原始对话

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

Latent.Space · 2026年9月23日 · 1:31:59

Eric Nguyen discusses unannotated genomic data, comparisons with ensemble methods, and the limits of results awaiting wet-lab validation.

Eric Nguyen

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Latent.Space

一目了然

Unannotated genomic data favors unsupervised learning[1]阅读支持性片段
Ensemble comparisons require more than one favorable result[2]阅读支持性片段
Promising computational results still need wet-lab validation[3]阅读支持性片段

关键时刻3

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25:53

AI research

Unannotated genomic data favors unsupervised learning

原始摘录

there's a lot more data, a lot more genomic data that's not annotated. Actually, most of it, pretty much in many ways, almost all of it is not annotated. And so being able to learn from an unsupervised manner, hugely desirable

Eric Nguyen·2026年9月23日

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32:39

AI research

Ensemble comparisons require more than one favorable result

原始摘录

they'll take the best methods and kind of do Do an ensemble, right? So they'll take up another, even if the best method is another previous model, they'll mix it with like an SVM and just like throw the kitchen sink at it. And so you can see why it would be the best, right? And so that was the bar for us. We're like, if they're going to throw the kitchen sink at it, like we're not going to cherry pick one model and say we're better than that.

Eric Nguyen·2026年9月23日

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36:30

AI research

Promising computational results still need wet-lab validation

原始摘录

We were actually in the process of validating the wet lab right now. So we didn't get to show it here, but we wanted to know, right? Actually, can it not just do this in silico, which it can, it showcased that it was able to continue And now we think this is a, you know, obviously, if this works in the lab, we think this is a hugely, hugely valuable paradigm

Eric Nguyen·2026年9月23日

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