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文学教育(上) 2017年第7期 7-11页

作者：李静睿

生活并不是一步走到今天,但当中的逻辑的确让人费解。"包夜"过后,我们甚至没有加对方的MSN,穿好衣服两个人交换了名片,那张名片我在回北京的飞机上撕掉,冲进马桶,不知道怎么回事,我记住了林夏的手机号码。

关键词：小叶手机转移性乳腺癌 ai

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ai快速发展对员工创新行为的影响

合作经济与科技 2025年第1期 96-98页

作者：席志东黑龙江大学经济与工商管理学院黑龙江哈尔滨

本文探讨ai快速发展对员工创新行为的影响。ai技术在多领域应用并取得显著成就,其发展趋势包括更强通用性、与其他技术融合、伦理法律规范完善、向边缘计算发展及人才需求增长。ai对员工创新行为有积极影响,但也存在消极影响。为应对这... 详细信息

本文探讨ai快速发展对员工创新行为的影响。ai技术在多领域应用并取得显著成就,其发展趋势包括更强通用性、与其他技术融合、伦理法律规范完善、向边缘计算发展及人才需求增长。ai对员工创新行为有积极影响,但也存在消极影响。为应对这些影响,员工应持续学习、培养软技能、建立个人品牌;组织应合理规划ai应用、建立创新激励机制、营造创新文化;社会应加强职业教育与培训、提供政策支持与引导、促进产学研合作。

关键词： ai 员工创新行为影响

The current research status and prospects of ai in chemical science

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Progress in Natural Science:Materials International 2024年第5期34卷 859-872页

作者： Minghao Yuan Qinglang Guo Yingxue Wang Beihang University CETC Academy of Electronics and Information Technology Group Co. Ltd China Academic of Electronics and Information Technology

This paper primarily examines the utilization and obstacles of ai in the domain of chemistry. Machine learning facilitates the advancement of chemical research at every level through the use of ai. ai has significantly contributed to enhancing the efficiency of chemical experiments and manufacturing, as well as reducing costs,throughout the many phases of chemical study, application, and production. Its impact is particularly notable in the development of new materials and the discovery of drugs. Nevertheless, the implementation of ai in the domain of chemistry encounters numerous obstacles, including inadequate data quality, limited model interpretability, and data privacy concerns. To address these issues, it is imperative for the scientific and technological community to foster multidisciplinary collaboration, develop a more comprehensive and practical ai framework,and investigate more secure data security technologies. In the future, as ai continues to advance, the relationship between ai and chemical research will become more dependable and intimate. This will lead to increased efficiency, safety, and cost-effectiveness in chemical research, ushering in a new era in the field of chemistry.

关键词： ai ai for chemistry science Chemistry Deep learning Machine learning

ai in Human-computer Gaming:Techniques,Challenges and Opportunities

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Machine Intelligence Research 2023年第3期20卷 299-317页

作者： Qi-Yue Yin Jun Yang Kai-Qi Huang Mei-Jing Zhao Wan-Cheng Ni Bin Liang Yan Huang Shu Wu Liang Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Automation Tsinghua UniversityBeijing 100084China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

With the breakthrough of AlphaGo,human-computer gaming ai has ushered in a big explosion,attracting more and more researchers all over the *** a recognized standard for testing artificial intelligence,various human-computer gaming ai systems(ais)have been developed,such as Libratus,Openai Five,and AlphaStar,which beat professional human *** rapid development of human-computer gaming ais indicates a big step for decision-making intelligence,and it seems that current techniques can handle very complex human-computer ***,one natural question arises:What are the possible challenges of current techniques in human-computer gaming and what are the future trends?To answer the above question,in this paper,we survey recent successful game ais,covering board game ais,card game ais,first-person shooting game ais,and real-time strategy game *** this survey,we 1)compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level ais;2)summarize the mainstream frameworks and techniques that can be properly relied on for developing ais for complex human-computer games;3)raise the challenges or drawbacks of current techniques in the successful ais;and 4)try to point out future trends in human-computer gaming ***,we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of ai in human-computer gaming.

