On the Challenges and Opportunities in Generative AI

On the Challenges and Opportunities in Generative AI. Manduchi, L., Pandey, K., Bamler, R., Cotterell, R., Däubener, S., Fellenz, S., Fischer, A., Gärtner, T., Kirchler, M., Kloft, M., Li, Y., Lippert, C., Melo, G. d., Nalisnick, E., Ommer, B., Ranganath, R., Rudolph, M., Ullrich, K., Broeck, G. V. d., Vogt, J. E, Wang, Y., Wenzel, F., Wood, F., Mandt, S., & Fortuin, V. 2024.

Arxiv

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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.

@unpublished{manduchi-challenges-opportunities-generative-2024,
	doi={10.48550/arXiv.2403.00025},
	url_ArXiv={https://arxiv.org/abs/2403.00025},
	url_pdf={https://arxiv.org/pdf/2403.00025.pdf},
	author={Manduchi, Laura and Pandey, Kushagra and Bamler, Robert and Cotterell, Ryan and Däubener, Sina and Fellenz, Sophie and Fischer, Asja and Gärtner, Thomas and Kirchler, Matthias and Kloft, Marius and Li, Yingzhen and Lippert, Christoph and Melo, Gerard de and Nalisnick, Eric and Ommer, Björn and Ranganath, Rajesh and Rudolph, Maja  and Ullrich, Karen and Broeck, Guy Van den and Vogt, Julia E and Wang, Yixin and Wenzel, Florian and Wood, Frank and Mandt, Stephan and Fortuin, Vincent},
	title={On the Challenges and Opportunities in Generative AI},
	publisher={arXiv},
	year={2024},
	copyright={arXiv.org perpetual, non-exclusive licence},
	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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