How I Built This with Guy Raz - AI is smarter than you think with Shane Legg of Google DeepMind

Episode Date: April 11, 2024

For decades, Shane Legg has anticipated the arrival of “artificial general intelligence” or AGI. In other words: an artificial agent that can do all the kinds of cognitive tasks that people can typically do, and possibly more...Now as the Chief AGI Scientist and a co-founder of Google DeepMind, he stands by that prediction and is calling on the world to prepare. This week on How I Built This Lab, Shane’s path to becoming an early AI expert and the work he and his team are doing to prepare for the technological revolution ahead. This episode was produced by Sam Paulson with music composed by Ramtin Arablouei. It was edited by John Isabella with research help from Carla Esteves. Our audio engineer was Cena Loffredo.You can follow HIBT on X & Instagram, and email us at [e-mail] Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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So artificial intelligence is already changing the way we live and work. Believe it or not, even if you don't realize it. And so far, most of the AI applications we've seen have been focused on specific tasks, like generating images or video from a text prompt or identifying chemical compounds for new drug treatments or using satellite imagery to spot forest fires before they spread or even producing a podcast like this one. But what if there was a single AI program

that could do just about anything a human could do but even better? Well, my guest today, Shane Legg, has devoted his entire career to creating a general purpose AI that can be effective for, well, just about anything. In 2010, Shane co-founded DeepMind with the mission of bringing this artificial general intelligence to life.

Today, the company is part of Google's AI division and Shane is on the front lines of a technological revolution. But his journey started back in the 1980s when he was just a kid in New Zealand with a new birthday present. So for my 10th birthday, my parents bought a small computer It was called a VZ-200, and it had an 8-bit microprocessor and had whole 8 kilobytes of memory and had built-in basic programming language. And as a 10-year-old boy, I'd heard about computers in movies or read about them in books or things like that,

but I had a real computer of my own now. And this was prior to the Internet and all these sorts of things. So there was really only one thing to do on the computer, and that was the program. It was a place where you could make things. You could write your own programs. You could start learning the language and so on. And you could just dream. You could invent things.

You can invent little spaces with little pixels or little graphics that would chase each other around the screen or do all sorts of things like this. And it was just sort of a playground for my imagination. I could bring these little worlds to life. I could bring these little characters to life. And that just absolutely captivated me when I was 10, 11. and so on. You would go on to study mathematics and statistics in New Zealand. This is in the late 90s when you kind of start your career.

The notion of artificial intelligence had been around. It had been written about in science fiction novels and it had already been depicted in films. Was that on your radar as a young man when you were starting out your career? When I first started, it wasn't. I started doing mathematics. I like the subject because it was very challenging. I did some computer science as well. I found computer science very easy because I'd spent my, you know, adolescence programming.

But it was only at the end of my second year when in my spare time I wrote some software that did calculus. And I showed that to some of the professors. And then they said, well, actually, we've got a summer job. And on a machine learning project, would you be interested in coming to work for us for the summer? What did machine learning mean in 1983? It's like it's like the equivalent of like a, I don't know, like a mainframe computer to a laptop today, right? Yeah, it was early days then. It was a relatively new field then that it sort of split off from artificial intelligence, not that many years earlier.

And the difference in flavor was the emphasis on instead of engineering like reasoning systems or something like that, the emphasis was on using, data and learning a model of something. And so that's the machine learning comes from. And presumably at that time, by the way, you had to feed the data into the machine because it's really, I mean, there really wasn't, I mean, the internet was around, but it wasn't populated with endless reams of information. Yeah, it wasn't. And also, even if you had endless reams of information, the algorithms and the computers couldn't handle it. Yeah. So what you would typically do is you would have specific small data sets. I mean, by small, you'd also print them out on a few sheets of paper kind of size. And you would have algorithms, particularly classification algorithms. So what they would do

is you'd have a list of, I don't know, let's say people, and you'd have different measurements about their blood results and then whether or not they have a disease. And you would learn a function, which is, given this person's blood results, do they or do they not have this disease? And then in a new instance, you'd get the blood results and you wouldn't know if they have the disease, but you'd learn to predict it to classify that individual as, say, having that disease or not. So that's a classification problem. So that was your first contact with what sort of kind of AI, machine learning. Yeah. But in early 2000, right, most people who were going into the field of computing were really focused on the internet. But you went right into artificial intelligence at a time when

it was like really nascent. It wasn't really much of an industry yet. Yeah, there wasn't much of an industry. It was certainly an active area in academia. And so, yeah, after doing my undergraduate and then master's degree, I ended up working to do things like document classification for newspapers, like determining this document here that we use as the word bank, is it really about rivers or is it about finance, right? Yeah. So what you're talking about is, basically creating a specific task for a machine that can get, you know, more or less, can get a bit more sophisticated doing that single task. Yeah.

