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：近期生成式机器学习的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前所未有的预测准确性和新颖设计能力带来了新的重大双重用途风险。例如，其预测准确性使得疫苗或病原体等生物制剂开发速度加快，而设计能力则可能被用于发现药物或规避DNA筛查技术。与其他具有双重用途的人工智能系统类似，BDTs呈现出一个棘手问题：监管者如何在遏制创新的同时维护公共安全？我们指出，当前主要针对大语言模型制定的监管提案对BDTs可能效果有限——后者训练所需计算资源更少，且通常以开源方式开发。我们提出一系列措施，涵盖负责任开发、风险评估、透明度、访问管理、网络安全与韧性建设等领域，以降低BDTs被滥用的风险。实施这些措施需要开发方与政府之间的紧密协作。