AI Revolution with Daphne Koller

**SPEAKER_1** (0:00)
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**SPEAKER_2** (0:46)
Hi, Das here, the lead editor on the a16z growth team with a quick bit of context. The conversation that follows is part of a series we recorded last month at our AI Revolution event. This series features some of the most impactful builders in the field of AI, from those who are scaling up big foundation models to those developing products that could transform entire industries.
In the conversations, they discuss and debate where we are, where we're going in the big open questions in AI. For the full series, subscribe to a16z live or visit a16z.com/airevolution.
Before we jump into the conversation, please note that the content here is for informational purposes only, should not be taken as legal business tax or investment advice or be used to evaluate any investment or security and is not directed at any investors or potential investors in any a16z fund. For more details, please see a16z.com/disclosures. And now let's get into it.

**Vijay Pande** (1:46)
Daphne is like the OG's OG in AI. She was a pioneer at Stanford in different areas of AI, especially in PGMs. She left Stanford to co-found Coursera with Andrew Ng and actually is now the founder, CEO of Insitro, a tech biocompany using AI to develop drugs in life sciences.
So Daphne, given all the things you could be doing, like life sciences.

**Daphne Koller** (2:15)
It's one of the really hard and really important problems. And there's very few things that are as challenging, as exciting as intervening in a safe and effective way in human health. And so it's just a thing that absolutely needs to be done if we are going to use AI for good, which I think is one of the things that I think I at least really strive to do.
The second part of the answer is why now? And what brought me back to this field back in 2016 post Coursera was the realization that we can now finally, for the first time, measure biology at scale, both at the cellular level, sometimes at subcellular level, and at the organism level via ways of quantitating human biology. And that gives us for the very first time, the ability to deploy machine learning in ways where it is truly meaningful to do that because the data sets are large enough for really interesting machine learning methods to be deployed. And that's something I spent a lot of time thinking about to your point, Vijay, because at this point, AI people can do pretty much anything.
And I am a big believer in leverage. That is, places where you can have a disproportionately large impact. And because of the fact that I had spent a large part of my Stanford career working in these two spaces simultaneously, core machine learning on the one hand and machine learning and service of biomedical data on the other, I actually have the ability to sort of bridge the chasm between these two very disparate disciplines. And when I was leaving Coursera in 2016 and I looked around me and I saw, even at that time, which of course is tiny compared to where we are today, that machine learning was changing the world. It wasn't having much of an impact in the life sciences. And I believe one of the main reasons for that is because there's so very few people who actually have the language of both disciplines and are able to bring them together. So I felt like I could have impact in AI across many things, but sure I could have disproportionate impact.

**Vijay Pande** (4:28)
Well, you spoke about the why now.
What's your take on AI for life sciences? What's the why now there? And is there, what's different now than even what we could do even just let's say five years ago?

**Daphne Koller** (4:39)
So I think it comes back to this ability to collect, but even more than collect and generate data at scale. So one of the things that we have at Incitro that is truly unique is we have a data factory. We have put together the tools that have been developed by people who are taking pluripotent stem cells, which are cells from you or me or anyone in this audience and turning them into this pluripotent status, which can make a Daphne neuron in a dish or a Daphne hepatocyte in a dish is gonna be different than the Vijay neuron in the Vijay hepatocyte because we have different genetics and that's gonna manifest in how these cells behave and how these cells look in different measurements.