And this was sort of a kind of a crude, you know, sort of a compared to what these machines can do today, relatively crude. But this is what you pursued. I mean, you pursued a PhD in artificial intelligence, you know, already starting in 2003, and really began to think about machine superintelligence. This was the subject of your thesis. Machine superintelligence. When you talk about machine superintelligence,

at that time, were you already using the term artificial general intelligence? Yeah. So maybe it's worth going back a little bit to about 1999. So I ended up working for a company called Intelligenesis, and they were doing various things, including this sort of document classification and so on. But one of the things that were interested in is building artificial intelligence systems

that were very general and capable, building a thinking machine or something, if you like. And they didn't say they didn't really get very far in that, but it did get me thinking about that subject. And then when that company collapsed, I spent a lot more time thinking about the subject. And I actually became convinced, particularly after reading the age of spiritual machine,

by Ray Kurzweil, that artificial, very powerful and general AI systems were going to come, but some a few decades into the future. So I actually came up with an estimate then that it was about a 50% chance of what I call AGI now by about 2028. And that's also about the time that I propose the term artificial general intelligence. And to be clear, the way I define artificial general intelligence is it's an artificial agent that can do all the kinds of cognitive tasks that people can typically do and possibly more. And I like that definition because I think it's very intuitive. We have a good understanding the sorts of cognitive things that people can typically do. And we can look around at existing systems and see that they can do some of the sorts

of cognitive tasks people can do, but they can't do others. And so we have some sort of, you know, intuitive sense of the kind of breadth and capability that you would need to be classified and artificial general intelligence. Okay, so we're about four years away from seeing whether your prediction is right. We're going to come back to that later in this conversation. But I want to talk about what you would go on to do because while you were in graduate school, you met Demis Hassab is still at Google today and Mustafa Suleiman in the news now because he was just brought on by Microsoft to head up their AI program.

And together you founded a company called DeepMind in the UK. What was the goal? What was, presumably was to develop an AGI? Yes. Our business plan from September 2010 had our logo on the front and deep mind, and it had one sentence, which is build the world's first artificial general intelligence. And eventually you would develop a program, you would develop a system that could defeat the greatest go players in the world.

And for people who don't know this game, and I don't know it very well, this is a much more complex game than chess. This was a huge challenge to try and figure out how to actually build that. Can you explain why you guys were focused on Go? Yeah. To be clear, we did work on mini projects. We had a broad range of things. But, you know, AlphaGo ended up being, of course, one of the big famous ones.

Yeah, it was a long-standing problem in artificial intelligence. In a game like chess, the number of possible moves, is not so great that you can actually use brute force computation, more or less, to actually search through all the space of possibilities of which moves and which counter moves and so on and find very good combinations. In the game of Go, that's much more difficult because the board is much, much larger and there's a much larger number of possibilities at each point. So when you start looking through the trees of different possible moves, it exponentially

grows at a much, much faster rate. And so this basically tripped up all the more brute force approaches to search and planning that would try to play this game well. So we had to come up with something new. And what we did is we actually blended together search techniques, something called MCTS, with deep learning. And the deep learning would actually learn two things. It would learn which moves were likely to be good moves,

just by looking at the patterns on the board, using a deep neural network. And it would also learn, given a certain state of the game, who is more likely to win or lose? And then what we would do is we'd get our AI system to play against itself, and as it would win or lose games,

it would take that signal, and then it would apply these learning algorithms to improve these deep learning networks, so they've become better and better at anticipating which are the likely good moves, and at any given point in time, which of the two players was most likely to win the game. Now, if you combine that with a search, then you start having a very, very powerful algorithm that both has this sort of classical search, but this sort of slightly more intuitive, deep learning aspect

where it's kind of picking up subtle patterns in the game and trying to figure out which way it's going based on sort of these more subtle structures and so on. And it was that beautiful combination of the two that led to the breakthrough and performance. We're going to take a quick break, but when we come back, how DeepMind went from mastering Go to working on some of the biggest challenges of our time. Stay with us. I'm Guy Raz, and you're listening to How I Built This Lab. Welcome back to How I Built This Lab. I'm Guy Raz. So in 2014, only a few years after its founding, DeepMind was acquired by Google. This is from a business perspective, an amazing story, but you got only around 75 employees, I think, at DeepMind.

And Google acquired the company for between reportedly five and $650 million. And then you became for about nine years sort of an independent part of Google. And we'll talk about why that changed recently. But essentially working on different projects, including projects around drug development and working with trying to model proteins that could potentially. offer life-saving cures. Yeah, yeah. So that was another big success, which is protein folding.

So your body is largely built out of proteins. These are these molecules that are the building blocks of, you know, most of biology. And it's not very difficult to know what the atoms are that make up the molecule. It's a certain chain of atoms. But what happens is the different atoms in that chain attract or repel each other in different ways. And the result of that is that. that chain actually folds up into a three-dimensional shape. And that three-dimensional shape can be all sorts of things. It can be an axle, it can be a spiral, it can be sort of a sheet, it can be sort of a sphere

that contains something. You can build all sorts of interesting structures out of these shapes, sort of like 3D Lego blocks, if you like. And these shapes are very important if you want to understand what that protein does. Now the problem is that while it's easy to find out the molecules that make up the protein, it's very difficult to find out the shape. And the techniques that people used to use would often require several years of research to find out what the shape was and cost maybe $200,000. It's maybe something that somebody would do as a PhD thesis, is find out the shape of one protein. And there are hundreds of millions of proteins. So there was this computational challenge that had been around for many decades, which is,

is it possible to take the molecule, just the knowledge of the atoms, and compute what the shape is directly, rather than go through this laborious process of several years of experiments and all these sorts of things. And so that was called the protein folding problem. How does a protein fold into the three-dimensional shape? And can you predict this computationally? And so people have been trying to do that for a long time. And we thought using advanced machine learning, deep learning techniques, and so on. that we had a shot at solving this problem.

And, yeah, long story short, we spent a few years working on it and we basically solved the problem, yeah. And just for some clarification around it, I mean, what does it mean in practical terms? I mean, is it ready to go in terms of enabling treatments and drug development? Yeah, it's not quite that direct. What we did is we folded all the proteins known to science and we released them all to the public for free. so you can find all online on the internet and you look at the shapes and so on. And we've had about 1.7 million researchers use that resource. It doesn't mean you suddenly know how to build a drug or something rather,

but it does mean that you can now see what the different shapes of the proteins are, the proteins that you might have in the drugs, the proteins that might be, I don't know, part of your liver or some other part of your biology. And you can see how they maybe interact with each other and all sorts of things like this. So it's not like you suddenly can solve the problem, but before when you're operating in the dark and you didn't even have any idea what these things looked like, and wouldn't even learn how they might connect together and other things like that. Now you can see a whole lot of this

information. And so that's incredibly useful when you want to go and then develop drugs and so on. So you might see, I don't know, there's a particular problem taking place and it's to do with a particular protein. You might be able to then go, okay, what proteins are going to connect into this other protein and act on in certain ways? And you can then target. get specific things that look like they're going to be, you know, very interesting and so on. So it's a, it's not a solution to drug discovery and everything, but it's a great enabler, an accelerator of this kind of process. Can you explain what happened around 2017 in the field of AI research?

My understanding is that large language models were essentially introduced. And I guess that was like a turning point in the acceleration of the development towards artificial general intelligence? I think it was. We, at Google, had invented an extremely powerful algorithm called Transformer. And we'd been experimenting

with it for things like translation between different multiple language targets in and out and all sorts of things like that. And we'd found it to be very, very scalable. And then what happened was that another company, OpenAI, latched onto the idea that this is in fact extremely scalable, more scalable than anybody

had appreciated. and they just basically scaled it up and scaled it up and scaled it up and scaled it up. And, you know, it just kept on scaling, basically. And so there was a, it was a bit of a surprise in just seeing how far these language models could go when you made them extremely big. And you started feeding in, you know, a significant chunk of text from, from the internet. And so that came as a surprise to many people. They didn't think it would go quite so far, yeah. So last year in 2023, DeepMind, which had been essentially an independent part of Google for a decade, was merged with Google's AI division.

And I think that was in response to what was happening with Open AI and maybe even some other competitors in the space to really kind of ramp up what Google could do around artificial intelligence. did part of that feel like you were joining an arms race that, you know, did it feel like, okay, all hands on deck, we've got to compete against these other companies? Yeah, so what happened basically was that, you know, it was becoming clear that extremely scaled-up models were going to become a really important thing in the future. And, you know, for Google, it was important that we had the biggest and best models. And it doesn't make sense to have, you know, two different, groups both developing big models. We needed to, you know, come together. We both had a lot of

expertise in this sort of thing, come together and use all the, you know, resources in terms of all the people together and then all the compute and everything to make the best models we possibly could. All right. Around that time, Shane, you signed a letter that was signed by many other people in the AI industry, warning of extinction risk. This is one of the quotes, is mitigating the risk of extinction from AI should be a global priority. Everyone signed this letter. I mean, Sam Altman signed this letter. Jeffrey Hinton, the sort of the godfather of AI, the heads of AI at Microsoft and Anthropic.

And everyone signed it. And again, this technology is going to happen, right? But part of me is like, okay, all these people who are creating this technology and warning that there's a risk of extinction from the technology they're creating, are signing this letter to sort of say, we should be figuring this out. But at the same time, like, we're just going to keep moving forward and marching forward. And so to me, there's sort of a dis...

Again, I'm not criticizing or attacking you. I'm just trying to figure, understand your thinking around this, because on the one hand, you're signing a letter that's sort of ringing the alarm bells and then, you know, and then a couple hours later, you're going back to your desk and doing your job. Yeah, and doing my job is often working on safety, to be clear. Okay? I mean, why am I doing what I do?

Well, one, I think that something deeply transformational is about to happen in the coming years. Yeah. The world that we live in is being shaped by human intelligence. The clothes I wear, the headphones I have on, the internet we're talking to each other, the words we're using, the concepts we're using. Even the atmosphere we're breathing at the moment has been affected by human intelligence and combustion engines that we've invented and all these sorts of things, right?

So human intelligence is profoundly powerful thing that's affected the world very, very deeply in many, many ways. Now, what's about to happen is that machine intelligence is going to arrive and it is going to be potentially very, very powerful. So this is going to be a deeply transformative event. Now, this could be amazing. This could be unbelievable.

This could be a new golden age for humanity opening up all sorts of possibilities, solving all kinds of problems, and just being really a mind-bendingly, fantastical thing. But like any very, very powerful technology, you know, there are things that we don't understand going into this.

There could be unintended consequences. We could possibly get some things wrong, right? And so we need to take it very, very seriously. We need to take seriously the possibility that things could possibly go wrong when we're going into such a, such a transition. And then the other point is that I don't see any way to stop this. Can't put the genie back in the bottle. You can't put the genie back in the bottle. Intelligence is

profoundly valuable for many, many reasons. And I don't know of any way to globally get everybody to stop using and developing this very, very valuable technology. And so as far as I can see, this will be developed. And so the important thing is that we understand that this is indeed extremely transformative and valuable, and we approach it with an appropriate level of care, so that we can understand where the risks lie, understand where the challenges are, and we can navigate that wisely. So we end up in a future where this powerful intelligence is giving humanity many wonderful blessings and gifts, and we avoid all kinds of people misusing it or different types of problems where it's been misfiring in some way and causing some

sort of problem or something like that. I get nervous when I hear a future of maximum human flourishing. It really feels in some ways like a false promise, not to say that parts of that won't happen, but a version of that quote is almost exactly what Eric Schmidt said when your model beat the go in 2016. You know, this is going to usher in an era where humanity is the winner. And I'm not trying to be cynical here. And by the way, I really appreciate that you acknowledge you don't have the answers. I mean, you're not a policy guy. You're a scientist. I don't believe anybody does. We're going into something which is at least as profound as the Industrial Revolution.

Yeah. Now, could you have anticipated all the consequences of the Industrial Revolution before it happened? There's no way you could. It affected everything. It affected how cities have It affected international trade. It affected health. It affected diet. It affected the structure of families. It affected culture. It enabled mass industrialized murder.

It enabled massive warfare too. Of course, there were hugely profoundly negative consequences, but massive benefits too. Yes. All around us, we see the benefits of it. So these deep transformations are subtle and complex and you can't see all the different things come out. So that's why I signed the letter. I signed the letter to say, to people, hey, wait a minute, there is enormous potential here, an enormous promise here,

but there can be some bad things here too. And we need to be really serious about this. We need to understand how big a transition this is, and we need to treat it with the appropriate care so that we can get the benefits and try to avoid the downsides. We're going to take another quick break, but when we come back, AI with a million times the power of a human brain, and why we should all be paying attention. Stay with us. I'm Guy Raz, and you're listening to How I Built This Lab. Welcome back to How I Built This Lab. I'm Guy Raz. Here's more for my conversation with Shane Lay, co-founder of Google Deep Mind.

I think sometimes we humans take an ahistorical perspective on things because we're looking at what is going on at this point in time. And we're not sort of fully thinking about the sweep and scale of human history. But let me actually try and take a historical perspective for a second because you could argue that our brain. Our human brains are not more intelligent than a human brain 30,000 years ago, maybe marginally, right? Like, but could a human 30,000 years ago in the right environment, like, be a member of the Manhattan Project team? I think it's possible. I don't know how much has changed in the brain in 30,000 years. But what we're talking about, especially if your prediction is right, and it's four years away, is a machine that could go from early Homo sapien to Manhattan.

project physicist in a matter of weeks, eventually days and then minutes and then seconds. I mean, that's kind of what could happen, a machine that just gets infinitely smarter, faster. Yeah, I mean, it's not clear how infinitely smarter, faster it can get. There may be limits into how quickly certain things can happen. We don't know what those limits are yet, so I don't want to make too many promises, but, you know, there may be certain, you know, limitations in terms, certain scaling, certain exponential costs and so on as certain things develop. But at a high level, I agree.

The one way I think about is this, the human brain consumes something like 20 watts. It weighs a few pounds. And it sends signals via axons inside the brain. They're electrochemical wave propagations. They travel at about 30 meters per second. and the cycling, the frequency of the signals is on the order of 100 hertz. Now, if you compare that to just a present-day supercomputer, instead of 20 watts, you can have easily 20 megawatts.

So you've got a million times the energy consumption. Instead of the size of the brain, it can be a million times that size. Instead of sending signals through an axon at 30 meters per second, you can send signals at the speed of light, 300,000 kilometers per second. instead of a frequency of transmission and the signal of 100 hertz, you can be a billion hertz, 10 billion hertz. So if you look at energy consumption, physical space, speed of signal transmission, and the frequency on the signal, you're looking at six to seven orders of magnitude in every direction just

with present day technology. Now, how intelligent a system will it be possible to construct, given these sorts of parameters, And I think the answer is probably an extremely intelligent system. Extremely intelligent. Extremely intelligent. So I think the answers we don't know, but I also think that it's not implausible to imagine a near future, not so distant, where machines are becoming exponentially more intelligent to the point where the definition of an artificial general intelligence system isn't, can it perform all the cognitive functions of a human, but rather what, what can you? can't it to do, right? What can't it think? So that definition of AGI to me is the bar. That's the entry level into AGI. But I do not think that's where it stops. I think you start going into

what they call ASI or artificial superintelligence. And it may be the case in certain dimensions is the scaling of capability tops out at a certain rate. And you don't actually go that much beyond humans. But in other dimensions, it may go far beyond humans. And so in some shape or form that's not very well understood, I think we will end up with systems that are, in general, far more intelligent than humans. And so this is why it is so important that people think about what's coming. They think about how to navigate the possibilities at opening up so that these very capable systems can solve cancer, that can create clean energy systems like solving fusion can do all sorts of amazing things to benefit humans.

That is why this is so important. That is why I talk about this. This is why I've been talking about this since back in, you know, 2005 when people would listen to me, you know, AGI is a thing. It is coming. And when it comes, it's going to be so important that this is handled very, very carefully so that humanity can,

get the benefits of this. I know the Biden administration put together an executive order last year, which is sort of a one of the most robust kind of attempts to build guardrails. Still, a lot of people don't think that it's enough. You are, you work at one of the most valuable companies in the world, one of the most powerful companies in the world, right? So what can you do? I mean, is there an answer? Like, you're talking to me about this. You're trying to shake the public.

