As increasingly powerful generative AI systems are developed, the release method greatly varies. We propose a framework to assess six levels of access to generative AI systems: fully closed; gradual or staged access; hosted access; cloud-based or API access; downloadable access; and fully open. Each level, from fully closed to fully open, can be viewed as an option along a gradient. We outline key considerations across this gradient: release methods come with tradeoffs, especially around the tension between concentrating power and mitigating risks. Diverse and multidisciplinary perspectives are needed to examine and mitigate risk in generative AI systems from conception to deployment. We show trends in generative system release over time, noting closedness among large companies for powerful systems and openness among organizations founded on principles of openness. We also enumerate safety controls and guardrails for generative systems and necessary investments to improve future releases.

翻译：随着功能日益强大的生成式人工智能系统不断被开发，其发布方式也呈现出显著差异。我们提出一个框架，用于评估生成式人工智能系统的六个访问层级：完全封闭、渐进或分阶段开放、托管访问、基于云或API访问、可下载访问以及完全开放。从完全封闭到完全开放的每个层级，均可视为一个梯度上的选项。我们概述了这一梯度上的关键考量：发布方式涉及权衡取舍，尤其是在集中权力与降低风险之间的张力方面。需要多样化的跨学科视角来审视并降低生成式人工智能系统从构思到部署过程中的风险。我们展示了生成式系统发布随时间演变的趋势，注意到大型企业针对强大系统倾向于封闭，而基于开放原则创立的组织则倾向于开放。我们还列举了生成式系统的安全控制措施与防护栏，以及改善未来发布所必需的投资。