Architecting Intelligence: A New Era of Generative AI and Deep Learning Models

Abstract

Generative AI, or GenAI, has been a highly transformative element in the realm of AI, shaped by advancements in architectures of deep learning. Transformer-based models such as GPT, BERT, and their successors have taken the NLP domain into an unprecedented sphere by infusing contextual and generative capabilities. Large language models (LLMs) are in the process of revolutionising human-computer interaction through applications involving content generation, summarisation, and translation. Similarly, small language models (SLMs) are emerging for resource-constrained settings, balancing efficiency with performance. In computer vision, CNNs are still at the core of things, but new architectures like EfficientNet and Vision Transformers (ViTs) have expanded the scope of applicability in multiple tasks such as object detection and image classification. GANs continue to push the creative boundaries of what can be synthesized into hyper-realistic images, videos, and even music. Despite these successes, difficulties such as mode collapse led researchers to develop other models, like diffusion models, that proved highly robust for the production of high-quality output. Encoders are commonly combined with decoders in transformer models. Underlying most successful state-of-the-art models are tasks in machine translation and representation learning. Diffusion models, inspired by physical processes, have recently proven to be a breakthrough in generative modeling in terms of coherent and diverse data samples. This paper surveys the evolution and synergy of these deep learning architectures and their roles in advancing AI across domains. Through exploring innovations and their interdisciplinary applications, we investigate how the convergence of these technologies is shaping the future of AI. The discussion then touches on scalability, efficiency, and ethical considerations as it moves towards emerging trends, such as hybrid models that blend the strengths of multiple architectures, catapulting AI to new frontiers of creativity and utility.