Abstract
In the next few years, applications of Generative 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 potential risks and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. While regulation is important, it is key that it does not put at risk the budding field of open-source Generative AI. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact.
| Original language | English |
|---|---|
| Pages (from-to) | 12348-12370 |
| Number of pages | 23 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 235 |
| Publication status | Published - 27 Jul 2024 |
| Event | 41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria Duration: 21 Jul 2024 → 27 Jul 2024 |
Acknowledgements
The authors would like to thank Meta for their generous support, including travel grants and logistical assistance, which enabled this collaboration, as well as for the organization of the first Open Innovation AI Research Community workshop where this work was initiated. Meta had no editorial input in this paper, and the views expressed herein do not reflect those of the company.ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability