Artificial intelligence: a survey on evolution, models, applications and future trends
Abstract
Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, Internet of Things, cloud computing, and the blockchain, in the new generation of big data and Industry 4.0. In this paper, we construct an extensive survey over the period 1961–2018 of AI and deep learning. The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI, from underlying mechanisms to practical applications, from fundamental algorithms to industrial achievements, from current status to future trends. Although there exist many issues toward AI, it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.
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The advent of machine learning and autonomous machines has resulted in a breach of the trust that existed, because machines can now operate and act independently.
“The advent of Machine Learning (ML) and the concept of making machines think and perform tasks autonomously, however, has resulted in a breach of the trust that existed, as machines can now operate and act independently [7].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [7]
A new AI with partiality in its coding can start by being partial towards different types of human beings and with consequent evolution might discriminate against humans as a whole.
“A new AI with partiality in its coding can start off by being partial towards different types of human beings which is terrible in itself, but with consequent evolution might be discriminating its services against humans as a whole [7], [12].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [7]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012 · see citing works
Self-citationHierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473 · see citing works
Brougham, D., & Haar, J. (2018) · Journal of Management & Organization · doi:10.1017/jmo.2016.55 · Cited by 1,117 · see citing works
Futuristic biosensors for cardiac health care: an artificial intelligence approach
Vashistha, R., Dangi, A. K., Kumar, A., Chhabra, D., & Shukla, P. (2018) · 3 Biotech · doi:10.1007/s13205-018-1368-y · Cited by 113 · see citing works
Piasecki, A., Jurasz, J., & Adamowski, J. F. (2018) · Acta Geophysica · doi:10.1007/s11600-018-0183-5 · Cited by 30 · see citing works
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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203 · see citing works
Revolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975 · see citing works
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919 · see citing works
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352 · see citing works
“A new AI with partiality in its coding can start off by being partial towards different types of human beings which is terrible in itself, but with consequent evolution might be discriminating its services against humans as a whole [7], [12].”
BackgroundExplore success factors that impact artificial intelligence adoption on telecom industry in China
Chen, H., Li, L., & Chen, Y. (2021) · Journal of Management Analytics · doi:10.1080/23270012.2020.1852895 · Cited by 300 · see citing works
Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany
von Garrel, J., & Mayer, J. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02304-7 · Cited by 236 · see citing works
Emerging Enabling Technologies for Industry 4.0 and Beyond
Sigov, A., Ratkin, L., Ivanov, L. A., & Xu, L. D. (2024) · Information Systems Frontiers · doi:10.1007/s10796-021-10213-w · Cited by 234 · see citing works
Interactive AI With Retrieval-Augmented Generation for Next Generation Networking
Zhang, R., Du, H., Liu, Y., Niyato, D., Kang, J., Sun, S., Shen, X., & Poor, H. V. (2024) · IEEE Network · doi:10.1109/mnet.2024.3401159 · Cited by 147 · see citing works
The Role of Artificial Intelligence in Nutrition Research: A Scoping Review
Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., & Oldewage-Theron, W. (2024) · Nutrients · doi:10.3390/nu16132066 · Cited by 114 · see citing works
Industry’s 4.0 transformation process: How to start, where to aim, what to be aware of
Calabrese, A., Dora, M., Levialdi Ghiron, N., & Tiburzi, L. (2022) · Production Planning & Control · doi:10.1080/09537287.2020.1830315 · Cited by 109 · see citing works
Artificial Intelligence in Personalised Learning: A Bibliometric Analysis
Li, K. C., & Wong, B. T.-M. (2023) · Interactive Technology and Smart Education · doi:10.1108/itse-01-2023-0007 · Cited by 88 · see citing works
Panchanadikar, R., & Freeman, G. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3677082 · Cited by 22 · see citing works
Panchanadikar, R., Freeman, G., Li, L., Schulenberg, K., & Hu, Y. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613905.3650845 · Cited by 17 · see citing works
Erol, M., Canbeldek Erol, M., Erol, A., & Gök Çolak, F. (2025) · European Journal of Education · doi:10.1111/ejed.70332 · Cited by 10 · see citing works
Castro-Lopez, A., El Abed, M., Cervero, A., & Álvarez-Blanco, L. (2026) · European Journal of Higher Education · doi:10.1080/21568235.2026.2620686 · Cited by 3 · see citing works
- Authors
- Lu, Y.
- Affiliations
- University of Kentucky (US)
- Corresponding author
- Lu, Yang
- ORCID
- Lu, Yang
- Venue
- Journal of Management Analytics
- Publisher
- Informa UK Limited
- Published
- 2019-01-02
- Volume
- 6
- Issue
- 1
- Pages
- 1-29
- ISSN
- 2327-0012 (ISSN-L)
- 2327-0039
- OpenAlex
- W2912185124
- Cited by
- 641
- References
- 13
- Citations by year
- 2026: 53 · 2025: 129 · 2024: 168 · 2023: 106 · 2022: 71 · 2021: 68 · 2020: 33 · 2019: 13
- FWCI
- 21.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Management Analytics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, deep learning, history of artificial intelligence, artificial intelligence applications, artificial intelligence models, Industry 4.0, big data, Internet of Things, cloud computing, blockchain, GPU computing
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