The Evolution Revolution: Scouting Frontiers in AI for Biology with Brian Hie
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What big‑picture challenges does AI face in biology and drug discovery?
Hello and welcome to the Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week we'll explore their revolutionary ideas, and together we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan LeBenz, joined by my co-host, Eric Tornberg. Hello and welcome back to the Cognitive Revolution. Today I'm speaking with Brian He, an assistant professor at Stanford and innovation investigator at Arc Institute, who is at the forefront of applying AI to biology. We begin with a discussion of biology's grand challenges, understanding the causal web of interactions in biological systems, and designing interventions which are both effective and narrowly targeted.
Brian offers his perspective on our progress in these areas and insights into how AI is already beginning to change the game. From there we discuss three of Brian's recent papers, each representing a different facet of his work. We start with mechanistic design and scaling of hybrid architectures, a paper that describes a semi automated way to build novel machine learning architectures from primitives, including the attention and state space mechanisms. This paper shows that small model performance on toy problems is highly predictive of their performance at much larger scale. And while this paper is very much focused on machine learning techniques, the insights that they derive are useful for the long sequence challenges of biology.
Moreover, the techniques themselves suggest that a sort of directed evolution, applied to machine learning, is likely to create a Cambrian explosion of AI architectures. From there, we move on to Evo, a hybrid, attention and state space language model that demonstrates surprising emergent capabilities, understanding higher level biological concepts despite being trained solely on DNA sequences. We discussed the model's ability to identify which genes are critical to an organism's survival and also to generate novel CRISPR variants, as well as the precautions that Brian and collaborators took when developing this model, and Brian's overall perspective on the biosecurity landscape. Finally, we discussed Brian's work on using AI to guide the evolution of antibody complexes.
In another result that demonstrates AI's remarkable ability to generalize out of distribution, Brian and his team have been able to use a model that was trained with three dimensional structure data representing single proteins to create antibodies that can be up to twenty five times better at binding to their targets than their natural counterparts, with obvious implications for drug discovery and development. Along the way, we touch on broader themes in AI for biology, including the challenges of interpreting models that are trained on data types which humans did not create and really only partially understand. The compute requirements for cutting edge research in AI for biology, and the need to account for evolutionary dynamism in AI system design.
As always, if you're finding value in the show, we'd appreciate it if you'd take a moment to share it with friends, write a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. And if you have any questions or feedback, feel free to reach out, either via our website, cognitive revolution.ai, or by DMing me on your favorite social network. Now, I hope you enjoy this survey of important frontiers in the application of AI to biology with Professor Brian He. Brian He, assistant professor at Stanford and innovation investigator at Arc Institute. Welcome to the Cognitive Revolution.
It's great to be here. Thanks, Nathan.
I'm excited for this. You have been uh a part of some really remarkable research publications recently, uh a couple that I I kind of felt as I encountered them came from like different parts of the world. But you were involved with both. So I think it's gonna be a really interesting conversation to understand those projects more deeply and also to try to make some connections between them.
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Chapters
8 chapters
1
What big‑picture challenges does AI face in biology and drug discovery?
0:00–9:46
2
How does the Mechanistic Design and Scaling of Hybrid Architectures paper explore new AI building blocks?
9:46–19:55
3
What is Scanorama and how does it help integrate single‑cell transcriptomics data?
19:55–28:18
4
How does the Evo DNA language model predict gene essentiality in bacteria and phages?
28:18–37:00
5
Why are more CRISPR variants needed and how can Evo generate them?
37:00–45:27
6
How does the structure‑informed language model evolve antibody‑antigen complexes for higher binding affinity?
45:27–53:57
7
What safety and biosecurity measures are being taken when training generative biology models?
53:57–1:03:08
8
What are the future directions and open problems for AI‑driven biology research?
1:03:08–1:14:40
Speakers
2 identifiedMore from "The Cognitive Revolution"
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