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- [40] Smarter peer learning for online knowledge distillationMultimedia Systems, 2022, 28 : 1059 - 1067Yu-e LinXingzhu LiangGan HuXianjin Fang
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
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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
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