As advancements in artificial intelligence propel progress in the life sciences, they may also enable the weaponisation and misuse of biological agents. This article differentiates two classes of AI tools that pose such biosecurity risks: large language models (LLMs) and biological design tools (BDTs). LLMs, such as GPT-4, are already able to provide dual-use information that could have enabled historical biological weapons efforts to succeed. As LLMs are turned into lab assistants and autonomous science tools, this will further increase their ability to support research. Thus, LLMs will in particular lower barriers to biological misuse. In contrast, BDTs will expand the capabilities of sophisticated actors. Concretely, BDTs may enable the creation of pandemic pathogens substantially worse than anything seen to date and could enable forms of more predictable and targeted biological weapons. In combination, LLMs and BDTs could raise the ceiling of harm from biological agents and could make them broadly accessible. The differing risk profiles of LLMs and BDTs have important implications for risk mitigation. LLM risks require urgent action and might be effectively mitigated by controlling access to dangerous capabilities. Mandatory pre-release evaluations could be critical to ensure that developers eliminate dangerous capabilities. Science-specific AI tools demand differentiated strategies to allow access to legitimate users while preventing misuse. Meanwhile, risks from BDTs are less defined and require monitoring by developers and policymakers. Key to reducing these risks will be enhanced screening of gene synthesis, interventions to deter biological misuse by sophisticated actors, and exploration of specific controls of BDTs.

翻译：随着人工智能的进步推动生命科学的发展，其也可能使生物制剂被武器化和滥用。本文区分了构成此类生物安全风险的两类人工智能工具：大型语言模型（LLMs）和生物设计工具（BDTs）。已有能力提供双重用途信息的大型语言模型，例如GPT-4，本可助推历史上的生物武器研发成功。当大型语言模型转变为实验室助手和自主科学工具时，这将进一步增强其支持研究的能力。因此，大型语言模型将尤其降低生物滥用的门槛。相比之下，生物设计工具将扩展复杂行为者的能力。具体而言，生物设计工具可能催生比迄今所见任何病原体严重得多的大流行性病原体，并可能引发更可预测和更具针对性的生物武器形式。两者结合，大型语言模型与生物设计工具可能提升生物制剂的危害上限，并使其广泛可及。大型语言模型与生物设计工具不同的风险特征对风险缓解具有重要启示。大型语言模型风险需紧急行动，且可通过控制危险能力的获取有效缓解。强制性的发布前评估对于确保开发者消除危险能力至关重要。针对科学的专用人工智能工具需采取差异化策略，在允许合法用户访问的同时防止滥用。与此同时，生物设计工具的风险尚不明确，需要开发者和政策制定者持续监测。降低这些风险的关键在于加强基因合成筛查、干预以阻止复杂行为者进行生物滥用，并探索对生物设计工具的特定管控。