Summary of Near to Mid-term Risks and Opportunities Of Open-source Generative Ai, by Francisco Eiras et al.
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
by Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H.S. Torr, Trevor Darrell, Yong Suk Lee, Jakob Foerster
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The abstract discusses the future of Generative AI applications in various fields, including science, medicine, and education, which may revolutionize these areas. However, this potential has sparked debates about risks and calls for regulation from tech companies. The authors argue for responsible open-sourcing of generative AI models to avoid stifling innovation. They introduce an AI openness taxonomy system, apply it to 40 large language models, and outline the benefits and risks of open versus closed source AI, along with potential risk mitigation strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative AI is a powerful technology that can change many areas of life, including science, medicine, and education. Some people think this power could be used for good or bad, so they want to regulate it more closely. This might stop new ideas from emerging. The authors suggest sharing the code for these models openly, which would allow others to build upon and improve them. They explain what open-source means, apply it to 40 language models, and talk about the advantages and disadvantages of making AI more or less accessible. |
