#93 – Daphne Koller: Biomedicine and Machine Learning

**Lex Fridman** (0:00)
The following is a conversation with Daphne Koller, a professor of computer science at Stanford University, a co-founder of Coursera with Andrew Ng, and founder and CEO of Insitro, a company at the intersection of machine learning and biomedicine. We're now in the exciting early days of using the data-driven methods of machine learning to help discover and develop new drugs and treatments at scale. Daphne and Insitro are leading the way on this with breakthroughs that may ripple through all fields of medicine, including one's most critical for helping with the current coronavirus pandemic. This conversation was recorded before the COVID-19 outbreak. For everyone feeling the medical, psychological, and financial burden of this crisis, I'm sending love your way. Stay strong. We're in this together. We'll beat this thing. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube. Review it with five stars on Apple Podcasts. Support it on Patreon or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N. As usual, I'll do a few minutes of ads now and never any ads in the middle that can break the flow of this conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the app store. When you get it, use code LexPodcast. Cash App lets you send money to friends, buy Bitcoin and invest in the stock market with as little as $1. Since Cash App allows you to send and receive money digitally, peer-to-peer, and security in all digital transactions is very important, let me mention the PCI data security standard that Cash App is compliant with. I'm a big fan of standards for safety and security. PCI DSS is a good example of that, where a bunch of competitors got together and agreed that there needs to be a global standard around the security of transactions. Now we just need to do the same for autonomous vehicles and AI systems in general. So again, if you get Cash App from the App Store Google Play and use the code LexPodcast, you get $10 and Cash App will also donate $10 to First, an organization that is helping to advance robotics and STEM education for young people around the world. And now here's my conversation with Daphne Koller.
So, you co-founded Coursera, and made a huge impact in the global education of AI, and after five years, in august 2016, wrote a blog post saying that you're stepping away, and wrote, quote, It is time for me to turn to another critical challenge, the development of machine learning and its applications to improving human health. So, let me ask two far out philosophical questions. One, do you think we will one day find cures for all major diseases known today? And two, do you think we will one day figure out a way to extend the human lifespan, perhaps to the point of immortality?

**Daphne Koller** (3:16)
So, one day is a very long time, and I don't like to make predictions of the type we will never be able to do X, because I think that's a, you know, that's smacks of hubris. It seems that never in the entire eternity of human existence will we be able to solve a problem. That being said, curing disease is very hard because oftentimes, by the time you discover the disease, a lot of damage has already been done. And so, to assume that we would be able to cure disease at that stage, assumes that we would come up with ways of basically regenerating entire parts of the human body in the way that actually returns it to its original state. And that's a very challenging problem. We have cured very few diseases. We've been able to provide treatment for an increasingly large number. But the number of things that you could actually define to be cures is actually not that large.
So, I think that there's a lot of work that would need to happen before one could legitimately say that we have cured even a reasonable number, far less all diseases.

**Lex Fridman** (4:27)
On a scale of zero to 100, where are we in understanding the fundamental mechanisms of major diseases? What's your sense? So, from the computer science perspective that you've entered the world of health, how far along are we?

**Daphne Koller** (4:43)
I think it depends on which disease. I mean, there are ones where I would say we're maybe not quite at 100 because biology is really complicated and there's always new things that we uncover that people didn't even realize existed. So, but I would say there's diseases where we might be in the 70s or 80s and then there's diseases in which I would say, probably the majority where we're really close to zero.