School of Biomedical Sciences, Li KaShing Faculty of Medicine, Hong Kong University, Pokfulam, SAR Hong Kong, China.
Microb Biotechnol. 2024 Oct;17(10):e70014. doi: 10.1111/1751-7915.70014.
The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied.
新的微生物生物技术和人工智能 (AI) 技术的出现为监测甚至有时控制病原体的进化开辟了一个全新的领域。然而,现在著名的生成式 AI 从大型数据集提取和重组先前的知识,使其不适合对不可靠的未来进行预测。相比之下,一个陌生的视角可以帮助我们识别与新技术的出现相关的关键问题,例如来自合成生物学的问题,同时重新审视 AI 的旧观点或将生成式 AI 作为一种溯因推理的生成器纳入其中。这可以使我们能够识别在不久的将来必然会出现的危险情况,并做好准备预测它们何时何地会发生。在这里,我们强调了这样一个事实,即在导致病原体爆发的众多原因中,实验室事故通常是由人口爆炸引起的,是流行病的主要原因。本综述仅限于动物病原体,并以讨论不常见的生物体或很少被强调或研究的生物体的异常组合为基础,探讨了潜在的流行起源。