Review
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Year 2024, , 47 - 58, 31.05.2024
https://doi.org/10.21541/apjess.1398155

Abstract

References

  • C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, vol. 27, no. 3, 1948, doi: 10.1002/j.1538-7305.1948.tb01338.x.
  • J. Weizenbaum, “ELIZA-A computer program for the study of natural language communication between man and machine,” Commun ACM, vol. 9, no. 1, 1966, doi: 10.1145/365153.365168.
  • M. L. Morbey, “AARON: Portrait of the Young Machine as a Male Artist,” RACAR : Revue d’art canadienne, vol. 20, no. 1–2, 2020, doi: 10.7202/1072764ar.
  • H. Cohen, “The further exploits of AARON, painter,” Stanford Humanities Review, vol. 4, no. 2, 1995.
  • S. Wang, “Artificial Neural Network. In: Interdisciplinary Computing in Java Programming,” The Springer International Series in Engineering and Computer Science, vol. 743, 2003.
  • M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,” Nature Reviews Genetics, vol. 16, no. 6. 2015. doi: 10.1038/nrg3920.
  • I. J. Goodfellow et al., “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014. doi: 10.1007/978-3-658-40442-0_9.
  • A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017.
  • M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3. 2022. doi: 10.1007/s41095-022-0271-y.
  • A. Madani et al., “Deep neural language modeling enables functional protein generation across families,” bioRxiv, 2021.
  • A. Madani et al., “Large language models generate functional protein sequences across diverse families,” Nat Biotechnol, vol. 41, no. 8, 2023, doi: 10.1038/s41587-022-01618-2.
  • A. Madani et al., “Supplementary—Large language models,” Nat Biotechnol, 2023.
  • N. Killoran, L. J. Lee, A. Delong, D. Duvenaud, and B. J. Frey, “Generating and designing DNA with deep generative models,” Dec. 2017, Accessed: Nov. 10, 2023. [Online]. Available: http://arxiv.org/abs/1712.06148
  • R. Yilmaz and F. G. Karaoglan Yilmaz, “The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation,” Computers and Education: Artificial Intelligence, vol. 4, 2023, doi: 10.1016/j.caeai.2023.100147.
  • N. Mostafazadeh et al., “A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories,” Proceedings of NAACL-HLT 2016, San Diego, California, June 12-17, 2016, 2016.
  • A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving Language Understanding by Generative Pre-Training,” OpenAI.com, 2018.
  • R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Dec. 2021, Accessed: Nov. 10, 2023. [Online]. Available: https://arxiv.org/abs/2112.10752
  • W. Xu, H. Sun, C. Deng, and Y. Tan, “Variational autoencoder for semi-supervised text classification,” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017. doi: 10.1609/aaai.v31i1.10966.
  • K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Trans Neural Netw Learn Syst, vol. 28, no. 10, 2017, doi: 10.1109/TNNLS.2016.2582924.
  • A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D, vol. 404, 2020, doi: 10.1016/j.physd.2019.132306.
  • M. Zhang and J. Li, “A commentary of GPT-3 in MIT Technology Review 2021,” Fundamental Research, vol. 1, no. 6. 2021. doi: 10.1016/j.fmre.2021.11.011.
  • X. Liu et al., “GPT understands, too,” AI Open, 2023, doi: 10.1016/j.aiopen.2023.08.012.
  • C. Luo, “Understanding Diffusion Models: A Unified Perspective,” Aug. 2022, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2208.11970
  • W. Luo, “A Comprehensive Survey on Knowledge Distillation of Diffusion Models,” Apr. 2023, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2304.04262
  • L. Lin, Z. Li, R. Li, X. Li, and J. Gao, “Diffusion Models for Time Series Applications: A Survey,” Apr. 2023, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2305.00624
  • H. S. Sætra, “Generative AI: Here to stay, but for good?,” Technol Soc, vol. 75, 2023, doi: 10.1016/j.techsoc.2023.102372.
  • J. Liu, D. Shen, Y. Zhang, B. Dolan, L. Carin, and W. Chen, “What Makes Good In-Context Examples for GPT-3?,” in DeeLIO 2022 - Deep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Proceedings of the Workshop, 2022. doi: 10.18653/v1/2022.deelio-1.10.
  • G. ÇELİK and M. F. TALU, “Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi,” Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 22, no. 1, pp. 181–192, Jan. 2020, doi: 10.25092/baunfbed.679608.
  • N. Köbis and L. D. Mossink, “Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry,” Comput Human Behav, vol. 114, 2021, doi: 10.1016/j.chb.2020.106553.
  • Tao Li and M. Ogihara, “Music Genre Classification with Taxonomy,” in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., IEEE, pp. 197–200. doi: 10.1109/ICASSP.2005.1416274.
  • A. Agostinelli et al., “MusicLM: Generating Music From Text,” Jan. 2023, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/2301.11325
  • D. C.-E. Lin, A. Germanidis, C. Valenzuela, Y. Shi, and N. Martelaro, “Soundify: Matching Sound Effects to Video,” Dec. 2021, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/2112.09726
