AI Got Good at Language. Now It’s Learning the Language of L

【文章标题】：AI Got Good at Language. Now It’s Learning the Language of Life. (Eric Nguyen, Co-Founder and CEO of Radical Numerics)

【文章标题】：AI已精通语言，如今正在学习生命的语言。（埃里克·阮，Radical Numerics联合创始人兼首席执行官）

【文章正文】： “The potential to create or manipulate life with AI could reinvent nearly all of biology, but it also carries inherent risk. We’ve seen just a taste of this on the natural-language and chatbot side. Giving AI the power to generate and create life carries a certain level of responsibility.”

【文章正文】： “利用AI创造或操控生命的潜力，可能彻底重塑几乎所有生物学领域，但同时也伴随着固有的风险。我们在自然语言和聊天机器人方面已经尝到了些许滋味。赋予AI生成和创造生命的能力，意味着承担一定程度的责任。”

— Eric Nguyen, Co-founder and CEO, Radical Numerics

——埃里克·阮，Radical Numerics联合创始人兼首席执行官

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Eric Nguyen

埃里克·阮

is the co-founder and CEO of

是

Radical Numerics

, an AI research lab that has raised $50 million to train models directly on biological data. Before starting the company, Eric helped develop Evo and Evo 2, large-scale genome language models trained on unlabeled DNA sequences. Radical Numerics is now building models that can connect information across DNA, RNA, proteins, epigenetics, and other parts of biology, rather than treating each as a separate problem. Researchers have already used Evo to generate viable bacteriophage genomes, and Eric says Radical Numerics’ newer model, Omnii, matched key findings from two years of Alzheimer’s wet-lab research in a matter of days. He also believes these tools could make it easier to create dangerous pathogens, which is why the company is working on both biological design and biodefense.

的联合创始人兼首席执行官。这是一家AI研究实验室，已筹集5000万美元，用于直接在生物数据上训练模型。在公司创立之前，埃里克参与开发了Evo和Evo 2——基于未标记DNA序列训练的大规模基因组语言模型。Radical Numerics目前正在构建能够连接DNA、RNA、蛋白质、表观遗传学及其他生物学领域信息的模型，而不是将每个领域视为独立问题。研究人员已经利用Evo生成了具有活力的噬菌体基因组，埃里克表示，Radical Numerics的新模型Omnii仅用数天时间就匹配了阿尔茨海默病湿实验室研究两年的关键发现。他还认为，这些工具可能让制造危险病原体变得更加容易，这正是该公司同时致力于生物设计和生物防御的原因。

In our conversation, we explore:

在我们的对话中，我们探讨了以下内容：

What AI models can learn by treating DNA as a language

AI模型将DNA视为一种语言时能学到什么

Why reading scientific papers is not the same as learning directly from biological data

为什么阅读科学论文与直接从生物数据中学习是不同的

How Eric’s unusually free-range childhood shaped the way he follows his curiosity

埃里克非同寻常的自由放养式童年如何塑造了他追随好奇心的方式

Why biology may have more useful data than researchers know how to use

为什么生物学可能拥有比研究人员已知如何利用的更多有用数据

How Radical Numerics plans to connect information across DNA, RNA, proteins, and other biological systems

Radical Numerics计划如何连接DNA、RNA、蛋白质及其他生物系统中的信息

Where the company sees early opportunities in drug discovery, diagnostics, synthetic biology, and biodefense

该公司在药物发现、诊断、合成生物学和生物防御领域看到了哪些早期机遇

Why testing AI-generated biology in the lab is still slow and difficult

为什么在实验室中测试AI生成的生物学产物仍然缓慢且困难

How models that design biological systems could also help detect dangerous or manipulated pathogens

设计生物系统的模型如何也能帮助检测危险或被操纵的病原体

How to make powerful biology models safer without eliminating the capabilities that make them valuable

如何在不消除强大生物学模型价值所在的能力的前提下，使其更加安全

Thank you to the partners who make this possible

感谢让这一切成为可能的合作伙伴

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