Autonomous driving paper index
From capability uplift to capability governance: an AI–biosecurity stack
One-line summary
Artificial intelligence is becoming a general-purpose enabling technology for the life sciences.
Engineering notes
Key topics: self-driving, foundation model. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Artificial intelligence is becoming a general-purpose enabling technology for the life sciences. AI-enabled tools can strengthen biosecurity and biodefense by improving early warning, accelerating vaccine and therapeutic discovery, supporting laboratory safety, and enabling more adaptive preparedness systems. However, these same tools may be a potential amplifier of misuse. The 2025 National Academies’ report The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations provides an important foundation for this analysis of the “capability uplift” enabled by AI across the design–build–test–learn (DBTL) cycle. “Capability uplift,” or ΔAI, is a term used in the report to assess how AI-enabled biological tools can uniquely, and in some cases, specifically enable increases in or changes to biosecurity risks. The report proposes an “if–then” approach to monitor emerging capabilities through observable indicators such as new datasets as the leading indicator of capability, model performance, and the erosion of build/test barriers. Since the report’s publication, agentic AI systems, virtual scientific teams, genome-scale foundation models, and self-driving laboratories have advanced from largely prospective concerns to early demonstrations. Multi-agent systems have been reported for biomedical hypothesis generation, design, and semi-autonomous discovery workflows, while self-driving laboratories now are considered as practical platforms for biotechnology. This perspective article extends these insights by employing a conceptual framework analysis and involves: (1) categorizing key AI capabilities across the DBTL cycle into a layered capability stack, and (2) illustrate how the if-then approach can be used to inform a dashboard based on observable indicators.
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