Summary of On the Challenges and Opportunities in Generative Ai, by Laura Manduchi et al.
On the Challenges and Opportunities in Generative AI
by Laura Manduchi, Kushagra Pandey, Clara Meister, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning models have made tremendous progress in generating high-resolution images, text, and structured data like videos and molecules. However, despite their promising capabilities, large-scale generative AI models still face fundamental challenges that hinder their widespread adoption across domains. This paper identifies these issues and highlights the key unresolved challenges in modern generative AI paradigms, providing insights for researchers to explore fruitful research directions and develop more robust and accessible generative AI solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models have made tremendous progress in generating high-resolution images, text, and structured data like videos and molecules. However, large-scale generative AI models still face some big challenges that make it hard for them to be used widely across different areas. This paper helps us understand what these challenges are and how they can be fixed, so we can make more powerful and easy-to-use AI systems. |
Keywords
* Artificial intelligence * Deep learning
