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INTERACTIVE KNOWLEDGE LEARNING BY ARTIFICIAL INTELLIGENCE FOR SMALLHOLDERS

《农业科学与工程前沿（英文）》 2023年第10卷第4期页码 648-653 doi: 10.15302/J-FASE-2023505

摘要：

Enhancement of farming management relies heavily on enhancing farmer knowledge. In the past, both the direct learning approach and the personnel extension system for improving fertilization practices of smallholders has proven insufficiently effective. Therefore, this article proposes an interactive knowledge learning approach using artificial intelligence as a promising alternative. The system consists of two parts. The first is a dialog interface that accepts information from farmers about their current farming practices. The second part is an intelligent decision system, which categorizes the information provided by farmers in two categories. The first consists of on-farm constraints, such as fertilizer resources, split application times and seasons. The second comprises knowledge-based practices by farmers, such as nutrient in- and output balance, ratios of different nutrients and the ratios of each split nutrient amount to the total nutrient input. The interactive knowledge learning approach aims to identify and rectify incorrect practices in the knowledge-based category while considering the farmer’s available finance, labor, and fertilizer resources. Investigations show that the interactive knowledge learning approach can make a strong contribution to prevention of the overuse of nitrogen and phosphorus fertilizers, and mitigating agricultural non-point source pollution.

关键词： artificial intelligence extension system non-point source pollution control smallholders fertilization

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Artificial intelligence in gastroenterology: where are we heading?

Joseph JY Sung, Nicholas CH Poon

《医学前沿（英文）》 2020年第14卷第4期页码 511-517 doi: 10.1007/s11684-020-0742-4

摘要： Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations.

关键词： artificial intelligence endoscopy robotics gastrointestinal diseases

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The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social

Yujia Peng,Jiaheng Han,Zhenliang Zhang,Lifeng Fan,Tengyu Liu,Siyuan Qi,Xue Feng,Yuxi Ma,Yizhou Wang,Song-Chun Zhu,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.006

摘要： 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.

关键词： Artificial general intelligence Artificial intelligence benchmark Artificial intelligence evaluation Embodied artificial intelligence Value alignment Turing test Causality

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智能源于人、拓于工——人工智能发展的一点思考

蒋昌俊,王俊丽

《中国工程科学》 2018年第20卷第6期页码 93-100 doi: 10.15302/J-SSCAE-2018.06.015

摘要：

人工智能（AI）旨在模拟人脑中信息存储和处理机制等智能行为，使机器具有一定程度的智能水平。随着互联网、大数据、云计算和深度学习等新一代信息技术的飞速发展，目前AI领域的研究和应用已经取得重要进展。本文将深入分析与AI密切相关的计算机科学、控制科学、类脑智能、人脑智能等学科或领域之间的交融与历史演进；指出神经科学、脑科学与认知科学中有关脑的结构与功能机制的研究成果，为构建智能计算模型提供了重要的启发，并从逻辑模型及系统、神经元及网络模型、视觉神经分层机制等方面，分别阐述智能的驱动与发展；最后从互联网的计算理论、AI的演算和计算的融合、类脑智能的模型和机理、AI对神经科学的推动作用、反馈计算的算法设计与控制系统的能级五个方面，对AI的发展趋势进行了展望。

关键词：人工智能人脑智能类脑智能智能发展学科演进

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人工智能走向2.0

潘云鹤

《工程（英文）》 2016年第2卷第4期页码 409-413 doi: 10.1016/J.ENG.2016.04.018

摘要：

随着互联网的普及、传感网的渗透、大数据的涌现、信息社区的崛起，以及数据和信息在人类社会、物理空间和信息空间之间的交叉融合与相互作用，当今人工智能(AI) 发展所处信息环境和数据基础已经发生了深刻变化，人工智能的目标和理念正面临重要调整，人工智能的科学基础和实现载体也面临新的突破，人工智能正进入一个新的阶段。这个源于传统而又与之不同的人工智能新阶段被称为人工智能2.0(AI 2.0)。本文从人工智能60 年的发展历史出发，通过分析促成人工智能2.0形成的外部环境与目标的转变，分析技术萌芽，提出了人工智能2.0 的核心理念，并结合中国发展的社会需求与信息环境特色，给出了发展人工智能2.0 的建议。

关键词：人工智能2.0 大数据群体智能跨媒体人机混合智能无人智能系统

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HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK

《农业科学与工程前沿（英文）》 2022年第9卷第1期页码 150-154 doi: 10.15302/J-FASE-2021419

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Artificial intelligence in radiotherapy: a technological review

Ke Sheng

《医学前沿（英文）》 2020年第14卷第4期页码 431-449 doi: 10.1007/s11684-020-0761-1

摘要： Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.

