Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review

Beyazıt Bestami Yüksel; Ayşe Yılmazer Metin

doi:10.21541/apjess.1705042

Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review

Abstract

This paper presents a comprehensive synthesis of major breakthroughs in artificial intelligence (AI) over the past fifteen years, integrating historical, theoretical, and technological perspectives. It identifies key inflection points in AI’s evolution by tracing the convergence of computational resources, data access, and algorithmic innovation. The analysis highlights how researchers enabled GPU-based model training, triggered a data-centric shift with ImageNet, simplified architectures through the Transformer, and expanded modeling capabilities with the GPT series. Rather than treating these advances as isolated milestones, the paper frames them as indicators of deeper paradigm shifts. By applying concepts from statistical learning theory such as sample complexity and data efficiency, the paper explains how researchers translated breakthroughs into scalable solutions and why the field must now embrace data-centric approaches. In response to rising privacy concerns and tightening regulations, the paper evaluates emerging solutions like federated learning, privacy-enhancing technologies (PETs), and the data site paradigm, which reframe data access and security. In cases where real-world data remains inaccessible, the paper also assesses the utility and constraints of mock and synthetic data generation. By aligning technical insights with evolving data infrastructure, this study offers strategic guidance for future AI research and policy development.

Keywords

References

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Narayanan, D., Shoeybi, M., Casper, J., LeGresley, P., Patwary, M., Korthikanti, V., Vainbrand, D., Kashinkunti, P., Bernauer, J., Catanzaro, B., Phanishayee, A., Zaharia, M., & Kazhamiaka, F. (2021). Efficient Large-Scale Language Model Training on GPU Clusters. ArXiv, abs/2104.04473.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., ... & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144.Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.

Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2),1–17. https://doi.org/10.1177/2053951717743530
Kairouz, P., McMahan, H. B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083
Liu, Y., Yu, T., Vaidya, J., et al. (2021). A survey of privacy-preserving federated learning: Techniques, challenges, and future directions. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1145/3460427
Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks, 10(5), 988-999.
Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine, 29(6), 141-142.
Noah Golowich, Alexander Rakhlin, Ohad Shamir, Size-independent sample complexity of neural networks, Information and Inference: A Journal of the IMA, Volume 9, Issue 2, June 2020, Pages 473–504, https://doi.org/10.1093/imaiai/iaz007.
Wager, S., Wang, S., & Liang, P. S. (2013). Dropout training as adaptive regularization. Advances in neural information processing systems, 26.
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2017). Understanding deep learning requires rethinking generalization. International Conference on Learning Representations (ICLR).
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.
Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H.-T. (2012). Learning from Data: A Short Course. AMLBook
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
C. I. R. Koksal, N. M. Cicek, A. Y. Metin and B. Ors, "Optimizing Data Availability and Utilization in Deep Learning Accelerator SoCs," 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS), Istanbul, Turkiye, 2023, pp. 1-4, doi: 10.1109/ICECS58634.2023.10382797.
Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., ... & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Wainwright, M. J., & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1–2), 1–305.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Raina, R., Madhavan, A., & Ng, A. Y. (2009, June). Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th annual international conference on machine learning (pp. 873-880).
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). https://doi.org/10.1109/CVPR.2009.5206848
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NeurIPS) 25, 1097–1105. https://papers.nips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781. https://arxiv.org/abs/1301.3781
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489. https://doi.org/10.1038/nature16961
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS) 30, 5998–6008. https://arxiv.org/abs/1706.03762
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P. & Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (NeurIPS) 33, 1877–1901. https://arxiv.org/abs/2005.14165
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., ... & Martinis, J. M. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510. https://doi.org/10.1038/s41586-019-1666-5
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977. https://doi.org/10.48550/arXiv.1912.04977
McSharry PE, Clifford GD, Tarassenko L, Smith L. A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3): 289-294; March 2003.
Adler, S., Hitzig, Z., Jain, S., Brewer, C., Chang, W., DiResta, R., ... & Zick, T. (2024). Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online. arXiv preprint arXiv:2408.07892.
OpenMined Foundation. (2024, November 28). Datasite server documentation. OpenMined. https://docs.openmined.org/en/latest/components/datasite-server.html
OpenMined. (2025, February). PySyft (Version 0.9.5) https://github.com/OpenMined/PySyft
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).
Silver, D., Schrittwieser, J., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359.
Zhang, C., et al. (2020). AI Development in China: Trends and Opportunities. Nature Machine Intelligence, 2(2), 59–62.
AI4EU Project. (2022). European Commission. https://www.ai4eu.eu
Narayanan, A., et al. (2019). India Stack: Public digital infrastructure for financial inclusion. Digital Public Goods Alliance Report.
Kaplan, J., et al. (2020). Scaling Laws for Neural Language Models. arXiv:2001.08361.
El Emam K, Mosquera L, Bass J Evaluating Identity Disclosure Risk in Fully Synthetic Health Data: Model Development and Validation J Med Internet Res 2020;22(11):e23139 DOI: 10.2196/23139
Goncalves, A., Ray, P., Soper, B. et al. Generation and evaluation of synthetic patient data. BMC Med Res Methodol 20, 108 (2020). https://doi.org/10.1186/s12874-020-00977-1
Kaabachi, B., Despraz, J., Meurers, T. et al. A scoping review of privacy and utility metrics in medical synthetic data. npj Digit. Med. 8, 60 (2025). https://doi.org/10.1038/s41746-024-01359-3
Mestari, S., Lenzini, G., & Demirci, H. (2023). Preserving data privacy in machine learning systems. Comput. Secur., 137, 103605. https://doi.org/10.1016/j.cose.2023.103605.
Kakarala, M., & Rongali, S. (2025). Data Privacy and Security in AI. World Journal of Advanced Research and Reviews.
Lami, B., Hussein, S., Rajamanickam, R., & Emmanuel, G. (2024). The role of artificial intelligence (AI) in shaping data privacy. International Journal of Law and Management. https://doi.org/10.1108/ijlma-07-2024-0242
Gupta, A., Amarnani, M., Soanki, S., & Kishore, J. (2025, February). AI and Data Privacy in Business. In 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) (pp. 109-114). IEEE.
Ijaiya, H. (2024). Harnessing AI for data privacy: Examining risks, opportunities and strategic future directions. Int. J. Sci. Res. Arch, 13, 2878-2892.
Zhu, T., Ye, D., Wang, W., Zhou, W., & Yu, P. (2020). More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence. IEEE Transactions on Knowledge and Data Engineering, 34, 2824-2843. https://doi.org/10.1109/TKDE.2020.3014246.
Yu, S., Carroll, F., & Bentley, B. (2024). Insights Into Privacy Protection Research in AI. IEEE Access, 12, 41704-41726. https://doi.org/10.1109/ACCESS.2024.3378126.
Yin, X., Zhu, Y., & Hu, J. (2021). A Comprehensive Survey of Privacy-preserving Federated Learning. ACM Computing Surveys (CSUR), 54, 1 - 36. https://doi.org/10.1145/3460427
Li, Q., Wen, Z., Wu, Z., & He, B. (2019). A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. IEEE Transactions on Knowledge and Data Engineering, 35, 3347-3366. https://doi.org/10.1109/TKDE.2021.3124599
Yin, L., Feng, J., Xun, H., Sun, Z., & Cheng, X. (2021). A Privacy-Preserving Federated Learning for Multiparty Data Sharing in Social IoTs. IEEE Transactions on Network Science and Engineering, 8, 2706-2718. https://doi.org/10.1109/TNSE.2021.3074185
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowl. Based Syst., 216, 106775. https://doi.org/10.1016/j.knosys.2021.106775
Mothukuri, V., Parizi, R., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021). A survey on security and privacy of federated learning. Future Gener. Comput. Syst., 115, 619-640. https://doi.org/10.1016/j.future.2020.10.007
Bonawitz, K., Kairouz, P., McMahan, H., & Ramage, D. (2021). Federated Learning and Privacy. Queue, 19, 87 - 114. https://doi.org/10.1145/3494834.3500240
Li, J., Ding, M., Yang, H., Shu, F., Quek, T., & Poor, H. (2019). On Safeguarding Privacy and Security in the Framework of Federated Learning. IEEE Network, 34, 242-248. https://doi.org/10.1109/MNET.001.1900506
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020). Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT. IEEE Transactions on Industrial Informatics, 16, 4177-4186. https://doi.org/10.1109/TII.2019.2942190
Gosselin, R., Vieu, L., Loukil, F., & Benoît, A. (2022). Privacy and Security in Federated Learning: A Survey. Applied Sciences.
Razi, Q., Piyush, R., Chakrabarti, A., Singh, A., Hassija, V., & Chalapathi, G. (2025). Enhancing Data Privacy: A Comprehensive Survey of Privacy-Enabling Technologies. IEEE Access, 13, 40354-40385. https://doi.org/10.1109/ACCESS.2025.3546618
Donald, O., Ajala, O., Arinze, C., Ofodile, O., Okoye, C., & Daraojimba, O. (2024). Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance. World Journal of Advanced Engineering Technology and Sciences.
Cho, H., Froelicher, D., Dokmai, N., Nandi, A., Sadhuka, S., Hong, M., & Berger, B. (2024). Privacy-Enhancing Technologies in Biomedical Data Science. Annual review of biomedical data science, 7 1, 317-343. https://doi.org/10.1146/annurev-biodatasci-120423-120107
Soykan, E., Karaçay, L., Karakoç, F., & Tomur, E. (2022). A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning. IEEE Access, 10, 97495-97519. https://doi.org/10.1109/ACCESS.2022.3204037
Jordan, S., Fontaine, C., & Hendricks-Sturrup, R. (2022). Selecting Privacy-Enhancing Technologies for Managing Health Data Use. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.814163
Chumburidze, Y., & Kakorina, O. (2023). Studying of Privacy-Enhancing Technologies. NBI Technologies. https://doi.org/10.15688/nbit.jvolsu.2023.3.5
Berger, B., & Cho, H. (2019). Emerging technologies towards enhancing privacy in genomic data sharing. Genome Biology, 20. https://doi.org/10.1186/s13059-019-1741-0
Seamons, K. (2022). Chapter 8 Privacy-Enhancing Technologies.
Klymenko, A., Meisenbacher, S., Messmer, F., & Matthes, F. (2023). Privacy-Enhancing Technologies in the Process of Data Privacy Compliance: An Educational Perspective. , 62-69.
Goldberg, I., Wagner, D., & Brewer, E. (1997). Privacy-enhancing technologies for the Internet. Proceedings IEEE COMPCON 97. Digest of Papers, 103-109. https://doi.org/10.1109/CMPCON.1997.584680
Melzi, P., Rathgeb, C., Tolosana, R., Vera-Rodríguez, R., & Busch, C. (2022). An Overview of Privacy-Enhancing Technologies in Biometric Recognition. ACM Computing Surveys, 56, 1 - 28. https://doi.org/10.1145/3664596
Lovrencic, R., & Škvorc, D. (2023). Multi-cloud applications: data and code fragmentation for improved security. International Journal of Information Security, 22, 713-721. https://doi.org/10.1007/s10207-022-00658-8
Zhang, J., Chen, B., Zhao, Y., Cheng, X., & Hu, F. (2018). Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues. IEEE Access, 6, 18209-18237. https://doi.org/10.1109/ACCESS.2018.2820162
Sun, Z., Strang, K., & Pambel, F. (2020). Privacy and security in the big data paradigm. Journal of Computer Information Systems, 60, 146 - 155. https://doi.org/10.1080/08874417.2017.1418631
Xu, Q., Cheng, Z., Cheng, Y., & Chen, G. (2018). Cross-Domain Data Access System for Distributed Sites in HEP. , 154-164. https://doi.org/10.1007/978-3-030-28061-1_17
Raptis, T., Passarella, A., & Conti, M. (2020). Distributed Data Access in Industrial Edge Networks. IEEE Journal on Selected Areas in Communications, 38, 915-927.
Kaplan, N., Baker, K., & Karasti, H. (2021). Long live the data! Embedded data management at a long‐term ecological research site. Ecosphere, 12.
Malerba, D., & Pasquadibisceglie, V. (2024). Data-Centric AI. J. Intell. Inf. Syst., 62, 1493-1502. https://doi.org/10.1007/s10844-024-00901-9
Li, X., Cao, C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., & Chen, L. (2020). A Survey of Data-Driven and Knowledge-Aware eXplainable AI. IEEE Transactions on Knowledge and Data Engineering, 34, 29-49. https://doi.org/10.1109/tkde.2020.2983930
Kumar, Y., Marchena, J., Awlla, A., Li, J., & Abdalla, H. (2024). The AI-Powered Evolution of Big Data. Applied Sciences.
Myakala, P., Jonnalagadda, A., & Naayini, P. (2025). Revolutionizing Big Data with AI-Driven Hybrid Soft Computing Techniques. Machine Learning and Applications: An International Journal. https://doi.org/10.5121/mlaij.2025.12101
Nguyen, D., Cheng, P., Ding, M., López-Pérez, D., Pathirana, P., Li, J., Seneviratne, A., Li, Y., & Poor, H. (2020). Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective. IEEE Communications Surveys & Tutorials, 23, 553-595. https://doi.org/10.1109/COMST.2020.3024783
Wang, D., Weisz, J., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019). Human-AI Collaboration in Data Science. Proceedings of the ACM on Human-Computer Interaction, 3, 1 - 24. https://doi.org/10.1145/3359313
Tripathi, M., Nath, A., Singh, T., Ethayathulla, A., & Kaur, P. (2021). Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Molecular Diversity, 25, 1439 - 1460. https://doi.org/10.1007/s11030-021-10256-w
Tripathi, M., Nath, A., Singh, T., Ethayathulla, A., & Kaur, P. (2021). Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Molecular Diversity, 25, 1439 - 1460. https://doi.org/10.1007/s11030-021-10256-w
Yuksel, B. B., & Metin, A. Y. (2026). Federated learning with homomorphic encryption for secure real time ECG anomaly detection: A multi institutional privacy preserving framework. Biomedical Signal Processing and Control, 116, 109557.

