On the Challenges and Opportunities in Generative AI
It addresses problems for researchers in generative AI by providing insights for future work, but it is incremental as it focuses on identifying issues rather than solving them.
The paper identifies fundamental shortcomings in current large-scale generative AI models that hinder widespread adoption, aiming to highlight unresolved challenges to enhance capabilities, versatility, and reliability.
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models show tremendous promise in synthesizing high-resolution images and text, as well as structured data such as videos and molecules. However, we argue that current large-scale generative AI models exhibit several fundamental shortcomings that hinder their widespread adoption across domains. In this work, our objective is to identify these issues and highlight key unresolved challenges in modern generative AI paradigms that should be addressed to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with insights for exploring fruitful research directions, thus fostering the development of more robust and accessible generative AI solutions.