关于 AI 开发的公开信

仅仅依赖闭源模型并不天然安全：它们可能遭到攻破、被滥用，或以外部人员无法察觉的方式发生失效。将先进 AI 能力集中在少数闭源模型背后，会进一步加剧这一风险。这会形成少量单点故障，削弱竞争，并将关键技术交由少数供应商掌控。相比之下，开放权重模型允许广泛的研究人员和开发者社区审查其行为、识别漏洞、开发防护措施，并随着时间推移持续改进它们。

在塑造这一生态系统时，政策制定者应谨慎，不要将正当的模型开发技术与不当挪用混为一谈。蒸馏，即利用一个模型的输出帮助训练或改进另一个模型的做法，是一种被广泛用于模型改进、评估和验证的技术。它延续了从既有技术中学习、在其基础上构建并加以改进的悠久传统；自开源软件运动兴起以来，这一传统一直在推动创新。

我们请求美国政府支持一项国际努力，开发必要的技术和治理工具，以审慎控制自动化 AI 开发的前沿发展速度。

Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time.

In shaping this ecosystem, policymakers should be careful not to conflate legitimate model-development techniques with misappropriation. Distillation, or the practice of using one model’s outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement.

We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.