🔬 An Oscar, Two Asteroids, and the Algorithm in Your sklearn: John Platt on AI for Science

Brandon Anderson (0:00)

Are you talking about introducing explicit priors that, you know, base upon some human intuition or maybe in this case, LOM intuition?

John Platt (0:08)

When you talk about multiple hypothesis testing, right, there's predictive models and there's descriptive models. A predictive model is like, let's say you just have a, you have some inputs and you have some outputs, and you just, I just want to build a piece of code that tries to just have the lowest error rate on some data set. The statistical model, a descriptive model is actually what science is trying to get to, which is, okay, it should be able to extrapolate because it has sort of the physics or the actual, some description of reality that's captured within it, and then you can use it to extrapolate. Yes, Newton thought of apples and gravity, but gravity isn't actually about apple, right? If you had taken 17th century machine learning model, like, oh, apples will fall, but how about planets? I don't know, I have no data about planets, so who knows what they do, right? The distinction between those is a little bit blurry, right? Because when a physicist or scientist comes, they use their intuition, or maybe even more than intuition. Like, essentially, there's maybe a solid pile of facts that they know about the world, and then they make sure that whatever model they build is sort of consistent with what's known.

Brandon Anderson (1:14)

Welcome to Latent Space Science. I'm Brandon, joined by my co-host RJ.

It's a pleasure to have Jon Platt, you know, with us today. Jon is a Google Fellow and Head of Applied Science at Google Research. He has really a fun, like, background. He, I guess, you described yourself when you were talking a few months ago as a mega nerd.

John Platt (1:32)

No, giga nerd.

Brandon Anderson (1:33)

Giga nerd, giga nerd, excited and absolutely everything. And it really, it really shows. Yeah, you was, correct me if I'm wrong about any of this stuff, but so you started college at 14 and started your PhD at 18 at Caltech.

You were advised or co-advised by Jon Hopfield, right?

John Platt (1:50)

Oh, yeah.

Brandon Anderson (1:51)

Yeah, yeah, who just won a Nobel Prize in, you know, two or three years ago.

John Platt (1:54)

Yes.

Brandon Anderson (1:55)

So Jon created several, responsible for several textbook algorithms, one known as Platt scaling, another one sequential minimal optimization, which is the textbook algorithm for training SVMs. Even today, it's still if you use SKLearn, it's there. Jon has discovered and named two asteroids, has a Oscar for technical developments from 2006

So if you've ever watched a Pixar movie, you've seen Jon's algorithms and work. Jon has an air dose bacon number of six or three, three and three from either side. I'm going to skip over like 20 years of your career. But then jumping into Google, working at Google Sciences, you've worked on fusion, quantum computing, climate modeling, and many other topics. Is that more or less right?

John Platt (2:43)

That's right, yeah.

Brandon Anderson (2:44)

Okay, cool. Did I miss anything important for today?

John Platt (2:47)

No. I've also done lots of applied math and signal processing and all sorts of fun things like that.

Brandon Anderson (2:52)

Yeah. I think you also, your Wikipedia has a fun story about patents and the iPhone too.

John Platt (3:00)

The iPod.

Brandon Anderson (3:01)

iPod, yeah. Yeah, welcome.

John Platt (3:03)

Thank you. Thank you for having me.

RJ (3:05)

Can you tell us about the era is the, I think the way that the acronym is pronounced.

I know that there's a lot of different semi-related stuff out there, both within and outside of Google. Can you tell us a little bit about the details of era and what makes it special?

John Platt (3:25)

Well, we've been doing AI for Science in Google Research for more than 10 years now. Around 10 years ago, it was very much using, I don't know what you call it now, maybe classical machine learning models, things like convolutional nets or whatever, and they were specific models to build to solve specific science problems. About two years ago, we got very excited about these more general LM's that have popped up in the last few years, and we were wondering what can be done with them. Of course, a lot of people have been playing and trying to figure out what the right thing to do is.

We stumbled into this mapping. In other words, we found that many different scientific problems can be mapped into something we call scorable tasks. So, you can often phrase a scientific problem as, gosh, I really would like to have a piece of code that maximizes some score. It's surprising the number of different scientific problems you can make a lot of progress on by mapping into that framework. Well, one thing is, a lot of scientists spend a lot of time building models. They might be statistical models or they might be physically-based models.