Recently The New York Times reported the following:
(Source: The NYT)
This A.I. Just Created Viruses Not Found in Nature (1/2)
Scientists trained artificial intelligence on libraries
of DNA and then asked the model to create recipes for viral genomes. Sixteen of
them were viable, yielding new viruses.
The NYT - By Carl Zimmer - Carl Zimmer covers news about
science for The Times and writes the Origins column.
Aug. 6, 2026
For the first time, scientists have used artificial
intelligence to create new kinds of viruses, raising hopes for medical advances
while also raising the disturbing possibility that the technology could someday
be used to invent dangerous pathogens.
Synthesizing viruses from scratch is hardly new. Researchers long ago learned how to manufacture viral genomes; they are used to investigate antiviral drugs and vaccines, as well as to learn how viruses work.
But the new study, published Thursday in the journal Science, goes well beyond duplicating viral genes. Scientists at Stanford University and the Arc Institute, a research organization in Palo Alto, Calif., taught A.I. to recognize patterns of DNA structure in nature, and then to use that data to write recipes for entirely new viruses.
The researchers followed those recipes to create DNA molecules, which they inserted into bacteria. The modified bacteria then produced viruses never seen in nature. The viruses were able to infect other bacteria, demonstrating that they were viable.
“This is an important milestone,” said Patrick Cai, a synthetic biologist at the University of Manchester, who was not involved in the study.
The viruses dreamed up by A.I. do not pose a threat to humans, because they are all similar to a naturally occurring virus called Phi X-174, which can infect only bacteria.
But the new study adds to growing worries that artificial intelligence might enable the creation of a new generation of biological weapons, from deadly poisons to unstoppable pandemics.
Dr. Moritz Hanke, a fellow at the Johns Hopkins Center for Health Security who was not involved in the new study, said governments and scientific organizations have been slow to develop guardrails that could block the creation of a deadly virus — even as the science races ahead.
“There’s just a huge disconnect,” he said.
The authors of the study relied on an A.I. model called Evo, which is similar in some ways to ChatGPT, made by OpenAI.
ChatGPT answers questions by stringing together words that seem likely to follow one another. It can do this thanks to years of training on vast amounts of text gathered from the internet, books and other sources.
DNA is strikingly similar to a book in some ways: a string of molecular building blocks, known as nucleotides, arrayed like letters in a line of text. A gene consists of hundreds of nucleotides drawn from a four-letter alphabet: A, C, G and T. The sequence encodes the instructions for building proteins and other molecules.
DNA has its own rules of grammar, and if a sequence violates them, the result is biological gibberish. Biologists have uncovered some of nature’s grammatical rules, but many remain a mystery.
The researchers wondered if Evo could pick up these rules on its own. Instead of training it on text, they trained it on genetic sequences drawn from millions of animals, plants, microbes and viruses. All told, Evo scanned about nine trillion nucleotides.
Evo eventually recognized patterns common across the tree of life and used them to generate blueprints for new genes encoding proteins that could perform specific jobs. These results led the team to wonder if Evo could master not just single genes but also an entire genome.
As the A.I. would be able to handle only small genomes at first, the scientists decided to try to make viruses. While a human genome contains over three billion nucleotides, many viruses have genomes just a few thousand nucleotides long.
“It just felt like the obvious next step,” said Samuel King, a graduate student at Stanford University and an author of the new study. He and his colleagues gave Evo another round of training, this time on the 11 genes of Phi X-174 and about 15,000 of its closest relatives.
(to be continued)
Translation
人工智能剛剛創造出自然界中不存在的病毒（1/2）
科學家利用DNA庫訓練人工智能，然後讓模型創建病毒基因組的合成方案。其中16個方案成功，產生了新的病毒
科學家首次利用人工智能創造了新型病毒，為醫學進步帶來了希望，同時也引發了一個令人不安的可能性：這項技術未來可能被用於製造危險的病原體。
從零開始合成病毒並非新鮮事。研究人員很久以前就掌握瞭如何製造病毒基因組；他們利用病毒基因組來研究抗病毒藥物和疫苗，以及了解病毒的運作機制。
但這項週四發表在《科學》雜誌上的新研究，遠遠超出了複製病毒基因的範疇。史丹佛大學和位於加州Palo Alto的Arc研究所的科學家訓練人工智能辨識自然界中DNA結構的模式，並利用這些資料編寫全新病毒的「配方」。
研究人員依照這些「配方」製造DNA分子，並將其插入細菌體內。改造後的細菌隨後產生了自然界中從未出現過的病毒。這些病毒能夠感染其他細菌，證明它們是可行的。
並未參與這項研究的曼徹斯特大學的合成生物學家Patrick Cai說道:「這是一個重要的里程碑」。
人工智能製造的這些病毒不會對人類構成威脅，因為它們都與一種名為Phi X-174的天然病毒相似，而Phi X-174只能感染細菌。
但這項新研究加劇了人們日益增長的擔憂：人工智能可能被用於製造新一代生物武器，從致命毒藥到無法控制的流行病。
約翰霍普金斯大學健康安全中心的研究員Moritz
Hanke博士並未參與這項新研究。他表示，儘管科學發展日新月異，但各國政府和科研機構在制定出能夠阻止致命病毒產生的防止措施方面卻進展緩慢。
他說:「這之間存在著巨大的脫節」。
研究的作者使用了名為Evo的人工智能模型，它在某些方面與OpenAI開發的ChatGPT類似。
ChatGPT透過將看似可能連續出現的字詞串連起來去回答問題。它之所以能夠做到這一點，是因為它經過多年對從互聯網、書籍和其他來源收集的大量文本進行訓練。
DNA在某些方面與書籍驚人地相似：它們都是由被稱為核苷酸 (nucleotides) 的分子構件組成的，排列方式如同文本中的字母。基因由數百個核苷酸組成，這些核苷酸取自字母表：A、C、G 和 T。此序列編碼構成對蛋白質和其他分子的指令。
DNA 有其自身的語法規則，如果序列違反這些規則，就會產生生物亂碼。生物學家已經揭示了一些自然界的語法規則，但許多規則仍然是個謎。
研究人員想知道 Evo 是否能夠自主學習這些規則。他們沒有用文字訓練 Evo，而是用來自數百萬種動物、植物、微生物和病毒的基因序列來訓練。 Evo 總共掃描了大約 9 兆個核苷酸。
Evo 最終識別出了生命之樹中常見的模式，並利用這些模式產生了編碼能夠執行特定功能的蛋白質的新基因藍圖。這些結果讓研究團隊開始思考，Evo 是否不僅能掌握單一基因，還能掌握整個基因組。
由於人工智能最初只能處理小型基因組，科學家決定嘗試建構病毒。人類基因組包含超過30億個核苷酸，而許多病毒的基因組只有數千個核苷酸長。
史丹佛大學研究生、這項新研究的作者之一 Samuel
King 說:「這感覺就像是順理成章的下一步」。他和同事們對Evo進行了新一輪的訓練，這次對象是Phi
X-174的11個基因以及大約15,000個與其親緣關係最近的基因。
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