AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning

被引:0
|
作者
Miller, Tymoteusz [1 ,2 ]
Durlik, Irmina [3 ]
Lobodzińska, Adrianna [4 ]
Dorobczyński, Lech [5 ]
Jasionowski, Robert [6 ]
机构
[1] Institute of Marine and Environmental Sciences, University of Szczecin, Szczecin,71-314, Poland
[2] Faculty of Data Science and Information, INTI International University, Negeri Sembilan, Nilai,71800, Malaysia
[3] Faculty of Navigation, Maritime University of Szczecin, Szczecin,71-650, Poland
[4] Institute of Biology, University of Szczecin, Szczecin,71-316, Poland
[5] Faculty of Mechatronics and Electrical Engineering, Maritime University of Szczecin, Szczecin,71-650, Poland
[6] Faculty of Marine Engineering, Maritime University of Szczecin, Szczecin,71-650, Poland
来源
Applied Sciences (Switzerland) | 2024年 / 14卷 / 24期
关键词
This article delves into the critical integration of domain knowledge into AI/ML systems across various industries; highlighting its importance in developing ethically responsible; effective; and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors; we explore the challenges; strategies; and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends; predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation; ensuring ethical practices; and aligning technological advancements with human values and real-world complexities. © 2024 by the authors;
D O I
10.3390/app142411612
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
  • [41] Smarter peer learning for online knowledge distillation
    Lin, Yu-e
    Liang, Xingzhu
    Hu, Gan
    Fang, Xianjin
    MULTIMEDIA SYSTEMS, 2022, 28 (03) : 1059 - 1067
  • [42] Analytic continuation via domain knowledge free machine learning
    Yoon, Hongkee
    Sim, Jae-Hoon
    Han, Myung Joon
    PHYSICAL REVIEW B, 2018, 98 (24)
  • [43] Fusing domain knowledge with machine learning: A public sector perspective
    Sundberg, Leif
    Holmstrom, Jonny
    JOURNAL OF STRATEGIC INFORMATION SYSTEMS, 2024, 33 (03):
  • [44] Incorporating domain knowledge in machine learning for soccer outcome prediction
    Daniel Berrar
    Philippe Lopes
    Werner Dubitzky
    Machine Learning, 2019, 108 : 97 - 126
  • [45] Embedding domain knowledge for machine learning of complex material systems
    Childs, Christopher M.
    Washburn, Newell R.
    MRS COMMUNICATIONS, 2019, 9 (03) : 806 - 820
  • [46] Incorporating domain knowledge in machine learning for soccer outcome prediction
    Berrar, Daniel
    Lopes, Philippe
    Dubitzky, Werner
    MACHINE LEARNING, 2019, 108 (01) : 97 - 126
  • [47] Embedding domain knowledge for machine learning of complex material systems
    Christopher M. Childs
    Newell R. Washburn
    MRS Communications, 2019, 9 : 806 - 820
  • [48] Combining domain knowledge and machine learning for robust fall detection
    Mirchevska, Violeta
    Lustrek, Mitja
    Gams, Matjaz
    EXPERT SYSTEMS, 2014, 31 (02) : 163 - 175
  • [49] Trajectories of legal work in the context of machine learning AI: conceptualising mediated evolution
    Faulconbridge, James
    INTERNATIONAL JOURNAL OF THE LEGAL PROFESSION, 2025, 32 (01) : 97 - 120
  • [50] Harnessing Approximate Computing for Machine Learning
    Shakibhamedan, Salar
    Aminifar, Amin
    Vassallo, Luke
    TaheriNejad, Nima
    2024 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI, ISVLSI, 2024, : 585 - 591