Recent advancements in generative machine learning have enabled rapid progress in biological design tools (BDTs) such as protein structure and sequence prediction models. The unprecedented predictive accuracy and novel design capabilities of BDTs present new and significant dual-use risks. For example, their predictive accuracy allows biological agents, whether vaccines or pathogens, to be developed more quickly, while the design capabilities could be used to discover drugs or evade DNA screening techniques. Similar to other dual-use AI systems, BDTs present a wicked problem: how can regulators uphold public safety without stifling innovation? We highlight how current regulatory proposals that are primarily tailored toward large language models may be less effective for BDTs, which require fewer computational resources to train and are often developed in an open-source manner. We propose a range of measures to mitigate the risk that BDTs are misused, across the areas of responsible development, risk assessment, transparency, access management, cybersecurity, and investing in resilience. Implementing such measures will require close coordination between developers and governments.

翻译：生成式机器学习的近期进展推动了生物设计工具（BDTs）的快速发展，例如蛋白质结构与序列预测模型。BDTs前所未有的预测精度与新型设计能力带来了显著的新双重用途风险。例如，其预测精度可使疫苗或病原体等生物制剂的开发速度加快，而设计能力则可能被用于发现药物或规避DNA筛查技术。与其他双重用途人工智能系统类似，BDTs构成了一个棘手问题：监管机构如何在不扼杀创新的前提下维护公共安全？我们指出，当前主要针对大语言模型的监管提案对BDTs可能效果有限，因为BDTs所需的训练计算资源更少且通常以开源形式开发。我们提出了一系列在负责任的开发、风险评估、透明度、访问管理、网络安全和韧性投资等领域的措施，以降低BDTs被滥用的风险。实施这些措施需要开发者与政府之间的密切协调。