Adapting GenAI Strategies: Understanding Models, Aims, and Challenges in Different Targeted Data and Domains
Wenli Yang, Yuan Lü, Soonja Yeom, David Herbert
Abstract
Generative Artificial Intelligence (GenAI) techniques have received massive and widespread industry-disrupting attention because they can create synthetic data, images, text, and other types of content that look very close to real-world examples. Unlike traditional AI that mainly focuses on classification or prediction, GenAI models learn and copy data distributions. This allows new applications such as content creation, data augmentation, and training of machine learning models. In this survey, we organise the discussion around two major themes: general strategies in GenAI and domain-specific adaptation. We develop a mapping approach to help researchers and practitioners find suitable GenAI techniques for different domains. For each domain, we provide detailed discussions about data considerations, real-world case studies, the use of domain-related tools and frameworks, and practical guidance. We also review domain-specific challenges and opportunities to support future research and application work. Our findings give a full view of the changing GenAI field and suggest ways to build more effective, flexible, and strong generative solutions.