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中英文对照版本（机器翻译，仅供参考）：
Safety fears as scientists make first viruses designed by AI
The Guardian
Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe.
科学家首次利用人工智能设计出了病毒。这一里程碑式的进展让人们对开发新型药物充满期待，但与此同时，也引发了人们对于如何确保这项技术安全的担忧。
The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killed E coli bugs that were resistant to natural bacteriophages.
这些病毒属于一种特殊的病毒，被称为“噬菌体”（bacteriophages），它们只感染细菌，目前世界各地都在利用噬菌体治疗持续性感染患者。在实验室测试中，一组由人工智能设计的病毒组合成功杀死了对天然噬菌体具有耐药性的大肠杆菌（E. coli）。
1.bacteriophage/bækˈtɪəriəˌfeɪdʒ/ n.[病毒] 噬菌体；抗菌素
2.E coli 大肠杆菌（Escherichia/ˌeʃəˈrɪkɪə/ coli）
Dr Brian Hie, a chemical engineer at Stanford University in California, used genome language models, the genetic equivalent of the large language models behind AI chatbots, to design functioning genomes for bacteriophages. The viruses were then made in the laboratory and pitted against E coli in a dish.
加州斯坦福大学的化学工程师布莱恩·希博士利用“基因组语言模型”（genome language models）来设计能够正常发挥作用的噬菌体基因组。这类模型可以被看作是人工智能聊天机器人背后的大型语言模型（LLM）在基因领域的对应版本。随后，研究人员在实验室中制造出这些病毒，并将它们与培养皿中的大肠杆菌进行对抗。
3.genome/ˈdʒiːnəʊm/ n.(biology 生)the complete set of genes in a cell or living thing 基因组；染色体组
例：the human genome 人体基因组
4.pit sb/sth against sth
to test sb or their strength, intelligence, etc. in a struggle or contest against sb/sth else 使竞争；使较量；使经受考验
eg:Lawyers and accountants felt that they were being pitted against each other. 律师和会计师都觉得他们要一争高下。
The ability to “rapidly design” genomes and tune them for specific bugs while overcoming resistance could “transform phage therapy” and “expand biotechnological toolkits”, the researchers wrote in the journal Science.
研究人员在《科学》（Science）杂志上写道，能够“快速设计”基因组，并针对特定细菌对其进行调整，同时克服细菌的耐药性，有望“改变噬菌体疗法”，并“拓展生物技术工具箱”。
But beyond the potential benefits, the scientists said the work raised “important biosafety, biocontainment and biosecurity considerations” and urged others who were designing whole genomes to “consult both safety and security professionals throughout the project”.
但科学家们表示，除了潜在的益处之外，这项工作也带来了“重要的生物安全、生物遏制和生物安保方面的问题”。他们敦促其他从事完整基因组设计的研究人员，在整个项目过程中“征求安全和安保专业人士的意见”。
In an accompanying article, Prof Tom Inglesby and Dr Mori Hanke at the Center for Health Security at Johns Hopkins University in Baltimore, reinforced the warning, writing: “Although this is promising for life sciences applications, it also raises urgent biosafety and biosecurity questions. The ability to compose viral genomes using generative AI now exists; the governance to safely steer it does not.”
在一篇配套评论文章中，巴尔的摩约翰斯·霍普金斯大学健康安全中心的汤姆·英格尔斯比教授和莫里·汉克博士进一步强调了这一警告。他们写道：“尽管这项技术在生命科学领域具有广阔前景，但它也引发了迫切的生物安全和生物安保问题。利用生成式人工智能编写病毒基因组的能力如今已经存在；但能够安全引导和监管这项技术的治理机制却尚未建立。”
Hie and his colleagues used AI models called Evo1 and Evo2 to design the new viral genomes. The models were trained on genetic data from 2m bacteriophages. The genetic code for viruses that can infect plants, humans or other animals was intentionally excluded from the AI’s training to reduce the risk of it designing dangerous viruses.
希博士及其同事使用名为 Evo1 和 Evo2 的人工智能模型来设计新的病毒基因组。这些模型使用来自200万个噬菌体的遗传数据进行训练。为了降低人工智能设计出危险病毒的风险，研究人员特意将能够感染植物、人类或其他动物的病毒遗传密码排除在训练数据之外。
The AI generated thousands of potential genomes from which the researchers selected nearly 300 to make in the lab. These were dropped into bacteria, which read the genetic code and churned out the new bacteriophages. The process was not efficient: only 16 bacteriophages proved to be viable, but a cocktail of them swiftly overcame resistance in two different strains of E. coli.
