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

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Who is John Platt and what are his most famous contributions to AI and science?

Brandon Anderson 0:00

Are you talking about introducing explicit priors that, you know, based 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... It's 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. A 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?

John Platt 0:46

If you take a 17th century machine learning model, like, oh, apples will fall, but how about planets? You know, 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 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 Latents-Based Science. I'm Brandon, joined by my co-host, RJ. It's a pleasure to have John Platt, you know, with us today. John is a Google Fellow and Head of Applied Science at Google Research. He has really a fun, like, background. I guess you described yourself when we were talking a few minutes ago as a mega nerd. No, giga nerd. Giggler, Giggler, 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 John Hotfield, right? Oh, yeah. Yeah, yeah. Who just won a Nobel Prize in, you know, two or two years ago.

Bruno Satin 1:54

Yes.

Brandon Anderson 1:55

So John created several, he was responsible for several textbook algorithms, one known as plot 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. John 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 John's algorithms and work. John has an Erdos-Bacon number of six or three, three and three from either side. And I'm going to skip over like 20 years of your career, but then jumping into Google, working at Google Sciences, you worked on fusion, quantum computing, climate modeling, and many other topics.

Brandon Anderson 2:42

Is that more or less right? That's right, yeah. Okay, cool. Did I miss anything important for today?

John Platt 2:47

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

Brandon Anderson 2:52

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

RJ Haneke 3:03

Yeah, welcome. Thank you. Thank you for having me. Can you tell us about the ERA is the, I think the way that the acronym is pronounced. And I know that there's a lot of different semi-related stuff out there, both within and outside of Google. So what 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 sort of AI for science in Google research for more than 10 years now. And around 10 years ago, it was very much using, I don't know what you call it now, maybe classical, classical machine learning models, you know, things like, you know, convolutional nets or whatever. And they were specific models to build to, you know, solve specific science problems. But about two years ago, we got very excited about these more general LMs that have popped up in the last few years. And we were wondering what can be done with them. And of course, a lot of people have been playing and trying to figure out what the right thing to do is.

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Chapters

8 chapters

1 Who is John Platt and what are his most famous contributions to AI and science? 0:00–17:02 2 What is Google’s Empirical Research Assistance (ERA) and how does it turn scientific problems into scorable tasks? 17:02–32:03 3 How does ERA use Gemini, Monte‑Carlo tree search and Upper Confidence Bound to automate code generation? 32:03–47:40 4 Why can overfitting on Kaggle competitions be misleading, and what did the contrail‑detection challenge reveal? 47:40–1:06:03 5 How can AI help reduce the climate impact of aircraft contrails? 1:06:03–1:20:37 6 What advances have AI brought to weather prediction and climate modeling at Google? 1:20:37–1:35:18 7 What is the current outlook for quantum computing and fusion as future energy solutions? 1:35:18–1:49:10 8 What key advice does John Platt give to scientists about using AI as a power tool? 1:49:10–2:01:17

Speakers

3 identified

Brandon Anderson 19 min RJ Haneke 13 min Bruno Satin

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Topics in this episode

AI 1 Google 21 Nvidia 1 Gemini 19 San Francisco 2 DeepMind 1 International Space Station 1 iPod 1 Richard Feynman 1 quantum computing 1 Caltech 2 Academy Award 1

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Recorded22 Sep 2026

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Show notes

How often do you get to talk to a guest who has both an Academy Award and who invented textbook machine learning algorithms? John Platt has an Oscar, two textbook algorithms, two named asteroids, and an Erdos-Bacon number of 6. This was easily the most fun bio of all the guests we’ve read to date. And the result was an epic and fun chat covering Google’s Empirical Research Assistance (ERA), how AI can help battle climate change, and tons of great stories about the co-evolution of science and AI.John’s colleague Dave Bacon likes to tease John that his career has been defined by being twenty years early to the next big thing. This may be convolutional neural networks (some credit him with coining the term), fusion research, quantum computing. John and Google have been working on solving some of humanity’s hardest problems with AI and computation for well over a decade now. Recently John and his team set their sights on using AI to solve any scientific problem that can be written down as a score.Google’s Empirical Research Assistance (ERA)John’s team has taken on many hard scientific problems over the years. In solving these, they noticed a pattern, many scientific problems can be reduced to what John calls a “scoreable task”. Once you have the score function, the goal is to find some code that maximizes the score. The hard part is in formulating the score, but once you have the score finding the maximizer can still be quite a lot of effort.John’s team set out to automate solutions to this general problem. This came out of the idea of an “auto-Kaggle” AI, which can solve any Kaggle problem you can throw at it. Kaggle is owned by Google, so all the data was ready and easily available to them!The result is Google’s Empirical Research Assistance or ERA (paper, github, blog). ERA is surprisingly simple conceptually. Gemini (or your LLM of choice) keeps a running tree of past experiments (notebooks) and where they’re going. It’s a close cousin of Monte Carlo Tree Search: at each iteration the Upper Confidence Bound rule picks which notebooks are most promising to mutate. This is optimistic, not greedy, so sometimes even the fifth-best notebook gets chosen. Gemini then proposes mutations for each one, about ten at a time. The history of each branch is shared, so different leaves can learn from each other.“It’s almost like having a hyper-eager grad student who doesn’t sleep.”Evolutionary algorithms have been around since the 70s, but this works because Gemini actually knows where to look! What’s even more interesting is that there was a step change between Gemini 2.0 and 2.5, and this went from just not working to working great.ERA is so powerful that John and his team solved many outstanding problems with it, resulting in at least ten papers. Some of these were climate change related, which we talk about in the next section.So, we had to ask: if you have an optimization god how do you avoid fooling yourself? John’s answer is that ERA provides predictive models. It’s up to the scientist to make sure they’re truly descriptive. Some of this just involves good old-fashioned careful machine learning science. “It’s a power tool. It can slice your fingers off.” This led to some fun discussion about Kaggle competitions, and the fun ways people can overfit to datasets without meaningfully solving the problem you actually care about: Google’s contrail-detection competition was won by entrants who noticed a half-pixel error in the labels (is the origin at the corner of the pixel or the center?) and this turned out to be a part of the winning special sauce. Great for winning $15,000, not so helpful if you actually want to solve contrails.“People themselves will act like these LLMs and try to reward hack. It goes back to Goodhart’s law: any metric that becomes a target is no longer good as a metric.”His advice for where to start instead?“Always just fit linear regression. Just do it. Just do it. Just do it. Or SVM.”Tackling Climate Change with AIJohn and his team have worked extensively to mitigate the effects of climate change. We talked about several of their initiatives.Perhaps the most interesting result we talked about was reducing the effects of condensation trails (contrails) from airplanes. Those little streaks you see running behind planes somehow account for 1% of all human-induced global warming?!? Some of these trails of ice crystals can hang out for days. These crystals are black in the infrared, acting like a thermal blanket that traps heat day and night.It’s easy to understand what’s happening here, a region of atmosphere becomes “ice supersaturated”, and a tiny bit of exhaust seeds water vapor that instantly crystallizes. The scale here is astounding, with a single gram of exhaust resulting in ten kilograms of ice crystals.The solution to all of this is quite simple, in principle! We know what parts of the atmosphere are most likely for the trails to form. Just have the planes drop a flight level or two. Problem solved, right? Well, the hard part is accounting for how much warming was prevented. This is a counterfactual problem, parts of which stumped John’s team for over two years. They had a working model for the heat-trapping half, but not for the reflected sunlight. ERA was able to find a simple model with some confounders they hadn’t considered. Cracked it!Modeling climate generally is a hard problem. Climate is best thought of an attractor of many different possible weather outcomes. This makes it much harder to model.“Weather is where you are on the attractor, and climate is the statistics of the attractor. The problem with climate is that we’re altering it. The attractor itself is changing, it’s moving.”John and his team have worked on treating both the symptoms and the disease of climate change, with several other works in the area. Another fun example we briefly cover is FireSat, a way of using a constellation of satellites to rapidly identify fires before they grow too big to put out. For anyone living in California, you understand the problem. In dry years a small fire can result in hundreds of thousands of acres. If you could find this fire when it’s the size of a room, it could be put out. By the time it hits an acre we have a much harder problem.Where is this all going? Looking forward by looking backBy now it should be clear John has an incredible and unique view over the intersection of science, computation, and AI. John talked about a class on physics of computation he took with Richard Feynman back in 1982. This was when quantum computing was an ill-defined concept with no theory or experimental backing. John recalls every Tuesday was a guest lecture, and every Thursday was Feynman explaining why the Tuesday guest was wrong. John also recalls doing science back when there was essentially no compute, a million operations per second was cutting edge.What is John’s recommendation: the most important skill is developing deep domain expertise. There’s no other way to develop taste than to tackle hard problems. One surprising part of this is that John recommends spending time doing things the old fashioned way. Play with tools, and just implement things yourself.“You could drive up the mountain, or you could hike up the mountain, and maybe it’s okay, even fun, to occasionally hike.”Summing it up, John’s message to the audience is that there will still be a place for scientists, and that if anything it will just open up more opportunities for “the creative stuff, the rigorous stuff, the philosophy stuff.” But don’t forget to spend time doing the grunt work.“There just seems to be this strong impetus in the world to optimize and squeeze everything out. But you do lose something when you hyper-optimize. It’s overfit.”And whatever tools you end up using, John’s advice is the same one Feynman gave him forty years ago: you must not fool yourself, and you are the easiest person to fool.We had a great time talking with John. We hope you enjoy! Also in this episode* Fusion is three years away, not thirty, if you ask John. And why the Lawson criterion means every fusion approach has an Achilles heel.* Why superconducting qubits are still finicky.* The asteroid he named after his mom, which turned out to have a moon.* The looming helium shortage nobody talks about.* How NeurIPS started as people crashing a private workshop at Snowbird, and why Hopfield networks are all you need.* Being Carver Mead’s sysadmin on a VAX with an 80 MB disk the size of a dishwasher.* Finding asteroids in 1985 with film, a stereoscope, and a letter to Brian Marsden. The Vera Rubin Observatory found 11,000 in six weeks.* The Feynman effect: total clarity in the room, none once you leave.* Quantum echoes, the NISQ era, and why he thinks quantum is neither thirty years away nor tomorrow.* A startup that wants to inject mercury into a fusion reactor and sell the transmuted gold. “It might not work.”* John’s 20% time rule for his own group: do stuff for learning, and you don’t even have to tell him what. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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