  • “Runway Research.” Accessed: Nov. 29, 2023. [Online]. Available: https://research.runwayml.com/
  • J. Arús-Pous et al., “Randomized SMILES strings improve the quality of molecular generative models,” J Cheminform, vol. 11, no. 1, 2019, doi: 10.1186/s13321-019-0393-0.
  • T. Taniguchi et al., “A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots,” Neural Networks, vol. 150, 2022, doi: 10.1016/j.neunet.2022.02.026.
  • “CNET found errors in more than half of its AI-written stories - The Verge.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures
  • M. Schreyer, T. Sattarov, B. Reimer, and D. Borth, “Adversarial Learning of Deepfakes in Accounting,” Oct. 2019, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/1910.03810
  • M. A. Selamat and N. A. Windasari, “Chatbot for SMEs: Integrating customer and business owner perspectives,” Technol Soc, vol. 66, 2021, doi: 10.1016/j.techsoc.2021.101685.
  • M. Brockschmidt, M. Allamanis, A. Gaunt, and O. Polozov, “Generative code modeling with graphs,” in 7th International Conference on Learning Representations, ICLR 2019, 2019.
  • C. Ebert and P. Louridas, “Generative AI for Software Practitioners,” IEEE Softw, vol. 40, no. 4, 2023, doi: 10.1109/MS.2023.3265877.
  • W. X. Zhao et al., “A Survey of Large Language Models,” Mar. 2023, Accessed: Nov. 10, 2023. [Online]. Available: https://arxiv.org/abs/2303.18223
  • K. Wach et al., “The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT,” Entrepreneurial Business and Economics Review, vol. 11, no. 2, 2023, doi: 10.15678/eber.2023.110201.
  • B. Jones, E. Luger, and R. Jones, “Generative AI & journalism: A rapid risk-based review.” Jun. 06, 2023. Accessed: Nov. 16, 2023. [Online]. Available: https://www.research.ed.ac.uk/en/publications/generative-ai-amp-journalism-a-rapid-risk-based-review
  • “Regulation of generative AI must protect freedom of expression | OpenGlobalRights.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.openglobalrights.org/regulation-generative-ai-protect-freedom-expression/
  • “Using a brand new account, I put two questions to ChatGPT (a computing system that gives people answers based on a summary of huge amounts… | Instagram.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.instagram.com/p/CydbE5sutDQ/?igshid=MzRlODBiNWFlZA%3D%3D
  • ian bremme, “Asking chatgpt about justice for israel/palestine generates vastly different responses,” https://twitter.com/ianbremmer/status/1713985163191837045.
  • “ChatGPT on Palestine and Israel - What do you make of this?”
  • “CHAT GPT’ye Filistin ve İsrail soruldu.”
  • S. C. F. A. W. Mazen Baroudi, “What happened to ‘All human beings are born free’? Reflections on a ChatGPT ‘experiment,’” 2023.
  • Mona Chalabi, “Asking ChatGPT about israel/Palastine.”
  • “Privacy as a critical enabler of customer trust - Cisco”, Accessed: Jan. 26, 2024. [Online]. Available: https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-privacy-benchmark-study-2024.pdf?CCID=cc000160&DTID=odicdc000016&OID=rptsc032067
  • “Generation Privacy: Young Consumers Leading the Way - Survey - Cisco”, Accessed: Jan. 26, 2024. [Online]. Available: https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-consumer-privacy-report-2023.pdf?CCID=cc000160&DTID=odicdc000016&OID=otrsc031725
  • “AWS re:Invent 2023: How to Use Data-Fueled Generative AI to Drive Productivity | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2023/11/aws-reinvent-2023-how-use-data-fueled-generative-ai-drive-productivity
  • Robert Waitman, “What Are the Privacy Risks of Generative Artificial Intelligence? | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2024/01/what-are-privacy-risks-generative-artificial-intelligence
  • Jacquelyn Bengfort, “Hackers Armed with Generative AI Pose a Greater Challenge to Businesses | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2023/09/hackers-armed-generative-ai-pose-greater-challenge-businesses
  • “How generative AI is boosting the spread of disinformation and propaganda | MIT Technology Review.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.technologyreview.com/2023/10/04/1080801/generative-ai-boosting-disinformation-and-propaganda-freedom-house/
  • “What are the risks and limitations of generative AI? | TechTarget.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.techtarget.com/searchEnterpriseAI/tip/What-are-the-risks-and-limitations-of-generative-AI
  • “PEN America Report on Artificial Intelligence and Free Expression.” Accessed: Jan. 26, 2024. [Online]. Available: https://pen.org/report/speech-in-the-machine/
  • “Generative AI: Understanding the risks and opportunities | Marsh.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.marsh.com/us/services/cyber-risk/insights/generative-ai-understanding-the-risks-and-opportunities.html
  • “After WormGPT, FraudGPT Emerges to Help Scammers Steal Your Data | PCMag.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.pcmag.com/news/after-wormgpt-fraudgpt-emerges-to-help-scammers-steal-your-data

