Daphne Koller: The Convergence of A.I. and Digital Biology

**Eric Topol** (0:06)
Hello, this is Eric Topol with Ground Truths, and I am absolutely thrilled to welcome Daphne Koller, the founder and CEO of In-Cetro, and a person who I've been wanting to meet for some time. Finally, we converged, so welcome, Daphne.

**Daphne Koller** (0:21)
Thank you, Eric, and it's a pleasure to finally meet you as well.

**Eric Topol** (0:24)
Yeah, I mean, you have been rocking it over the years with elected to National Academy of Engineering and Science and right at the interface of life science and computer science and in my view, there's hardly anyone I can imagine who's doing so much at that interface. So I wanted to first start with your meeting in Davos last month because I kind of figured we start broad AI rather than starting to get into what you're doing these days. And you had a really interesting panel with Yann LeCun and Andrew Ng and Kai-Fu Lee and others. And I wanted to get your impression about that and also kind of the general sense. I mean, AI is just moving at a speed that is just crazy stuff.
What were your thoughts about that panel and just last month, where are we?

**Daphne Koller** (1:24)
I think we've been living on an exponential curve for multiple decades. And the thing about exponential curves is they are very misleading things. And in the early stages, people basically take the line between whatever we were last year and this year, and they interpolate linearly and they say, God, things are moving so slowly. And then as the exponential curve starts to pick up, it becomes more and more evident that, you know, things are moving faster, but it's still people interpolate linearly and it's only when things kind of really hit that inflection point that people realize that even with the linear interpolation, where we'll be next year is just mind blowing. And if you realize that you're on that exponential curve, where we will be next year is just totally unanticipatable. And I think what we started to discuss in that panel was are we in fact on an exponential curve?
What are the rate limiting factors that may or may not enable that curve to continue, specifically availability of data and what it would take to make that curve available in areas outside of the speech, whatever natural language, large language models that exist today and go far beyond that, which is what you would need to have these be applicable to areas such as biology and medicine. And so that was kind of the message to my mind from the panel.

**Eric Topol** (2:52)
Yeah, and it was, there was some differences in opinion, of course. Yann can be a little strong and I think it was good to see that you challenged it on some things and how there are this worldview of AI and how, I guess, where we go from here.
As you mentioned, in the area of life science, there already had been before large language models hit stride, so much progress, particularly in imaging cells, subcellular, I mean, rare cells, I mean, just stuff that was just, you know, without any labeling, without fluorescing, and I mean, just amazing stuff. And then now it's gone into another level. So as we get into that, just before I do that, I wanna ask you about this convergence story.
You know, Jensen Huang, I'm sure you heard his quote about biology as the opportunity to be engineering, not science. I'm not sure if I understand not science, but what about this convergence? Because it is quite extraordinary to see two fields coming together, moving at such high velocity.

**Daphne Koller** (4:08)
So a quote that I will replace Jensen's, or will propose a replacement for Jensen's quote, which is one that many people have articulated, is that math is to physics as machine learning is to biology. It is a mathematical foundation that allows you to take something that up until that point has been kind of mysterious and fuzzy and almost magical and create a formal foundation for it. Now physics, especially Newtonian physics, is simple enough that math is the right foundation to capture what goes on in a lot of physics.
Biology as an evolved natural system is so complex that you can't articulate a mathematical model for that de novo. You need to actually let the data speak and then let machine learning find the patterns in those data and really help us create a predictability, if you will, for biological systems so that you can start to ask, what if questions? What would happen if we perturb the system in this way? How would it react? We're nowhere close to being able to answer those questions reliably today, but as you feed a machine learning system more and more data, hopefully it'll become capable of making those predictions. And in order to do that, and this is where it comes to this convergence of these two disciplines, the fodder, the foundation for all of machine learning is having enough data to feed the beast.