Risks and Opportunities of Open-Source Generative AI
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Eiras, Francisco Petrov, Aleksandar Vidgen, Bertie Schroeder, Christian Pizzati, Fabio Elkins, Katherine Mukhopadhyay, Supratik Bibi, Adel Purewal, Aaron Botos, Csaba Steibel, Fabro Keshtkar, Fazel Barez, Fazl Smith, Genevieve Guadagni, Gianluca Chun, Jon Cabot, Jordi Imperial, Joseph Nolazco, Juan Arturo Landay, Lori Jackson, Matthew Torr, Phillip H. S. Darrell, Trevor Lee, Yong Foerster, Jakob |
| author_facet | Eiras, Francisco Petrov, Aleksandar Vidgen, Bertie Schroeder, Christian Pizzati, Fabio Elkins, Katherine Mukhopadhyay, Supratik Bibi, Adel Purewal, Aaron Botos, Csaba Steibel, Fabro Keshtkar, Fazel Barez, Fazl Smith, Genevieve Guadagni, Gianluca Chun, Jon Cabot, Jordi Imperial, Joseph Nolazco, Juan Arturo Landay, Lori Jackson, Matthew Torr, Phillip H. S. Darrell, Trevor Lee, Yong Foerster, Jakob |
| contents | Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Risks and Opportunities of Open-Source Generative AI Eiras, Francisco Petrov, Aleksandar Vidgen, Bertie Schroeder, Christian Pizzati, Fabio Elkins, Katherine Mukhopadhyay, Supratik Bibi, Adel Purewal, Aaron Botos, Csaba Steibel, Fabro Keshtkar, Fazel Barez, Fazl Smith, Genevieve Guadagni, Gianluca Chun, Jon Cabot, Jordi Imperial, Joseph Nolazco, Juan Arturo Landay, Lori Jackson, Matthew Torr, Phillip H. S. Darrell, Trevor Lee, Yong Foerster, Jakob Machine Learning Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI. |
| title | Risks and Opportunities of Open-Source Generative AI |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.08597 |