Generative Artificial Intelligence: A Historical and Future Perspective

Year 2024, , 47 - 58, 31.05.2024
https://doi.org/10.21541/apjess.1398155

Abstract

The artificial intelligence field has seen a surge in development, particularly after the advancement of Generative Adversarial Network (GAN) models, resulting in a diverse range of applications. The varied usage of generative models significantly enhances the importance of this domain. The primary focus of this article is the history of generative models, aiming to provide insights into how the field has evolved and to comprehend the complexities of contemporary models. The diversity in application areas and the advantages introduced by these technologies are explored in detail to facilitate a thorough understanding, with the expectation that this knowledge will expedite the emergence of new models and products. The advantages and innovative applications across sectors underscore the critical role these models play in industry. Distinguishing between traditional artificial intelligence and generative artificial intelligence, the article examines the differences. The architecture of generative models, grounded in deep learning and artificial neural networks, is compared briefly with other generative models. Lastly, the article delves into the future of artificial intelligence, addressing associated risks and proposing solutions. It concludes by emphasizing the significance of the article for new research endeavors, serving as a guiding resource for researchers navigating critical discussions in the field of generative models and artificial intelligence.

References

  • C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, vol. 27, no. 3, 1948, doi: 10.1002/j.1538-7305.1948.tb01338.x.
  • J. Weizenbaum, “ELIZA-A computer program for the study of natural language communication between man and machine,” Commun ACM, vol. 9, no. 1, 1966, doi: 10.1145/365153.365168.
  • M. L. Morbey, “AARON: Portrait of the Young Machine as a Male Artist,” RACAR : Revue d’art canadienne, vol. 20, no. 1–2, 2020, doi: 10.7202/1072764ar.
  • H. Cohen, “The further exploits of AARON, painter,” Stanford Humanities Review, vol. 4, no. 2, 1995.
  • S. Wang, “Artificial Neural Network. In: Interdisciplinary Computing in Java Programming,” The Springer International Series in Engineering and Computer Science, vol. 743, 2003.
  • M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,” Nature Reviews Genetics, vol. 16, no. 6. 2015. doi: 10.1038/nrg3920.
  • I. J. Goodfellow et al., “Generative adversarial nets,” in Advances in Neural Information Processing Systems, 2014. doi: 10.1007/978-3-658-40442-0_9.
  • A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017.
  • M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3. 2022. doi: 10.1007/s41095-022-0271-y.
  • A. Madani et al., “Deep neural language modeling enables functional protein generation across families,” bioRxiv, 2021.
  • A. Madani et al., “Large language models generate functional protein sequences across diverse families,” Nat Biotechnol, vol. 41, no. 8, 2023, doi: 10.1038/s41587-022-01618-2.
  • A. Madani et al., “Supplementary—Large language models,” Nat Biotechnol, 2023.
  • N. Killoran, L. J. Lee, A. Delong, D. Duvenaud, and B. J. Frey, “Generating and designing DNA with deep generative models,” Dec. 2017, Accessed: Nov. 10, 2023. [Online]. Available: http://arxiv.org/abs/1712.06148
  • R. Yilmaz and F. G. Karaoglan Yilmaz, “The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation,” Computers and Education: Artificial Intelligence, vol. 4, 2023, doi: 10.1016/j.caeai.2023.100147.
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  • A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving Language Understanding by Generative Pre-Training,” OpenAI.com, 2018.
  • R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Dec. 2021, Accessed: Nov. 10, 2023. [Online]. Available: https://arxiv.org/abs/2112.10752
