Eric Nguyen

This account is verified. Learn more

@exnx

Co-founder & CEO @RadicalNumerics

San Francisco, CA radicalnumerics.ai Joined April 2009

780 Following 6K Followers 636 Posts

Latest posts 5

Pinned post

Eric Nguyen @exnx Oct 20

Introducing Omnii-1, our most powerful genome language model, and a major step toward general biological intelligence

In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use

2:35

Eric Nguyen @exnx 21h

I wanted to share some context around the recent discussion of our phage work.

A few thoughts on the collaboration and the path forward.

Blog: radicalnumerics.ai/blog/phage-col…

www.radicalnumerics.ai A note on our phage collaboration — Radical Numerics Eric Nguyen on the recent discussion around Radical Numerics, Tabula Bioscience, and George Church, and on recognizing the collaboration behind AI-designed phages reaching a patient.

Eric Nguyen @exnx 5d

Designing phages with AI is one challenge. Getting it to a patient is another.

Behind every treatment is a network of scientists, physicians, manufacturers, and partners working together.

Our new blog explores the journey from AI-generated DNA to patient care, and what it will take to scale it.

Blog: radicalnumerics.ai/blog/phage-des…

Eric Nguyen @exnx Nov 2

A few years ago, we were just a group of researchers chasing unconventional bets in AI.

That journey took us from new architectures like Hyena, to Evo and creating the field of generative genomics, to a much bigger realization.

We need AI that can understand biology as a whole. We started @RadicalNumerics to pursue that vision.

Here’s a conversation with my cofounders on how we got here, why we took the leap, and what we believe comes next.

17:26

Eric Nguyen @exnx Oct 29

Really cool to see the in-context learning and chain of thought paradigm making it's way to genome language models more and more. Exciting work by @garykbrixi

Garyk Brixi Verified account @garykbrixi Oct 29

Language models can learn new tasks from just a few examples in their prompt, with no retraining. This is called in-context learning (ICL). Now genome language models can do it too.

With a handful of examples, Evo 2 ICL generates proteins, de novo antibodies, CRISPR-Cas9 loci, mitochondrial genomes and entire bacteriophages.

We can even use the synthetic sequences iteratively to construct a chain-of-thought (CoT) and achieve harder design goals.

New work with Yukun A. Hao, @BrianHie and team 🧵

View quoted post on X