🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)
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Why is there an arms‑race between AI‑driven bio‑design and bio‑defense?
The design side's gonna get more capable. The defensive side needs to try its best to get ahead. So I think inherently there is this arms race style dynamic that the defensive side has been far, far lagging. And so what we want to do is bring the defensive side to par essentially. We felt it was important as a lab that a team that was both building the design capabilities is actually also best suited for building. Building the defense capabilities, because they're basically the same models. A model that is good at generating turns out is also very good at discriminating or predicting if a sequence is pathogenic or not. For us, we as a company thought it was very important to have a dual mandate. It's this idea of essentially being cognizant and feeling responsible for the capabilities that we're enabling on the design side.
So if we're going to create models like Can design function into sequences. We believe and see a gap in companies being able to safeguard that technology.
Welcome to Lane Space. I'm Brandon. I build RNA therapeutics at Atomic AI. I'm joined by my co-host RJ Honeke, uh CTO and co-founder of Mira Omics. Today it's a pleasure to have with us Eric Gwynn at um CEO and co-founder of radical numerics. Eric uh started got his PhD in Chris Ray's group. Uh he spent a lot of time thinking about how to do long context genomic models before. uh long context or genomic models were cool. He was the, you know, first author and I think, you know, basically visionary behind the Evo generative model, one of the first generative genomics platforms, uh developed Evo two, which, you know, naturally led into um, you know, radical numerics. Uh yeah. Thank you for being here. Did I miss anything?
That sounds great. Yeah. Cool. Welcome. Thank you. So, Eric, let's talk about uh Omni and the blog posts that you guys did about the benchmarking. But I want to hear first, okay, what is a genetic language model? Why do I care? What does it do? And then let's talk about the top line results from the the blog post.
So uh a a genome language model or GLM is a large language model trained on DNA sequences. So very much like natural language and chatbots you see, but not trained on words or natural language, but on the raw fabric of life, which is these sequence of letters that make up DNA. And we ourselves, our our company and our team is known for creating the first uh generative uh genomics models, which are models trained on DNA, not just to read, but also write, meaning able to generate new sequences of DNA. And we felt this was a an area that was overlooked and that if AI could read and write DNA, it could change a lot. Under scientific discovery and understanding of human health and how to treat it. And so we felt that it was a
a big opportunity to train AI on on the genome.
What
kind of things can
you
potentially do with with a model like this? Great to start for us when we first started working on DNA models. We worked on this model called hyena DNA, which is a large language model, but it used a convolution instead of a tension. So a little more technical details. Um DNA has this property that Well, it's very long, right? Um at the time These large language models had limited constraints on uh context, right? Being able to f fit long sequences. And so we were looking for in a more efficient algorithm to be able to handle something like DNA. And so we came up with this what we call the hyena operator that uses convolutions. Long story short, it let us process longer sequences, in this case, up to a million, and at the time was the largest context for a language model.
And what you what we did with it was essentially used it to read DNA, predict function. So given a sequence of DNA, string of characters, um, we predict its regulatory function, its effect on a genome. And you know, this is interesting to scientists because a lot of the DNA in our bodies um, you know, perhaps people are less aware, but Actually, we don't know a lot that much about our genome. It's we know it obviously it encodes the information for making us us and how you know all the different complexities and potential diseases.
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