人工智能生成了数千种潜在的基因组，研究人员从中挑选了近300种，在实验室进行制造。这些基因组被导入细菌中，由细菌读取其中的遗传密码，并制造出新的噬菌体。这个过程的效率并不高：最终只有16种噬菌体被证明能够存活并发挥作用，但将其中多种噬菌体组合起来使用后，很快就克服了两种不同菌株大肠杆菌的耐药性。
5.churn out To churn out something means to produce large quantities of it very quickly. 快速大量生产
eg:He began to churn out literary compositions in English. 他开始用英文很快地创作出大量的文学作品。
6.cocktail 此处表示[C] a mixture of different substances, usually ones that do not mix together well （常指掺合不太相容的）混合物
Eg:a lethal cocktail of drugs 致命的混合药物
7.strain 此处表示[C] a particular type of plant or animal, or of a disease caused by bacteria, etc. （动、植物的）系，品系，品种；（疾病的）类型
Eg:a new strain of mosquitoes resistant to the poison 对这种毒药有抗药性的新品种蚊子
Bacteriophage genomes are tiny, but Inglesby and Hanke said the work nevertheless proved that generative AI could create functioning viral genomes. Whether the same approach could be applied to other viruses was unknown, but they said work on pathogens that could infect humans, animals or plants should not be pursued.
噬菌体的基因组非常小，但英格尔斯比和汉克表示，这项研究仍然证明了生成式人工智能能够创造出可以正常发挥作用的病毒基因组。目前尚不清楚同样的方法是否可以应用于其他病毒，但他们表示，不应该继续开展针对能够感染人类、动物或植物的病原体的相关研究。
8.pathogen/ˈpæθədʒən/n.a thing that causes disease 病原体
“Such genomes might encode new pathogens that … cannot be contained by existing countermeasures,” they wrote.
他们写道：“这类基因组可能编码出新的病原体，而现有的应对措施……可能无法对其进行遏制。”
Tom Ellis, professor of synthetic genome engineering at Imperial College London, said the work was impressive, but revealed how hard it would be to make more complex genomes. “This is literally the smallest and easiest genome to make,” he said.
伦敦帝国理工学院合成基因组工程教授汤姆·埃利斯表示，这项工作令人印象深刻，但同时也揭示了制造更加复杂基因组的难度。“这实际上就是最小、最容易制造的一类基因组，”他说。
An AI trained on the genetic code of dangerous bugs could be used to design more harmful viruses, Ellis said, but controlling access to genetic data and having restrictions on making genomes that look dangerous would help. “Governments are working hard to do this already,” he added.
埃利斯表示，如果人工智能使用危险病原体的遗传密码进行训练，那么它可能被用于设计危害更大的病毒。不过，限制人们获取遗传数据的权限，并对制造看起来具有危险性的基因组加以限制，将有助于降低风险。“各国政府已经在努力开展这方面的工作，”他补充道。
“But honestly,” he said, “the threat from full AI design and writing of a genome of a virus or bacteria is very overblown when we consider that just taking existing pathogens and making gain-of-function changes to their genomes is so much easier and much more likely to be a real pathogenic threat.”
但他说：“不过说实话，如果考虑到直接利用现有病原体，并对其基因组进行功能增强（gain-of-function）改造，要容易得多，而且也更有可能成为现实中的病原体威胁，那么人们对于完全依靠人工智能设计和编写病毒或细菌基因组所产生的威胁，其实存在很大的夸大。”
9.overblown/ˌəʊvəˈbləʊn/ adj.1)that is made to seem larger, more impressive or more important than it really is 过分的；夸张的；虚饰过度的
SYN exaggerated
2)(of flowers 花朵) past the best, most beautiful stage 残败的；盛期已过的
Dr Filippa Lentzos, a reader in science and international security at King’s College London, said the most important point to intervene at the moment was when DNA was being manufactured. “It’s important to see the bigger governance picture and not focus regulation solely on the AI model,” she said. “A layered approach makes more sense: safeguards around model development and access, responsible research review, synthesis screening, and established laboratory biosafety and biosecurity.”
