinations of these MPs. To mimic typical rat behavior with high similarity, we propose parameterizing the behavior using a probabilistic model and movement characteristics. First, an analysis of fifteen 10min video sequences revealed that an actual rat has six typical behaviors in the open
field, and each kind of behavior contains different bio-inspired combinations of eight MPs. We used the softmax classifier to obtain the behavior-movement hierarchical probability model. Secondly, we specified the MPs using movement parameters that are static and dynamic. We obtained the predominant values of the static and dynamic movement parameters using hierarchical clustering and fuzzy C-means
clustering, respectively. These predominant parameters were used for fitting the rat spinal joint trajectory using a second-order Fourier series, and the joint trajectory was generalized using a back propagation neural network with two hidden layers. Finally, the hierarchical probability model and the generalized joint trajectory were mapped to the robot as control policy and commands, respectively. We implemented the six typical behaviors on the robot, and the results show high similarity when compared with the behaviors of actual rats.
Keywords: Biomimetic Bio-inspired robot Neural network learning system Behavior generation
Machine Learning for Chemistry: Basics and Applications Review
Yun-Fei Shi, Zheng-Xin Yang, Sicong Ma, Pei-Lin Kang, Cheng Shang, P. Hu, Zhi-Pan Liu
Engineering 2023, Volume 27, Issue 8, Pages 70-83 doi: 10.1016/j.eng.2023.04.013
The past decade has seen a sharp increase in machine learning (ML) applications in scientific research. This review introduces the basic constituents of ML, including databases, features, and algorithms, and highlights a few important achievements in chemistry that have been aided by ML techniques. The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations. Important two-dimensional (2D) and three-dimensional (3D) features representing the chemical environment of molecules and solids are briefly introduced. Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios. Three important fields of ML in chemistry are discussed: ① retrosynthesis, in which ML predicts the likely routes of organic synthesis; ② atomic simulations, which utilize the ML potential to accelerate potential energy surface sampling; and ③ heterogeneous catalysis, in which ML assists in various aspects of catalytic design, ranging from synthetic condition optimization to reaction mechanism exploration. Finally, a prospect on future ML applications is provided.
Keywords: Machine learning Atomic simulation Catalysis Retrosynthesis Neural network potential
Teng Zhou, Zhen Song, Kai Sundmacher
Engineering 2019, Volume 5, Issue 6, Pages 1017-1026 doi: 10.1016/j.eng.2019.02.011
Materials development has historically been driven by human needs and desires, and this is likely to continue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-efficiency energy, personalized consumer products, secure food supplies, and professional healthcare. New functional materials that are made and tailored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic materials. Finally, concluding remarks and an outlook are provided.
Keywords: Big data Data-driven Machine learning Materials screening Materials design
Adversarial Attacks and Defenses in Deep Learning Feature Article
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu
Engineering 2020, Volume 6, Issue 3, Pages 346-360 doi: 10.1016/j.eng.2019.12.012
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical
to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of
DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various
misbehaviors of the DL models while being perceived as benign by humans. Successful implementations
of adversarial attacks in real physical-world scenarios further demonstrate their practicality.
Hence, adversarial attack and defense techniques have attracted increasing attention from both machine
learning and security communities and have become a hot research topic in recent years. In this paper,
we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques.
We then describe a few research efforts on the defense techniques, which cover the broad frontier
in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke
further research efforts in this critical area.
Keywords: Machine learning Deep neural network Adversarial example Adversarial attack Adversarial defense
Optically Digitalized Holography: A Perspective for All-Optical Machine Learning
Min Gu, Xinyuan Fang, Haoran Ren, Elena Goi
Engineering 2019, Volume 5, Issue 3, Pages 363-365 doi: 10.1016/j.eng.2019.04.002
Siwei Song, Yi Wang, Fang Chen, Mi Yan, Qinghua Zhang
Engineering 2022, Volume 10, Issue 3, Pages 99-109 doi: 10.1016/j.eng.2022.01.008
Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error. Herein, a methodology combining domain knowledge, a machine learning algorithm, and experiments is presented for accelerating the discovery of novel energetic materials. A high-throughput virtual screening (HTVS) system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established. With the proposed HTVS system, candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25 112 molecules. Furthermore, a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results, thus verifying the effectiveness of the proposed methodology. This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.
Keywords: Energetic materials Machine learning High-throughput virtual screening Molecular properties Synthesis
Automatic malware classification and new malwaredetection using machine learning Article
Liu LIU, Bao-sheng WANG, Bo YU, Qiu-xi ZHONG
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 9, Pages 1336-1347 doi: 10.1631/FITEE.1601325
Keywords: Malware classification Machine learning n-gram Gray-scale image Feature extraction Malware detection
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
Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints
Kun Li, Max Q.-H. Meng
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
Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective
Li Sun, Fengqi You
Journal Article
A machine learning approach to query generation in plagiarism source retrieval
Lei-lei KONG, Zhi-mao LU, Hao-liang QI, Zhong-yuan HAN
Journal Article
Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era
Chao Shang、 Fengqi You
Journal Article
Learning Rat-Like Behavior for a Small-Scale Biomimetic Robot
Zihang Gao, Guanglu Jia, Hongzhao Xie, Qiang Huang, Toshio Fukuda, Qing Shi
Journal Article
Machine Learning for Chemistry: Basics and Applications
Yun-Fei Shi, Zheng-Xin Yang, Sicong Ma, Pei-Lin Kang, Cheng Shang, P. Hu, Zhi-Pan Liu
Journal Article
Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design
Teng Zhou, Zhen Song, Kai Sundmacher
Journal Article
Adversarial Attacks and Defenses in Deep Learning
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu
Journal Article
Optically Digitalized Holography: A Perspective for All-Optical Machine Learning
Min Gu, Xinyuan Fang, Haoran Ren, Elena Goi
Journal Article
Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials
Siwei Song, Yi Wang, Fang Chen, Mi Yan, Qinghua Zhang
Journal Article