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  • K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Trans Neural Netw Learn Syst, vol. 28, no. 10, 2017, doi: 10.1109/TNNLS.2016.2582924.
  • A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D, vol. 404, 2020, doi: 10.1016/j.physd.2019.132306.
  • M. Zhang and J. Li, “A commentary of GPT-3 in MIT Technology Review 2021,” Fundamental Research, vol. 1, no. 6. 2021. doi: 10.1016/j.fmre.2021.11.011.
  • X. Liu et al., “GPT understands, too,” AI Open, 2023, doi: 10.1016/j.aiopen.2023.08.012.
  • C. Luo, “Understanding Diffusion Models: A Unified Perspective,” Aug. 2022, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2208.11970
  • W. Luo, “A Comprehensive Survey on Knowledge Distillation of Diffusion Models,” Apr. 2023, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2304.04262
  • L. Lin, Z. Li, R. Li, X. Li, and J. Gao, “Diffusion Models for Time Series Applications: A Survey,” Apr. 2023, Accessed: Sep. 12, 2023. [Online]. Available: http://arxiv.org/abs/2305.00624
  • H. S. Sætra, “Generative AI: Here to stay, but for good?,” Technol Soc, vol. 75, 2023, doi: 10.1016/j.techsoc.2023.102372.
  • J. Liu, D. Shen, Y. Zhang, B. Dolan, L. Carin, and W. Chen, “What Makes Good In-Context Examples for GPT-3?,” in DeeLIO 2022 - Deep Learning Inside Out: 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Proceedings of the Workshop, 2022. doi: 10.18653/v1/2022.deelio-1.10.
  • G. ÇELİK and M. F. TALU, “Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi,” Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 22, no. 1, pp. 181–192, Jan. 2020, doi: 10.25092/baunfbed.679608.
  • N. Köbis and L. D. Mossink, “Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry,” Comput Human Behav, vol. 114, 2021, doi: 10.1016/j.chb.2020.106553.
  • Tao Li and M. Ogihara, “Music Genre Classification with Taxonomy,” in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., IEEE, pp. 197–200. doi: 10.1109/ICASSP.2005.1416274.
  • A. Agostinelli et al., “MusicLM: Generating Music From Text,” Jan. 2023, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/2301.11325
  • D. C.-E. Lin, A. Germanidis, C. Valenzuela, Y. Shi, and N. Martelaro, “Soundify: Matching Sound Effects to Video,” Dec. 2021, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/2112.09726
  • “Runway Research.” Accessed: Nov. 29, 2023. [Online]. Available: https://research.runwayml.com/
  • J. Arús-Pous et al., “Randomized SMILES strings improve the quality of molecular generative models,” J Cheminform, vol. 11, no. 1, 2019, doi: 10.1186/s13321-019-0393-0.
  • T. Taniguchi et al., “A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots,” Neural Networks, vol. 150, 2022, doi: 10.1016/j.neunet.2022.02.026.
  • “CNET found errors in more than half of its AI-written stories - The Verge.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures
  • M. Schreyer, T. Sattarov, B. Reimer, and D. Borth, “Adversarial Learning of Deepfakes in Accounting,” Oct. 2019, Accessed: Nov. 16, 2023. [Online]. Available: http://arxiv.org/abs/1910.03810
  • M. A. Selamat and N. A. Windasari, “Chatbot for SMEs: Integrating customer and business owner perspectives,” Technol Soc, vol. 66, 2021, doi: 10.1016/j.techsoc.2021.101685.
  • M. Brockschmidt, M. Allamanis, A. Gaunt, and O. Polozov, “Generative code modeling with graphs,” in 7th International Conference on Learning Representations, ICLR 2019, 2019.
  • C. Ebert and P. Louridas, “Generative AI for Software Practitioners,” IEEE Softw, vol. 40, no. 4, 2023, doi: 10.1109/MS.2023.3265877.
  • W. X. Zhao et al., “A Survey of Large Language Models,” Mar. 2023, Accessed: Nov. 10, 2023. [Online]. Available: https://arxiv.org/abs/2303.18223
  • K. Wach et al., “The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT,” Entrepreneurial Business and Economics Review, vol. 11, no. 2, 2023, doi: 10.15678/eber.2023.110201.