伦敦国王学院科学与国际安全领域高级讲师菲利帕·伦佐斯博士表示，目前最重要的干预环节是在DNA实际制造出来的时候。“重要的是从更宏观的治理角度来看待这个问题，而不是把监管重点仅仅放在人工智能模型上，”她说。“采取分层式的方法更加合理：既要对模型的开发和访问设置安全保障，也要进行负责任的研究审查、DNA合成筛查，并落实成熟的实验室生物安全和生物安保措施。”
10.synthesis/ˈsɪnθəsɪs/ (pl. syntheses/‑siːz/)
1)~ (of sth) the act of combining separate ideas, beliefs, styles, etc.; a mixture or combination of ideas, beliefs, styles, etc. 综合；结合；综合体
eg:the synthesis of art with everyday life 艺术与日常生活的结合
2)[U]the natural chemical production of a substance in animals and plants （物质在动植物体内的）合成
eg:protein synthesis 蛋白质的合成
3)[U]the artificial production of a substance that is present naturally in animals and plants （人工的）合成
eg:the synthesis of penicillin 青霉素的合成
4)[U]the production of sounds, music or speech by electronic means （用电子手段对声音、音乐或语音的）合成
eg:speech synthesis 语音合成
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原文：
Safety fears as scientists make first viruses designed by AI
The Guardian
Scientists have made the first viruses designed by artificial intelligence in a milestone that raises hopes for new medicines but also concerns over how to ensure the technology remains safe.
The viruses are specific kinds known as bacteriophages, which only infect bacteria and are used around the world to treat patients with persistent infections. In lab tests, a cocktail of the AI-designed viruses killedE coli bugs that were resistant to natural bacteriophages.
Dr Brian Hie, a chemical engineer at Stanford University in California, used genome language models, the genetic equivalent of the large language models behind AI chatbots, to design functioning genomes for bacteriophages. The viruses were then made in the laboratory and pitted againstE coli in a dish.
The ability to “rapidly design” genomes and tune them for specific bugs while overcoming resistance could “transform phage therapy” and “expand biotechnological toolkits”, the researchers wrote in the journalScience.
But beyond the potential benefits, the scientists said the work raised “important biosafety, biocontainment and biosecurity considerations” and urged others who were designing whole genomes to “consult both safety and security professionals throughout the project”.
In anaccompanying article, Prof Tom Inglesby and Dr Mori Hanke at the Center for Health Security at Johns Hopkins University in Baltimore, reinforced the warning, writing: “Although this is promising for life sciences applications, it also raises urgent biosafety and biosecurity questions. The ability to compose viral genomes using generative AI now exists; the governance to safely steer it does not.”
Hie and his colleagues used AI models called Evo1 and Evo2 to design the new viral genomes. The models were trained on genetic data from 2m bacteriophages. The genetic code for viruses that can infect plants, humans or other animals was intentionally excluded from the AI’s training to reduce the risk of it designing dangerous viruses.
The AI generated thousands of potential genomes from which the researchers selected nearly 300 to make in the lab. These were dropped into bacteria, which read the genetic code and churned out the new bacteriophages. The process was not efficient: only 16 bacteriophages proved to be viable, but a cocktail of them swiftly overcame resistance in two different strains ofE. coli.
Bacteriophage genomes are tiny, but Inglesby and Hanke said the work nevertheless proved that generative AI could create functioning viral genomes. Whether the same approach could be applied to other viruses was unknown, but they said work on pathogens that could infect humans, animals or plants should not be pursued.
“Such genomes might encode new pathogens that … cannot be contained by existing countermeasures,” they wrote.
Tom Ellis, professor of synthetic genome engineering at Imperial College London, said the work was impressive, but revealed how hard it would be to make more complex genomes. “This is literally the smallest and easiest genome to make,” he said.
An AI trained on the genetic code of dangerous bugs could be used to design more harmful viruses, Ellis said, but controlling access to genetic data and having restrictions on making genomes that look dangerous would help. “Governments are working hard to do this already,” he added.
“But honestly,” he said, “the threat from full AI design and writing of a genome of a virus or bacteria is very overblown when we consider that just taking existing pathogens and making gain-of-function changes to their genomes is so much easier and much more likely to be a real pathogenic threat.”
Dr Filippa Lentzos, a reader in science and international security at King’s College London, said the most important point to intervene at the moment was when DNA was being manufactured. “It’s important to see the bigger governance picture and not focus regulation solely on the AI model,” she said. “A layered approach makes more sense: safeguards around model development and access, responsible research review, synthesis screening, and established laboratory biosafety and biosecurity.”
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