On the Challenges and Opportunities in Generative AI

• URL: http://arxiv.org/abs/2403.00025v1
• Date: Wed, 28 Feb 2024 15:19:33 GMT
• Title: On the Challenges and Opportunities in Generative AI
• Authors: Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina D\"aubener, Sophie Fellenz, Asja Fischer, Thomas G\"artner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Bj\"orn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
• Abstract summary: We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
• Score: 135.2754367149689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of deep generative modeling has grown rapidly and consistently over the 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 do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with valuable insights for exploring fruitful research directions, thereby fostering the development of more robust and accessible generative AI solutions.

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