The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Generative artificial intelligence
Open accessAbstract
Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.
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Cites
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Highly accurate protein structure prediction with AlphaFold
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Language Models are Few-Shot Learners
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High-Resolution Image Synthesis with Latent Diffusion Models
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Auto-Encoding Variational Bayes
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Denoising Diffusion Probabilistic Models
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Explanation in artificial intelligence: Insights from the social sciences
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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · cited 2×
“…variance property of GAI, generative models are not immune to output errors, or so-called hallucinations , which manifest themselves in confidently generated results that seem plausible but are unreasonable with respect to the source of information (Ji et al., 2023; Susarla et al., 2023).”
BackgroundMethodDwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“…and automating even traditionally creative tasks, e.g., by generating customized contextual texts or images, facilitating new opportunities for businesses to innovate and differentiate themselves in the competitive economic landscape (Dwivedi et al., 2021; Lund et al., 2023; Pavlik, 2023).”
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Cited by
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024) · Sustainability · doi:10.3390/su16031166 · Cited by 302
Generative AI for Consumer Behavior Prediction: Techniques and Applications
Madanchian, M. (2024) · Sustainability · doi:10.3390/su16229963 · Cited by 60
Liu, J., Sihes, A. J. B., & Lu, Y. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00406-0 · Cited by 52
(2025) · doi:10.1007/s12525-025-00862-z · Cited by 23
AI literacy for teacher educators: a holistic curriculum for capacity-building in higher education
Caspari-Sadeghi, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1745768 · Cited by 18
Metadata
- Authors
- Banh, L., & Strobel, G.
- Venue
- Electronic Markets
- Published
- 2023-12
- Volume
- 33
- Issue
- 1
- ISSN
- 1019-6781 (ISSN-L)
- 1422-8890
- OpenAlex
- W4389359039
- Cited by
- 579
- References
- 54
- FWCI
- 52.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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