And hopefully our listeners are like, okay, what do we do? But you're one of the people working on this at a company that has access to, you know, the White House and 10 Downing Street and, you know, the halls of power around the world. How do you build in a protective system? Yeah, it's a very complex thing because it's not about a specific thing. It's like the internet is a good example. it's not like there's a very particular technological solution to get the value out of the internet and avoid the problems. It actually affects all kinds of aspects of society, of markets, of social relations, all kinds of things.

So it's really something that a very broad range of people from society have to engage with. That's really the only way to do it. It's not going to be solved by some people in a company somewhere. That's not how it's going to work. It's something that society as a whole needs to engage with because that's the nature of the transformation, which is a work here. And it would be inappropriate, I think, if it, it was like, you know, society was relying on, you know, a few people in a company to do the right thing. That's, that's not really a robust way to go about deep transformations. So we need to, as a society,

and as I said, it's not just the machine learning people, but all sorts of aspects of society, all sorts of people from different disciplines, need to think very carefully about what's becoming possible and take seriously, because there's a long time a lot of people didn't take this seriously, take seriously the idea that general intelligence and machines is actually coming. What does that mean? What are some of the implications of that? What do we need to start doing to prepare for that? Are there certain things that just shouldn't be allowed? Are there certain rules or policies or regulations or other stuff like that? So we can try to navigate this as wisely as possible. All right. This might be a weird question, Shane. But when you think,

think about you, who you are, your family, are there, it's going to sound like I'm a survivalist or crazy conspiracy theories, but are there things that you ever think about like, I better do this? I better think about the way I'm doing this in my life to prepare for the potential downside consequences of what this other thing could mean. No, not really. The way I think about it is that the maximum leverage I have as an individual in all of this is to support things like AGI safety research. I've been a vocal advocate for this for many years,

including when it was widely ridiculed, and there's a lot of eye-rolling going on. I led the AGI safety group here at Deep Mind for many years and was always advocating publicly for people to take this topic seriously. I personally think about the subject quite a lot and different approaches and the pros and cons for them and discuss this with various people. And I engage with other companies and with government. I've talked to the UK government here about these subjects and so on

and consulting them about AGI safety and the challenges around that and the different approaches and so on. So as an individual, rather than, you know, prepping for some sort of scenario. I don't know what you have in mind there going. You can move to New Zealand. You're a New Zealand citizen. I'm a New Zealand citizen.

I can move to New Zealand and have a bunker there. But, you know, that's, that's not okay. There's something is happening and it's big in the world and it's going to affect, it's going to affect a lot of people. It's going to affect everyone. So the point of leverage that I have in this whole process has been to stick my neck out and say, look, you know, this is serious. We have to take this seriously.

We have to think very hard about questions like AGI safety and socio-technical risks and all kinds of things like this and policy and governance questions and all this sort of stuff so that we have the best chance possible of navigating this wisely in a way which is broadly beneficial to the world. That's the point of leverage I have. There's no use going to try to build a bunker somewhere. That's not going to get very far. We're all going to, either we're all going to, it's all going to work out or we're all going to die.

So I hear you. I wouldn't put in such an extreme spectrum. I think with these technologies, there's actually a wide range of possibilities. There are some very, very good ones. And there's probably many mixed scenarios. So if you look at again, something like the internet, you know, the internet is not all blessings, right? It's not all good. But it has its wonderful aspects as well, right?

It has its wonderful aspects. And so I think realistically, there will. will be positive and negative aspects of artificial intelligence. And if we can do this wisely, we can get a lot of the positive benefits. And the positive benefits can be amazing. But we need to take it seriously if we're going to navigate this well. That's Shane Leg, co-founder and chief AGI scientist at Google DeepMind. Shane Legg, thanks so much.

Thank you. Hey, thanks so much for listening to the show this week. Please make sure to click the follow button on your podcast app so you never miss a new episode of the show. and as always it's free. This episode was produced by Sam Paulson with music composed by Rumtin Arablewe. It was edited by John Isabella with research help from Carla Estevez.

Our audio engineer was Sina LaFredo. Our production staff also includes Alex Chung, Casey Herman, Chris Messini, Terry Thompson, Jayce Howard, Malia Agadello, Neva Grant, and Catherine Seifer. I'm Guy Raz, and you've been listening to How I Built This Lab.