Generative AI: A Comparative Analysis of Architectures, Evaluation Metrics, and Emerging Challenges

Generative artificial intelligence (GenAI) has been the game-changing force across the board, triggered by advances in foundational models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Large Language Models (LLMs). The chapter provides an in-depth account on wide ranges of outputs in probabilistic latent spaces which are appropriately rendered by VAEs whereas GANs do produce high-fidelity outputs through adversarial training. LLMs produce text from scratch based on transformer-based autoregressive models, and diffuse models go through iterations of noise in the appropriate manner. Comparison analysis results in model comparisons for output quality training stability computational efficiency as well as scalability. Ethical, legal, and technical issues are also addressed in the chapter, such as those related to training data biases, with respect to deepfakes, as well as intellectual property. This work encourages one framework towards improving GenAI. Employing architectural expertise, together with performance indicators as well as ethical standards, it focuses on the development of sound judgment criteria, regulating buildings, as well as sustainable innovation for achieving all its potential responsibly.