Cindy Vindman, Benjamin Trump, Christopher Cummings, Madison Smith, Alexander J. Titus, Ken Oye, Valentina Prado, Eyup Turmus, Igor Linkov
{"title":"人工智能与合成生物学的融合:迫在眉睫的大洪水","authors":"Cindy Vindman, Benjamin Trump, Christopher Cummings, Madison Smith, Alexander J. Titus, Ken Oye, Valentina Prado, Eyup Turmus, Igor Linkov","doi":"arxiv-2404.18973","DOIUrl":null,"url":null,"abstract":"The convergence of artificial intelligence (AI) and synthetic biology is\nrapidly accelerating the pace of biological discovery and engineering. AI\ntechniques, such as large language models and biological design tools, are\nenabling the automated design, build, test, and learning cycles for engineered\nbiological systems. This convergence promises to democratize synthetic biology\nand unlock novel applications across domains from medicine to environmental\nsustainability. However, it also poses significant risks around reliability,\ndual use, and governance. The opacity of AI models, the deskilling of\nworkforces, and the outdated nature of current regulatory frameworks present\nchallenges in ensuring responsible development. Urgent attention is needed to\nupdate governance structures, integrate human oversight into increasingly\nautomated workflows, and foster a culture of responsibility among the growing\ncommunity of bioengineers. Only by proactively addressing these issues can we\nrealize the transformative potential of AI-driven synthetic biology while\nmitigating its risks.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"88 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Convergence of AI and Synthetic Biology: The Looming Deluge\",\"authors\":\"Cindy Vindman, Benjamin Trump, Christopher Cummings, Madison Smith, Alexander J. Titus, Ken Oye, Valentina Prado, Eyup Turmus, Igor Linkov\",\"doi\":\"arxiv-2404.18973\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The convergence of artificial intelligence (AI) and synthetic biology is\\nrapidly accelerating the pace of biological discovery and engineering. AI\\ntechniques, such as large language models and biological design tools, are\\nenabling the automated design, build, test, and learning cycles for engineered\\nbiological systems. This convergence promises to democratize synthetic biology\\nand unlock novel applications across domains from medicine to environmental\\nsustainability. However, it also poses significant risks around reliability,\\ndual use, and governance. The opacity of AI models, the deskilling of\\nworkforces, and the outdated nature of current regulatory frameworks present\\nchallenges in ensuring responsible development. Urgent attention is needed to\\nupdate governance structures, integrate human oversight into increasingly\\nautomated workflows, and foster a culture of responsibility among the growing\\ncommunity of bioengineers. Only by proactively addressing these issues can we\\nrealize the transformative potential of AI-driven synthetic biology while\\nmitigating its risks.\",\"PeriodicalId\":501219,\"journal\":{\"name\":\"arXiv - QuanBio - Other Quantitative Biology\",\"volume\":\"88 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Other Quantitative Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.18973\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.18973","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Convergence of AI and Synthetic Biology: The Looming Deluge
The convergence of artificial intelligence (AI) and synthetic biology is
rapidly accelerating the pace of biological discovery and engineering. AI
techniques, such as large language models and biological design tools, are
enabling the automated design, build, test, and learning cycles for engineered
biological systems. This convergence promises to democratize synthetic biology
and unlock novel applications across domains from medicine to environmental
sustainability. However, it also poses significant risks around reliability,
dual use, and governance. The opacity of AI models, the deskilling of
workforces, and the outdated nature of current regulatory frameworks present
challenges in ensuring responsible development. Urgent attention is needed to
update governance structures, integrate human oversight into increasingly
automated workflows, and foster a culture of responsibility among the growing
community of bioengineers. Only by proactively addressing these issues can we
realize the transformative potential of AI-driven synthetic biology while
mitigating its risks.