International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN. 

From Foundations to Frontiers – Tracing the Global Evolution of Artificial Intelligence

    ,

Author Affiliations

  • 1Lingaya's Vidyapeeth, Nachauli, Jasana Road, Faridabad, Haryana-121002, India
  • 2Lingaya's Vidyapeeth, Nachauli, Jasana Road, Faridabad, Haryana-121002, India

Res. J. Computer & IT Sci., Volume 13, Issue (2), Pages 1-6, December,20 (2025)

Abstract

Artificial Intelligence (AI) has transformed from theoretical concepts to a technological revolution impacting every sector of modern society. This paper examines AI's historical progression from early symbolic systems to contemporary deep learning architectures, analyzing its global development across different regions and industries. The study highlights significant applications in healthcare, finance, education, and environmental science while addressing critical ethical concerns surrounding bias, privacy, and governance.

References

  1. Turing, A. M. (1950)., Computing machinery and intelligence., Mind, 59(236), 433-460.
  2. McCulloch, W. S., & Pitts, W. (1943)., A logical calculus of the ideas immanent in nervous activity., Bulletin of Mathematical Biophysics, 5(4), 115-133.
  3. James, A. O., & Akaranta, O. (2011)., Inhibition of corrosion of zinc in hydrochloric acid solution by red onion skin acetone extract., Research Journal of Chemical Sciences, 1(1), 31-37.
  4. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012)., Image Net classification with deep convolutional neural networks., Advances in Neural Information Processing Systems, 25, 1097-1105.
  5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017)., Attention is all you need., Advances in Neural Information Processing Systems, 30, 5998-6008.
  6. Jobin, A., Ienca, M., & Vayena, E. (2019)., The global landscape of AI ethics guidelines., Nature Machine Intelligence, 1(9), 389-399.
  7. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018)., A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play., Science, 362(6419), 1140-1144.
  8. LeCun, Y., Bengio, Y., & Hinton, G. (2015)., Deep learning., Nature, 521(7553), 436-444.
  9. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., &Vayena, E. (2018)., AI4People—An ethical framework for a good AI society., Minds and Machines, 28(4), 689-707.
  10. Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017)., Neuroscience-inspired artificial intelligence., Neuron, 95(2), 245-258. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., &Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
  11. Marcus, G. (2018)., Deep learning: A critical appraisal., arXiv preprint arXiv:1801.00631.
  12. Binns, R. (2018)., Fairness in machine learning: Lessons from political philosophy., Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159.
  13. Buolamwini, J., & Gebru, T. (2018)., Gender shades: Intersectional accuracy disparities in commercial gender classification., Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 77-91.
  14. Rahwan, I. (2018)., Society-in-the-loop: Programming the algorithmic social contract., Ethics and Information Technology, 20(1), 5-14. https://doi.org/10.1007/s10676-017-9430-8
  15. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019)., XAI—Explainable artificial intelligence., Science Robotics, 4(37), eaay7120. https://doi.org/10.1126/scirobotics.aay7120
  16. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021)., On the dangers of stochastic parrots: Can language models be too big?., Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623.
  17. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021)., Datasheets for datasets., Communications of the ACM, 64(12), 86-92. https://doi.org/10.1145/3458723
  18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243., undefined, undefined
  19. Mitchell, M. (2019)., Artificial intelligence: A guide for thinking humans., Farrar, Straus and Giroux.
  20. Susskind, R. (2018)., Online courts and the future of justice., Oxford University Press.
  21. Eubanks, V. (2018)., Automating inequality: How high-tech tools profile, police, and punish the poor., St. Martin
  22. Zuboff, S. (2019)., The age of surveillance capitalism: The fight for a human future at the new frontier of power., PublicAffairs.
  23. Dignum, V. (2019)., Responsible artificial intelligence: How to develop and use AI in a responsible way., Springer.
  24. Marcus, G., & Davis, E. (2019)., Rebooting AI: Building artificial intelligence we can trust., Pantheon Books.
  25. Crawford, K. (2021)., Atlas of AI: Power, politics, and the planetary costs of artificial intelligence., Yale University Press.
  26. Domingos, P. (2015)., The master algorithm: How the quest for the ultimate learning machine will remake our world., Basic Books.
  27. O, Weapons of math destruction: How big data increases inequality and threatens democracy., Crown.
  28. Tegmark, M. (2017)., Life 3.0: Being human in the age of artificial intelligence., Knopf.
  29. Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018)., Notes from the AI frontier: Insights from hundreds of use cases., McKinsey Global Institute.
  30. Russell, S., & Norvig, P. (2021)., Artificial intelligence: A modern approach (4th ed.)., Pearson.
  31. Goodfellow, I., Bengio, Y., & Courville, A. (2016)., Deep learning., MIT Press.
  32. Bostrom, N. (2014)., Superintelligence: Paths, dangers, strategies., Oxford University Press.
  33. Paramanik, A., & Paramanik, R. C. (2014)., Assessment of medicinal plants and environmental factors using molecular marker., International E-Publication. ISBN: 978-93-84648-12-1
  34. Brynjolfsson, E., & McAfee, A. (2017)., Machine, platform, crowd: Harnessing our digital future., W. W. Norton & Company.