关键词： artificial intelligence radiation therapy medical imaging treatment planning quality assurance outcome prediction

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The linkage mechanism between urban intelligence and low carbon innovation

Ming LIN

《工程管理前沿（英文）》 2019年第6卷第4期页码 584-586 doi: 10.1007/s42524-019-0076-6

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Application of artificial intelligence in surgery

Xiao-Yun Zhou, Yao Guo, Mali Shen, Guang-Zhong Yang

《医学前沿（英文）》 2020年第14卷第4期页码 417-430 doi: 10.1007/s11684-020-0770-0

摘要： Artificial intelligence (AI) is gradually changing the practice of surgery with technological advancements in imaging, navigation, and robotic intervention. In this article, we review the recent successful and influential applications of AI in surgery from preoperative planning and intraoperative guidance to its integration into surgical robots. We conclude this review by summarizing the current state, emerging trends, and major challenges in the future development of AI in surgery.

关键词： artificial intelligence surgical autonomy medical robotics deep learning

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Determination of shear strength of steel fiber RC beams: application of data-intelligence models

Abeer A. AL-MUSAWI

《结构与土木工程前沿（英文）》 2019年第13卷第3期页码 667-673 doi: 10.1007/s11709-018-0504-4

摘要： Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process. This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization. For the considered data-intelligence models, the input matrix attribute is one of the central element in attaining accurate predictive model. Hence, various input attributes are constructed to model the shear strength “as a targeted variable”. The modeling is initiated using historical published researches steel fiber reinforced concrete beams information. Seven variables are used as input attribute combination including reinforcement ratio ( ), concrete compressive strength ( ), fiber factor ( ), volume percentage of fiber ( ), fiber length to diameter ratio ( ) effective depth ( ), and shear span-to-strength ratio ( ), while the shear strength ( ) is the output of the matrix. The best network structure obtained using the network having ten nodes and one hidden layer. The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams. In more representable details, the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82, respectively.

关键词： hybrid intelligence model shear strength prediction steel fiber reinforced concrete

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Can industrial intelligence promote industrial transformation? ––Case of mining enterprises

Anlin SHAO

《工程管理前沿（英文）》 2017年第4卷第3期页码 375-378 doi: 10.15302/J-FEM-2017108

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The imperative need to develop guidelines to manage human versus machine intelligence

Donald KENNEDY, Simon P. PHILBIN

《工程管理前沿（英文）》 2018年第5卷第2期页码 182-194 doi: 10.15302/J-FEM-2018085

摘要： Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions. Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area.

关键词： human intelligence & machine intelligence HI-MI decision-making artificial intelligence

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Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph

Yang Jiao, Zhan Zhang, Ting Zhang, Wen Shi, Yan Zhu, Jie Hu, Qin Zhang

《医学前沿（英文）》 2020年第14卷第4期页码 488-497 doi: 10.1007/s11684-020-0762-0

摘要： Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.

关键词： knowledge representation uncertain causality graphical model artificial intelligence diagnosis dyspnea

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人工智能与统计分析 Perspective

Bin YU, Karl KUMBIER

《信息与电子工程前沿（英文）》 2018年第19卷第1期页码 6-9 doi: 10.1631/FITEE.1700813

摘要：人工智能（artificial intelligence, AI）本质上是由数据驱动的。在其通过人机协作完成数据生成、算法开发与结果评估的任务中，需要应用许多统计概念。

关键词：人工智能；统计；人机协作

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Current applications of artificial intelligence for intraoperative decision support in surgery

Allison J. Navarrete-Welton, Daniel A. Hashimoto

《医学前沿（英文）》 2020年第14卷第4期页码 369-381 doi: 10.1007/s11684-020-0784-7

摘要： Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one ( =21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.

关键词： artificial intelligence decision support clinical decision support systems intraoperative deep learning computer vision machine learning surgery