John Jumper: AlphaFold and the Future of Science

**John Jumper** (0:00)
This is something of a nice change. I've given a lot of scientific talks, and no one claps and cheers when I come on. Not normally even when I come on.
It's really exciting, it's really wonderful to be here. I guess I should start off assuming that not everyone in this cavernous hall knows who I am. Who am I? I'm someone who has done some work in AI for science, who really believes that we can use the AI systems, these technologies, these ideas, to change the world in a very specific way, to make science go faster, to enable new discoveries. I think it's really, really wonderful. We have the opportunity to take these tools, these ideas, and aim them toward the question of how can we build the right AI systems so that sick people can become healthy and go home from the hospital. It's been a really wonderful and winding journey for me to end up here. I was originally trained as a physicist. I thought I was going to be a laws of the universe physicist. If I was very, very lucky, I could do something that would end up one sentence in a textbook. I did physics and I went to actually do a PhD in physics. Then kind of what I was working on didn't really grab me. It didn't feel like what I wanted to do, so I dropped out. I didn't start a startup. That would have been very on point for this event. But I dropped out and I ended up working at a company that was doing computational biology. How do we get computers to say something smart about biology? And I loved it. I loved it not just because it was fun, but it was something that would let me do what I thought I was good at. Write code, manipulate equations, think hard thoughts about the nature of the world, and use it toward this very applied purpose that at the end, we want to make medicines, we want to enable others to make medicines. And I really kind of became a biologist and a machine learner, actually a machine learner because I left that job and I went back to undergrad school in biophysics and chemistry. And I no longer had access to this incredible computer hardware that I had when I was working at my previous job. And in fact, they had custom ASICs for simulating how proteins, this part of your body that I'll talk about, move. And since I didn't have that anymore, but I still wanted to work on the same problems, well, I didn't want to just do the same thing with less compute. And so I started to learn, and I was getting very interested in statistics, in machine learning. We didn't call it AI back then. In fact, we didn't even call it machine learning. That was a bit disreputable. I said, I'm working in statistical physics. But how are we going to develop algorithms? How are we going to learn from data and do that instead of very large compute, and I guess it turns out in terms of AI, in addition to very large compute, to answer new problems? And after this, I joined Google DeepMind. And really, joining a company that wanted to say, how are we going to take these powerful technologies and all these ideas, and they were becoming very, very readily apparent how powerful these technologies were with applications to especially games, but also to things like data centers and others. How are we going to take these technologies and use them to advance science and really push forward scientific frontier? And how can we do this in an industrial setting with an incredibly fast pace working with some really smart people, working with great computer resources, and with all that, you darn well better make some progress? And it's been really, really fun. And the fact that I'm on this stage indicates that we made some progress. And I think really the guiding principle for me has that when we do this work, that ultimately we are building tools that will enable scientists to make discoveries. And what I think is really heartening about the work we've done, and the part that really I think still just resonates with me at my core, is there are about, I think, 35,000 citations of AlphaFold. But within that is there are tens of thousands of examples of people using our tools to do science that I couldn't do on my own, but are using it to make discoveries, be it vaccines, be it drug development, be it how the body works. And I think that's really, really exciting. And the part I want to talk to you about today in the story I want to tell you is a bit about the problem, a bit about how we did it, and I think especially the role of research and machine learning research and the fact that it isn't just off the shelf machine learning. And then I want to tell you a little bit about what happens when you make something great and how people use it and what it does for the world. So I'll start with the world's shortest biology lesson. The cell is complex.