  35. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020)., Language models are few-shot learners., Advances in Neural Information Processing Systems, 33, 1877-1901.
  36. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020)., Exploring the limits of transfer learning with a unified text-to-text transformer., Journal of Machine Learning Research, 21(140), 1-67.
  37. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021)., On the opportunities and risks of foundation models., arXiv preprint arXiv:2108.07258.
  38. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022)., Hierarchical text-conditional image generation with CLIP latents., arXiv preprint arXiv:2204.06125.
  39. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., &Amodei, D. (2017)., Deep reinforcement learning from human preferences., Advances in Neural Information Processing Systems, 30.
  40. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... &Sutskever, I. (2021)., Learning transferable visual models from natural language supervision., International Conference on Machine Learning, 8748-8763.
  41. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017)., Proximal policy optimization algorithms., arXiv preprint arXiv:1707.06347.
  42. Ho, J., Jain, A., & Abbeel, P. (2020)., Denoising diffusion probabilistic models., Advances in Neural Information Processing Systems, 33, 6840-6851.
  43. Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., ... & Kavukcuoglu, K. (2018)., IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures., International Conference on Machine Learning, 1407-1416.
  44. Fedus, W., Zoph, B., & Shazeer, N. (2022)., Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity., Journal of Machine Learning Research, 23(120), 1-39.
  45. Varala, R. (2013)., A facile synthesis of biologically active phthalimides & its analogues - A study [Doctoral thesis], . International E-Publication. ISBN: 978-93-83520-15-2
  46. Turing, A. M. (1950)., Computing machinery and intelligence., Mind, 59(236), 433–460.
  47. Copeland, B. J. (2004)., The essential Turing: Seminal writings in computing, logic, philosophy, artificial intelligence, and artificial life., Oxford University Press.
  48. McCulloch, W. S., & Pitts, W. (1943)., A logical calculus of the ideas immanent in nervous activity., The Bulletin of Mathematical Biophysics, 5(4), 115–133.
  49. Piccinini, G. (2004)., The first computational theory of mind and brain: A close look at McCulloch and Pitts’s “Logical Calculus of Ideas Immanent in Nervous Activity.”, Synthese, 141(2), 175–215.
  50. Kothari, D. P. (2011)., Energy and environmental problems facing India and their solutions for sustainable development., Souvenir from 1st International Science Congress, Indore, India, 24-25 Dec. (pp. 1-3).
  51. Weizenbaum, J. (1966)., ELIZA—A computer program for the study of natural language communication between man and machine., Communications of the ACM, 9(1), 36–45.
  52. Güzeldere, G., & Franchi, S. (1995)., Dialogues with colorful personalities of early AI., Stanford Humanities Review, 4(2), 161–169.
  53. Shortliffe, E. H. (1976)., Computer-based medical consultations: MYCIN., Elsevier. ISBN: 978-0-444-00163-4
  54. European Commission (2021)., Proposal for a regulation laying down harmonized rules on artificial intelligence (AI Act).,
  55. OECD (2019)., Principles on artificial intelligence.,
  56. Nilsson, N. J. (1984)., Shakey the Robot (Technical Report No. 323)., SRI International.
  57. Brynjolfsson, E., & McAfee, A. (2017)., Artificial intelligence, for real., Harvard Business Review, 1(1), 1–31.
  58. Lee, K.-F. (2018)., AI Superpowers: China, Silicon Valley, and the New World Order., Houghton Mifflin Harcourt.
  59. Amodei, D., et al. (2016)., Concrete problems in AI safety., arXiv preprint arXiv:1606.06565.
  60. Vaswani, A., et al. (2017)., Attention is all you need., Advances in Neural Information Processing Systems (NeurIPS), 30, 5998–6008.
  61. Bengio, Y. (2019)., The consciousness prior., arXiv preprint arXiv:1709.08568.
  62. CIFAR. (2020)., Pan-Canadian AI Strategy: Annual Report., Canadian Institute for Advanced Research.
  63. Topol, E. (2019)., Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again., Basic Books.
  64. World Economic Forum. (2023)., The future of AI in Africa: Challenges and opportunities.,
  65. Jumper, J., et al. (2021)., Highly accurate protein structure prediction with Alpha Fold., Nature, 596(7873), 583–589.
  66. Obermeyer, Z., et al. (2019)., Dissecting racial bias in an algorithm used to manage the health of populations., Science, 366(6464), 447–453.
  67. GDPR. (2016)., General Data Protection Regulation (EU) 2016/679., European Parliament.
  68. Crawford, K. (2021)., Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence., Yale University Press.
  69. Buolamwini, J., & Gebru, T. (2018)., Gender shades: Intersectional accuracy disparities in commercial gender classification., Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT), 77–91.
  70. Russell, S. (2019)., Human Compatible: AI and the Problem of Control., Viking.
  71. Bostrom, N. (2014)., Superintelligence: Paths, Dangers, Strategies., Oxford University Press.
  72. Zuboff, S. (2019)., The Age of Surveillance Capitalism., Public Affairs.
  73. European Commission. (2021)., Proposal for a Regulation on a European Approach for Artificial Intelligence (AI Act)., COM (2021) 206 final.
  74. Zhang, D., et al. (2021)., The AI divide: China, the US, and the global AI race., Brookings Institution Report.
  75. UNESCO. (2021)., Recommendation on the Ethics of Artificial Intelligence., United Nations.