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

Eric Nguyen (0:00)

The design side is going to get more capable. The defensive side needs to try 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 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 that can design function into sequences, we believe and see a gap in companies being able to safeguard that technology.

Brandon (1:00)

Welcome to Latent Space. I'm Brandon. I build RNA Therapeutics at Atomic AI. I'm joined by my co-host, RJ Honecky, CTO and co-founder of Miraomics. Today, it's a pleasure to have with us Eric Nguyen, CEO and co-founder of Radical Numerics. Eric got his PhD in Chris Ray's group. He spent a lot of time thinking about how to do long-context genomic models before long-context or genomic models were cool.

He was the first author and I think basically visionary behind the Evo generative model, one of the first generative genomics platforms developed Evo 2, which naturally led into Radical Numerics.

Thank you for being here. Did I miss anything?

Eric Nguyen (1:46)

That sounds great.

Brandon (1:48)

Welcome.

Eric Nguyen (1:49)

Thank you. Eric, let's talk about Omni and the blog post that you guys did about the benchmarking. But I want to hear first, okay, what is a genetic language model?

Why do I care?

Brandon (2:03)

What does it do?

Eric Nguyen (2:04)

Then let's talk about the top line results from the blog post.

A genome language model or GLM is a large language model trained on DNA sequences. 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 the sequence of letters that make up DNA.

Our company, our team is known for creating the first generative genomics models, which are models trained on DNA, not just to read, but also write, meaning able to generate new sequences of DNA. We felt this was an area that was overlooked, and that if AI could read and write DNA, it could change a lot.

Science of discovery and understanding of human health and how to treat it.

So we felt that it was a big opportunity to train AI on the genome. What kind of things can you potentially do 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.

DNA has this property that, well, it's very long, right? At the time, these large language models had limited constraints on context, right? Being able to fit long sequences. And so we were looking for 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, uses convolutions. Long story short, it let us process longer sequences, in this case up to a million, at the time was the largest context for a language model. And what we did with it was essentially used it to read DNA, predict function. So given a sequence of DNA, string of characters, we predict its regulatory function, its effect on a genome. And this is interesting to scientists because a lot of the DNA in our bodies, 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 and help all the different complexities and potential diseases. At the same time, the grammar rules about how the combination of those letters are formed, what they encode and how they encode function and traits. It's not fully understood. And so the hope was using these DNA models, language models, to be able to map some function from the raw DNA sequence. And so our first generation models was able to show that, yes, we can train AI to be able to read and understand to some degree DNA sequences, and especially what we called the longer range interactions, meaning over sequences, if you use chatbots, for example, if you've heard the phrase context rot, the longer the input you put into a language model, it starts to deteriorate. And so being able to pick up long range information and sort of patterns, motifs, grammar over long sequences was what we were trying to accomplish.