Risks and Opportunities of Open-Source Generative AI

Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder, Fabio Pizzati +20 more
5/14/2024
cs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{eiras2024risks,
  title  = {Risks and Opportunities of Open-Source Generative AI},
  author = {Francisco Eiras and Aleksandar Petrov and Bertie Vidgen and Christian Schroeder and Fabio Pizzati and Katherine Elkins and Supratik Mukhopadhyay and Adel Bibi and Aaron Purewal and Csaba Botos and Fabro Steibel and Fazel Keshtkar and Fazl Barez and Genevieve Smith and Gianluca Guadagni and Jon Chun and Jordi Cabot and Joseph Imperial and Juan Arturo Nolazco and Lori Landay and Matthew Jackson and Phillip H. S. Torr and Trevor Darrell and Yong Lee and Jakob Foerster},
  year   = {2024},
  eprint = {2405.08597},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.08597v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.08597.svg)](https://codeofpaper.com/paper/2405.08597)

Discussion