Details

Primary Language

English

Subjects

Machine Learning Algorithms

Journal Section

Review

Authors

Beyazıt Bestami Yüksel ^*
0000-0001-5060-6236
Türkiye

Ayşe Yılmazer Metin
0000-0003-4502-7365
Türkiye

Publication Date

January 31, 2026

Submission Date

May 23, 2025

Acceptance Date

August 31, 2025

Published in Issue

Year 2026 Volume: 14 Number: 1

DOI

https://doi.org/10.21541/apjess.1705042

IZ

https://izlik.org/JA24PY24MS

Cite

RIS / Bibtex

APA

Yüksel, B. B., & Yılmazer Metin, A. (2026). Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review. Academic Platform Journal of Engineering and Smart Systems, 14(1), 1-16. https://doi.org/10.21541/apjess.1705042

AMA

1.Yüksel BB, Yılmazer Metin A. Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review. APJESS. 2026;14(1):1-16. doi:10.21541/apjess.1705042

Chicago

Yüksel, Beyazıt Bestami, and Ayşe Yılmazer Metin. 2026. “Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review”. Academic Platform Journal of Engineering and Smart Systems 14 (1): 1-16. https://doi.org/10.21541/apjess.1705042.

EndNote

Yüksel BB, Yılmazer Metin A (January 1, 2026) Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review. Academic Platform Journal of Engineering and Smart Systems 14 1 1–16.

IEEE

[1]B. B. Yüksel and A. Yılmazer Metin, “Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review”, APJESS, vol. 14, no. 1, pp. 1–16, Jan. 2026, doi: 10.21541/apjess.1705042.

ISNAD

Yüksel, Beyazıt Bestami - Yılmazer Metin, Ayşe. “Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review”. Academic Platform Journal of Engineering and Smart Systems 14/1 (January 1, 2026): 1-16. https://doi.org/10.21541/apjess.1705042.

JAMA

1.Yüksel BB, Yılmazer Metin A. Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review. APJESS. 2026;14:1–16.

MLA

Yüksel, Beyazıt Bestami, and Ayşe Yılmazer Metin. “Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review”. Academic Platform Journal of Engineering and Smart Systems, vol. 14, no. 1, Jan. 2026, pp. 1-16, doi:10.21541/apjess.1705042.

Vancouver

1.Beyazıt Bestami Yüksel, Ayşe Yılmazer Metin. Artificial Intelligence Breakthroughs and Data Futures: A Retrospective and Prospective Review. APJESS. 2026 Jan. 1;14(1):1-16. doi:10.21541/apjess.1705042