关键词： Human-computer gaming ai intelligent decision making deep reinforcement learning self-play

ai-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

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Computers, Materials & Continua 2022年第12期73卷 5609-5624页

作者： Woo Hyun Park Isma Farah Siddiqui Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan

With the advent of the big data era,security issues in the context of artificial intelligence(ai)and data analysis are attracting research *** the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and ***,they can be exposed to ***,various hacker threats *** example,users’assets are exposed through notices and mail alerts regularly sent to users by *** the future,hacker threats will increase mainly due to naturally anonymous ***,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term ***,several application versions are ***,research on tasks and performance for algorithm application is *** propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the *** algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing ***,we create 24 topic-based *** normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

关键词： Metaverse security computational linguistics grouping bridgehead ai

ai Enlightens Wireless Communication: Analyses and Solutions for DMRS Channel Estimation

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China Communications 2023年第5期20卷 275-287页

作者： Bule Sun Zhiqin Wang Ang Yang Xiaofeng Liu Shi Jin Peng Sun Rakesh Tamrakar Dajie Jiang Vivo Mobile Communication Co. Ltd.Beijing 100015China China Academy of Information and Communications Technology Beijing 100191China National Mobile Communications Research Laboratory Southeast UniversityNanjing 211189China

In this paper,a systematic description of the artificial intelligence(ai)-based channel estimation track of the 2nd Wireless Communication ai Competition(WaiC)is provided,which is hosted by IMT-2020(5G)Promotion Group 5G+aiWork ***,the system model of demodulation reference signal(DMRS)based channel estimation problem and its corresponding dataset are *** the potential approaches for enhancing the performance of ai based channel estimation are discussed from the viewpoints of data analysis,pre-processing,key components and backbone network *** last,the final competition results composed of different solutions are *** is expected that the ai-based channel estimation track of the 2nd WaiC could provide insightful guidance for both the academia and industry.

关键词： MIMO DMRS channel estimation ai data analysis preprocessing model design

Application of ML&ai to model petrophysical and geomechanical properties of shale reservoirs e A systematic literature review

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Petroleum 2022年第2期8卷 158-166页

作者： Fahad I.Syed Abdulla AlShamsi Amirmasoud K.Dahaghi Neghabhan S The University of Kansas School of EngineeringChemical and Petroleum Engineering1450 Jayhawk Blvd.Lawrence66045USA

Extensive reviews and cross-comparison studies are essential to analyze the emerging developments in a specific field of *** the past decade,hydrocarbon exploration and exploitation from the shale reservoirs have been the most discussed and researched area around the globe.A dramatic development in shale formations became the game-changer,especially in the *** the other hand,Artificial Intelligence(ai)and Machine Learning(ML)are playing an important role in the rapid development in all the industries through automating most of the routine *** oil industry is also getting equal benefits of ai and ML for the reservoir development planning and its operational accuracy through a series of automated *** the field development,computerized static and dynamic simulation models are generated based on several Petrophysical and Geomechanical properties gathered through different resources that are quite time-taking and *** and ML have made this process much easier,faster,and economical by means of learning through uncounted experiences from already explored and developed reservoirs,their rock properties,and the crossponding fluid flow behavior under different circumstances and hence,predicts *** article provides a comprehensive literature review in the area of ai and ML applications to model Petrophysical and Geomechanical properties using different approaches and ***,a systematic publication counts in each field of subject study per year in different literature databases are presented that infect reflects the trending interest in this ***,multiple ai and ML techniques are discussed in detail which have been tested in the last decade for the sake of achieving higher accuracy in Petrophysical and Geo-Mechanical simulation models.

