The Evolution Revolution: Scouting Frontiers in AI for Biology with Brian Hie

**SPEAKER_2** (0:02)
In case you missed it, last week we aired an exceptional series with four big picture thinking VCs. And no, it's not the podcast you're thinking of. Check out This Won't Last with Keith Rabois, Logan Bartlett, Zach Weinberg, and Kevin Ryan. You'll be able to sit in on their monthly back channel as they volley predictions about the future of tech, business, and the venture markets. They're only releasing these conversations for a limited time. So check out episode one and subscribe at the link in the description.

**Nathan Labenz** (0:29)
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 Labenz, joined by my co-host, Erik Torenberg. Hello and welcome back to The Cognitive revolution. Today I'm speaking with Brian Hie, 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, aka microskills, 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 discuss 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 discuss 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 25 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. If you have any questions or feedback, feel free to reach out either via our website, cognitiverevolution.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 Hie. Brian Hie, Assistant Professor at Stanford and innovation Investigator at Arc Institute, welcome to The Cognitive revolution.

**Brian Hie** (3:59)
It's great to be here. Thanks, Nathan.

**Nathan Labenz** (4:02)
I'm excited for this. You have been a part of some really remarkable research publications recently, a couple that I felt as I encountered them came from different parts of the world, but you were involved with both. I think it's going to be a really interesting conversation to understand those projects more deeply and also to try to make some connections between them. I wanted to maybe just start off with really big picture survey of the landscape of AI for science and maybe more specifically AI for biology. I'll just float my understanding at you and you can critique it, tell me what I'm missing or how you would characterize things differently. I'm not a biologist, so I'm probably going to get some stuff wrong.