发表机构
The University of Texas at Austin; Dell Paediatric Research Institute（德克萨斯大学奥斯汀分校; 戴尔儿科研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了人工智能在生物研究中的能力及其生物安全威胁路径，提出通过纵深防御治理将能力阈值与责任挂钩，以降低高风险并保留有益用途。
AI 中文摘要
人工智能正在重塑日益互联的数字到物理工作流程中的生物学研究。通用大语言模型能够检索和整合科学信息，支持实验规划和计算分析；生物基础模型能够预测、优化和生成蛋白质、基因及基因组规模的序列；智能体系统能够协调多步骤研究任务；自动化实验室能够部分闭合设计-构建-测试-学习循环。这些技术可能极大地惠及医学、公共卫生和生物技术。然而，其生物安全风险不仅取决于人工智能能做什么，还取决于谁使用它、他们的专业知识和意图、他们对实验室工具和材料的获取途径，以及现有的保障措施。当前证据表明，人工智能的提升效应确实存在，但主要影响数字任务而非物理任务。前沿系统在计算机模拟和筛查规避基准上已超过专家基线，而受控湿实验室研究发现，隐性知识和物理执行仍是重大障碍。本综述描述了人工智能工具使用带来的不同生物威胁，从信息收集和生物设计到采购、合成、测试、放大和潜在释放。我们进一步审视了为何通用模型的对齐技术难以迁移到生物模型，以及可解释性在审计危险能力是否真正被移除方面的新兴作用。我们主张采取纵深防御治理，将能力阈值与生物人工智能生态系统中相应的责任挂钩，在降低高后果风险的同时保留有益用途。
英文摘要
Artificial intelligence is reshaping biological research across an increasingly connected digital-to-physical workflow. General-purpose large language models can retrieve and integrate scientific information, support experimental planning, and computational analysis; biological foundation models can predict, optimize, and generate proteins, genes, and genome-scale sequences; agentic systems can coordinate multistep research tasks; automated laboratories can partially close the design-build-test-learn cycle. These technologies could greatly benefit medicine, public health, and biotechnology. However, their biosecurity risk depends not only on what the AI can do, but also on who uses it, their expertise and intent, their access to laboratory tools and materials, and the safeguards in place. Current evidence shows that AI uplift exists but primarily affects digital rather than physical tasks. Frontier systems have exceeded expert baselines on in-silico, and screening-evasion benchmarks, whereas controlled wet-laboratory studies find that tacit knowledge and physical execution remain substantial barriers. This review describes the different biological threats from AI tool use, from information gathering and biological design to procurement, synthesis, testing, scale-up, and potential release. We further examine why alignment techniques for general-purpose models transfer poorly to biological ones, and the emerging role of interpretability in auditing whether hazardous capabilities are genuinely removed. We argue for defense-in-depth governance that links capability thresholds to proportionate responsibilities across the biological AI ecosystem, reducing high-consequence risk while preserving beneficial use.