关键词： ai ML Petrophysics GeoMechanics

Analysis of the lifetime and culling reasons for ai boars

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Journal of Animal Science and Biotechnology 2017年第4期8卷 866-874页

作者： Damian Knecht Anna Jankowska-Maosa Kamil Duziński Institute of Animal Breeding Wroclaw University of Environmental and Life SciencesChelmonskiego 38C51–630 WroclawPoland

Background: The aim of the study was to analyze the lifetime and culling reasons for boars used in insemination centers(ai centers).Methods: The data collected from 355 culled boars from 1998 to 2013 included: age at start of semen collection,boar herd life, culling reason, daily gain and lean meat content, and number of ejaculates not meeting sales requirements after dilution. Culling reasons were divided into 7 groups: low semen value(LSV), low or lack of libido(LL), leg problems(LP), infectious diseases(ID), old age(OA), reduced demand for semen from the given boar(RD),and others(OT).Results: The most common culling reasons for boars were LSV(23.7%) and RD(22.5%). It was observed that the lowest daily gains were noted in boars culled due to OA. Boars culled due to OA and RD were maintained in production for the longest time(over 1000 d), for LSV and ID retention was about 700 d, and due to LL below 400 d. The survival probability was over 0.9 until 1.5 yr, and just over 0.2 until 4 yr. The highest relative frequency was observed in the 36 ^(th) and 42 ^(nd) mo of life(over 16%). Hazard risk analysis revealed a more than 10 times higher risk of culling in the case of LL, ID or OT, in comparison to ***: The results can be used as a direct point of reference for the identification of emerging problems in ai boar exploitation and the development of an appropriate culling policy in ai centers.

关键词： ai centers Boars Culling Exploitation Management

A novel ai approach for modeling land surface temperature of Freetown,Sierra Leone,based on land-cover changes

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International Journal of Digital Earth 2022年第1期15卷 1236-1258页

作者： Mosbeh R.Kaloop Mudassir Iqbal Mohamed T.Elnabwy Elhadi K.Mustafa Jong Wan Hu Department of Civil and Environmental Engineering Incheon National UniversityIncheonKorea Incheon Disaster Prevention Research Center Incheon National UniversityIncheonKorea Public Works and Civil Engineering Department Mansoura UniversityMansouraEgypt Department of Civil Engineering University of Engineering and Technology PeshawarPeshwarPakistan National Water Research Center Coastal Research InstituteAlexandriaEgypt Department of Surveying and Geo-Informatics Southwest Jiaotong UniversitySichuanPeople’s Republic of China

Land use/land cover(LULC)indices can be considered while developing land surface temperature(LST)*** relationship between LST and LULC indices must be established to accurately estimate the impacts of LST *** study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in *** neural network(ANN)and gene expression programming(GEP)were employed to develop LST prediction *** of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed *** extracted data of LULC indices,such as normal difference vegetation index(NDVI),normal difference built-up index(NDBI),urban index(UI),and normal difference water index(NDWI),were utilized as attributes to model *** results show that the root-mean-square error(RMSE)of the ANN and GEP models were 0.91oC and 1.08 oC,*** GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation,which can be conveniently used to test new data regarding the thematic *** sensitivity analysis revealed that UI is the most influential parameter followed by NDBI,NDVI,and NDWI towards contributing LST.

关键词： Land surface temperature landsat land use/land cover ai modeling

Towards efficient generative ai and beyond-ai computing:New trends on ISSCC 2024 machine learning accelerators

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Journal of Semiconductors 2024年第4期45卷 12-15页

作者： Bohan Yang Jia Chen Fengbin Tu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina ai Chip Center for Emerging Smart Systems The Hong Kong University of Science and TechnologyHong KongChina School of the Gifted Young University of Science and Technology of ChinaHefei 230026Chin

Compared to the last decade when the convolution neu-ral network(CNN)dominated the research field,machine learn-ing(ML)algorithms have reached a pivotal moment called the generative artificial intelligence(ai)*** the emer-gence of large-scale foundation models[1],such as large multi-modal model(LMM)GPT-4[2]and text-to-image generative model DALL·E[3].

关键词： ISSCC beyond ai