The AI Will See You Now: Exploring Biomedical AI and Google’s Med-PaLM2 With Karan Singhal

**Elad Gil** (0:05)
Welcome to No Priors. Today, we're speaking with Karan Singhal, a researcher at Google where he is a leader on medical AI, specifically on MedPOM2, where he and a team are working on responsible path to generative AI in healthcare.
Google just announced the launch of its next generation language model, POM2, with improved multilingual reasoning and coding capabilities, which is behind MedPOM2. So it's a great time to be speaking with Karan about everything he and his team are working on. Karan, welcome to No Priors.

**Karan Singhal** (0:32)
Hey, guys.

**Elad Gil** (0:33)
So you've been working in this field for a long time.
Tell us about how you ended up working on medical AI at Google. I think I saw you started a fake news detector for using AI as a 19-year-old.

**Karan Singhal** (0:46)
Yeah, that was one of my first AI projects. I really got into AI thinking about how it could be used in socially responsible ways. And for me, I was thinking around the time of the 2016 election that maybe a little bit naively that we could, AI-based solutions could be a bit of help for things like misinformation and detecting that. I think in the longer run, I mean, I've thought of that as kind of a more naive project. And I think in the longer run, I've been thinking more about how it can help shape the trajectory of AI to be more beneficial and more broadly. And I think for me, thinking about the medical setting has been motivated largely by thinking about the fact that it's a great place to think about concerns around safety, reducing hallucination and misinformation as well here.
Thinking about how we can produce medical question answers that are less likely to be harmful and all these kinds of things.
And that motivation, I think, has driven us to this point where really going for the jugular in terms of thinking about how to train these models, make them better in the setting. And so very excited about that kind of work.

**Elad Gil** (1:49)
Have you been working on the medical domain your entire time with Google?

**Karan Singhal** (1:54)
No. I mean, for me, this is just something I've gotten to the last year and a half. So I've been new to it. I've been learning from an excellent team and it's been an amazing journey so far.

**Elad Gil** (2:03)
What else has been the most interesting in your work at Google so far?

**Karan Singhal** (2:07)
Yeah, I started working out in representation learning and federated learning. So this is kind of the technology, representation learning in particular is kind of the technology underlying a lot of the deep neural networks of today, including GPT-3, GPT-4 and so on.
So this is largely about learning representations of text, of images, of other modalities, such that you can efficiently encode them, you can learn from them in the future, you can generalize the new text and images and so on. So the work for this really started back in the beginning of the deep learning era, like in 2013, with compositional neural networks and scaling those up and Word2Vec around 2015 and GloVe and all these things. And I think since then, we've been working on technologies around self-supervised learning, around doing that in a privacy-preserving way. And so after a couple years of working on that at Google, had the opportunity to quickly grow and start to lead a team, I got to the point where I was thinking, okay, I've upscaled in a lot of ways, I've gotten to the point where I can mentor many other researchers in a lot of ways.
And now it's a great time to be thinking about my next thing and going for something ambitious in terms of shaping the trajectory of AI. And so about a year and a half ago, a few of us had the idea to think about this medical setting as kind of a setting in which these concerns are especially important and that there was a ripe opportunity to think about this paradigm of foundation models in medical AI. And so within Google, we had the opportunity to pitch what's called a brain moonshot, which is kind of like an internal incubator program for ambitious research projects. And this is, you know, a lot of cool research projects that you've heard of from Google have eventually come out of this program. As we pitched that, we got it accepted and funded. We got the ability to kind of get a bunch of compute to bring other folks on board with the sponsorship of a bunch of leaders. And our first thing together was really MedPaLM2. So that was a really amazing thing for us to be able to work on together.