Deep Learning and Generative AI Systems: Concepts, Architectures, and Model Landscape

This article explores the evolution of deep learning and generative AI systems, from neural networks to large language models (LLMs) and multimodal AI. It covers key architectures, the shift from prediction to generation, and how modern AI systems are built.

Details

The article starts by explaining how deep learning stacks layers to learn representations, going from input features to higher-level abstractions. This automatic feature extraction is a key advantage over traditional manual feature engineering. It discusses various deep learning architectures like CNNs, DBNs, and DHNs, noting that no single model excels in all domains. The focus then shifts to the transition from predictive to generative models, where the goal is to learn the underlying data distribution and generate new content. Approaches like VAEs, GANs, diffusion models, and transformers are highlighted. Large language models (LLMs) are explored next, explaining how their token prediction capability can lead to reasoning-like behavior, despite limitations like hallucination and outdated knowledge. Techniques like fine-tuning and retrieval-augmented generation (RAG) are mentioned as ways to address these issues. The article then introduces multimodal AI, which combines text, image, audio, and video understanding into a unified system. Finally, it emphasizes that modern AI is not a single model, but a composition of various components, and the real challenge lies in this system-level architecture.