From Data to Imagination: The Evolution of Generative Models
DOI:
https://doi.org/10.15680/IJCTECE.2018.0101001Keywords:
Generative Models, Artificial Creativity, GANs, VAEs, Transformers, Deep Learning, Neural Networks, AI Imagination, Synthetic Media, Machine LearningAbstract
Generative models have revolutionized the landscape of artificial intelligence by shifting the focus from predictive tasks to creative and constructive capabilities. From the early use of probabilistic models to the modern architectures of deep neural networks, the evolution of generative models has been marked by increasing sophistication in capturing data distributions and generating novel content. This paper explores the trajectory of generative modeling—from rudimentary statistical techniques to complex structures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based large language models. By analyzing both the theoretical foundations and practical implementations of these models, we investigate how machines have moved closer to simulating human-like imagination through artificial means.The study utilizes a combination of technical analysis and experimental evaluation to examine the performance of various generative models in tasks related to text, image, and multimodal content creation. We also discuss the philosophical and societal implications of synthetic media, including authorship, originality, and ethical responsibility. A core aim is to understand how data-driven systems have progressed from simply learning representations to autonomously generating meaningful, contextually aware content. Through a detailed methodology involving benchmark datasets, model fine-tuning, and qualitative and quantitative evaluation, we identify key capabilities and limitations of current generative systems.Our findings suggest that while significant progress has been made, generative models still rely heavily on input conditioning, training diversity, and optimization constraints. Despite these limitations, they represent a pivotal advancement in the quest for computational creativity. This paper contributes to the growing discourse on artificial imagination, offering insight into how generative models not only imitate but also expand the creative boundaries of machine learning. As we continue to develop these systems, the line between data processing and autonomous creativity becomes increasingly blurred, raising important questions about the future of AI in human-centric domains.
References
1. 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. https://doi.org/10.1145/3442188.3445922
2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.
3. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.
4. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27.
5. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. arXiv preprint arXiv:2006.11239.
6. Kingma, D. P., & Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv preprint arXiv:1312.6114.
7. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero-Shot Text-to-Image Generation. arXiv preprint arXiv:2102.12092.
8. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Blog.
9. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. arXiv preprint arXiv:2112.10752.
10. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30.
