The Evolution Of Artificial Intelligence: From Machine Learning To Generative Intelligence
Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Generative Intelligence, Neural Networks, Large Language Models, AI EthicsAbstract
Artificial Intelligence (AI) has undergone a remarkable transformation over the past few decades, evolving from basic rule-based systems to complex generative models capable of producing human-like text, images, and even reasoning patterns. The journey of AI reflects a deep interplay between computational power, data availability, and algorithmic innovation. Initially, AI research focused on symbolic logic and rule-based problem-solving, later giving way to machine learning and deep learning paradigms that enabled machines to learn patterns from massive datasets. The recent advent of generative intelligence, powered by large language models (LLMs) and multimodal systems, has marked a new chapter in AI development, where creativity, linguistic capability, and reasoning are being simulated at an unprecedented scale. This article explores the historical evolution of AI, its foundational milestones, and the shift from traditional machine learning to generative intelligence. Furthermore, it discusses the ethical, social, and philosophical implications of this transformation, as well as future trajectories of intelligent systems in education, science, and society.
References
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Silver, D., Schrittwieser, J., Simonyan, K., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