  • B. Jones, E. Luger, and R. Jones, “Generative AI & journalism: A rapid risk-based review.” Jun. 06, 2023. Accessed: Nov. 16, 2023. [Online]. Available: https://www.research.ed.ac.uk/en/publications/generative-ai-amp-journalism-a-rapid-risk-based-review
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  • “Using a brand new account, I put two questions to ChatGPT (a computing system that gives people answers based on a summary of huge amounts… | Instagram.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.instagram.com/p/CydbE5sutDQ/?igshid=MzRlODBiNWFlZA%3D%3D
  • ian bremme, “Asking chatgpt about justice for israel/palestine generates vastly different responses,” https://twitter.com/ianbremmer/status/1713985163191837045.
  • “ChatGPT on Palestine and Israel - What do you make of this?”
  • “CHAT GPT’ye Filistin ve İsrail soruldu.”
  • S. C. F. A. W. Mazen Baroudi, “What happened to ‘All human beings are born free’? Reflections on a ChatGPT ‘experiment,’” 2023.
  • Mona Chalabi, “Asking ChatGPT about israel/Palastine.”
  • “Privacy as a critical enabler of customer trust - Cisco”, Accessed: Jan. 26, 2024. [Online]. Available: https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-privacy-benchmark-study-2024.pdf?CCID=cc000160&DTID=odicdc000016&OID=rptsc032067
  • “Generation Privacy: Young Consumers Leading the Way - Survey - Cisco”, Accessed: Jan. 26, 2024. [Online]. Available: https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-consumer-privacy-report-2023.pdf?CCID=cc000160&DTID=odicdc000016&OID=otrsc031725
  • “AWS re:Invent 2023: How to Use Data-Fueled Generative AI to Drive Productivity | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2023/11/aws-reinvent-2023-how-use-data-fueled-generative-ai-drive-productivity
  • Robert Waitman, “What Are the Privacy Risks of Generative Artificial Intelligence? | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2024/01/what-are-privacy-risks-generative-artificial-intelligence
  • Jacquelyn Bengfort, “Hackers Armed with Generative AI Pose a Greater Challenge to Businesses | BizTech Magazine.” Accessed: Jan. 26, 2024. [Online]. Available: https://biztechmagazine.com/article/2023/09/hackers-armed-generative-ai-pose-greater-challenge-businesses
  • “How generative AI is boosting the spread of disinformation and propaganda | MIT Technology Review.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.technologyreview.com/2023/10/04/1080801/generative-ai-boosting-disinformation-and-propaganda-freedom-house/
  • “What are the risks and limitations of generative AI? | TechTarget.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.techtarget.com/searchEnterpriseAI/tip/What-are-the-risks-and-limitations-of-generative-AI
  • “PEN America Report on Artificial Intelligence and Free Expression.” Accessed: Jan. 26, 2024. [Online]. Available: https://pen.org/report/speech-in-the-machine/
  • “Generative AI: Understanding the risks and opportunities | Marsh.” Accessed: Jan. 26, 2024. [Online]. Available: https://www.marsh.com/us/services/cyber-risk/insights/generative-ai-understanding-the-risks-and-opportunities.html
  • “After WormGPT, FraudGPT Emerges to Help Scammers Steal Your Data | PCMag.” Accessed: Nov. 29, 2023. [Online]. Available: https://www.pcmag.com/news/after-wormgpt-fraudgpt-emerges-to-help-scammers-steal-your-data
There are 60 citations in total.

Details

Primary Language English
Subjects Adversarial Machine Learning, Machine Vision
Journal Section Reviews
Authors

Hatice Kübra Kılınç 0000-0003-3040-6776

Ö. Fatih Keçecioğlu 0000-0001-7004-4947

Early Pub Date May 28, 2024
Publication Date May 31, 2024
Submission Date November 30, 2023
Acceptance Date February 27, 2024
Published in Issue Year 2024

Cite

IEEE H. K. Kılınç and Ö. F. Keçecioğlu, “Generative Artificial Intelligence: A Historical and Future Perspective”, APJESS, vol. 12, no. 2, pp. 47–58, 2024, doi: 10.21541/apjess.1398155.

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