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Artificial Intelligence: A Comprehensive Review of Technological Evolution, Comparative Analysis, Applications, Challenges, and Future Research Directions

by Surati Sandipkumar B., Sumathi Shenoy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 129
Year of Publication: 2026
Authors: Surati Sandipkumar B., Sumathi Shenoy
10.5120/ijca60062e1fe4f1

Surati Sandipkumar B., Sumathi Shenoy . Artificial Intelligence: A Comprehensive Review of Technological Evolution, Comparative Analysis, Applications, Challenges, and Future Research Directions. International Journal of Computer Applications. 187, 129 ( Jul 2026), 16-23. DOI=10.5120/ijca60062e1fe4f1

@article{ 10.5120/ijca60062e1fe4f1,
author = { Surati Sandipkumar B., Sumathi Shenoy },
title = { Artificial Intelligence: A Comprehensive Review of Technological Evolution, Comparative Analysis, Applications, Challenges, and Future Research Directions },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 129 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 16-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number129/artificial-intelligence-a-comprehensive-review-of-technological-evolution-comparative-analysis-applications-challenges-and-future-research-directions/ },
doi = { 10.5120/ijca60062e1fe4f1 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-29T00:34:38.616868+05:30
%A Surati Sandipkumar B.
%A Sumathi Shenoy
%T Artificial Intelligence: A Comprehensive Review of Technological Evolution, Comparative Analysis, Applications, Challenges, and Future Research Directions
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 129
%P 16-23
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Intelligence (AI) has evolved from symbolic reasoning systems to advanced generative models and autonomous agents. This paper reviews the historical development of AI, including foundational contributions in machine intelligence, machine learning, deep learning, transformer architectures, and large language models. The study examines major AI paradigms, highlights key application areas, and discusses emerging technologies such as multimodal AI and autonomous agents. Furthermore, the paper analyzes challenges including explainability, bias, privacy, hallucinations, and computational requirements. Finally, the review identifies the future research directions focused on trustworthy, responsible, and human-centered AI systems.

References
  1. A. M. Turing, “Computing Machinery and Intelligence,” Mind, vol. 59, no. 236, pp. 433–460, 1950.
  2. J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence,” Dartmouth College, Hanover, NH, USA, 1955.
  3. T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
  4. Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021.
  6. A. Vaswani et al., “Attention Is All You Need,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 5998–6008, 2017.
  7. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, pp. 4171–4186, 2019.
  8. D. Gunning and D. Aha, “DARPA's Explainable Artificial Intelligence (XAI) Program,” AI Magazine, vol. 40, no. 2, pp. 44–58, 2019.
  9. T. Brown et al., “Language Models are Few-Shot Learners,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 1877–1901, 2020.
  10. A. Chowdhery et al., “PaLM: Scaling Language Modeling with Pathways,” 2022. Available: arXiv:2204.02311.
  11. H. Touvron et al., “LLaMA: Open and Efficient Foundation Language Models,” 2023. Available: arXiv:2302.13971.
  12. OpenAI, “GPT-4 Technical Report,” 2023. Available: arXiv:2303.08774.
  13. Google DeepMind, “Gemini: A Family of Highly Capable Multimodal Models,” 2023. Available: arXiv:2312.11805.
  14. M. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nature Medicine, vol. 25, no. 1, pp. 24–29, 2019.
  15. W. Holmes, M. Bialik, and C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA, USA: Center for Curriculum Redesign, 2019.
  16. A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021.
  17. L. Floridi and J. Cowls, “A Unified Framework of Five Principles for AI in Society,” Harvard Data Science Review, vol. 1, no. 1, 2019.
  18. Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. Bang, A. Madotto, and P. Fung, “Survey of Hallucination in Natural Language Generation,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023.
  19. E. J. Paul Duffy, “Artificial Intelligence in Drug Discovery: Applications and Challenges,” Drug Discovery Today, vol. 25, no. 7, pp. 1312–1320, 2020.
  20. B. Lepri, J. Staiano, D. Sangokoya, E. Letouzé, and N. Oliver, “The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision Making for Social Good,” Data Science and Society, Springer, pp. 3–24, 2017.
  21. I. Goodfellow, P. McDaniel, and N. Papernot, “Making Machine Learning Robust Against Adversarial Inputs,” Communications of the ACM, vol. 61, no. 7, pp. 56–66, 2018.
  22. A. Allam and Z. A. Dhunny, “On Big Data, Artificial Intelligence and Smart Cities,”Cities, vol. 89, pp. 80–91, 2019.
  23. K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA, USA: MIT Press, 2012.
  24. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
  25. S. Bubeck, V. Chandrasekaran, R. Eldan, et al., “Sparks of Artificial General Intelligence: Early Experiments with GPT-4,” arXiv:2303.12712, 2023.
  26. Y. Wang, H. Sun, Q. Liu, et al., “A Survey of Large Language Models,” ACM Computing Surveys, vol. 57, no. 1, 2024.
  27. Y. Bengio, G. Hinton, A. Yao, D. Song, et al., “Managing Extreme AI Risks Through Safe and Responsible AI Development,” arXiv:2310.17688, 2023.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Deep Learning Generative AI Large Language Models Transformer Architecture GPT-4 Explainable AI AI Applications Artificial General Intelligence Multimodal AI Autonomous Agents

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