AI is smarter than you think with Shane Legg of Google DeepMind

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And that's it. Hope to hear from you soon, and we are so excited to have you come on the show. And now, onto the show. Hello and welcome to how I built this lab. I'm Guy Raz. 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 uh for my 10th birthday, my parents bought a uh small computer. Um it's called a VZ two hundred. And it um has an eight bit microprocessor and head.

And had built in basic programming language. And as a ten year old boy, I'd I'd heard about computers, you know, in in movies or read about them in books or things like that. But I had a real computer of my own. No.

Um 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. You It was it was a it was a place where you could make things.

You could write your own programs, you could im you you could start learning the language and so on, and you could just dream. You could invent things. You could 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 a 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 ten, eleven. And so on.

You you would go on to study mathematics and statistics in New Zealand. Um this is in the late nineties 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 in in it already been depicted in films. Was that on your radar? As a young man when you were starting out your career? Uh

When I first started it wasn't. I I I started doing mathematics. I liked the subject because it was very challenging. Uh, I did some computer science as well. I found computer science very easy because I'd spent my you know, adolescents uh programming. But it was only at the end of my Um second year. When in my spare time

I wrote some software that did calculus. Uh and I showed that to some of the professors and then they said, uh, well, actually we've got a summer job. uh and on a machine learning project would you be interested in um coming to work for us for the summer. What did machine learning mean in nineteen eighty three? It's like I I it's uh it's like the equivalent of like a I don't know, like a mainframe computer to a laptop today, right? Yeah, it was it was it was early days then. Um

It was it was a relatively new field then that had sort of split off from artificial intelligence um 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 um using um data and learning a model of something. And so that's the where machine learning comes from.

And presumably at that time, by the way, you you had to feed the data into the machine. Because there's really the I mean, there really wasn't I mean the internet was around, but it wasn't populated with with endless streams 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 Um specific small data sets.

I mean by small you'd like Also You know, print them out on a few sheets of paper kind of size. And you would have algorithms, particularly um classification algorithms. So what they would do is you'd have a a list of I don't know. It's a people.

and you'd have different measurements about their blood results and then whether or not they have the 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 they having their disease or not. So that's a classification problem. So that was your first contact with what

sort of kind of AI, machine learning. Um but in early two thousand, right? Most people who were going into the field of computing were really focused on the internet. But you w w 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, I after doing my undergraduate and then master's degree I ended up working uh to do things like document classification for newspapers. Like you know, determining this document here that we use is 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. Get br you know, more or less. s can get a bit more sophisticated doing that single task. Yeah. And and this was sort of a kind of a crude comp you know, sort of a compared to what what these machines can do today, relatively crude. But

Um but this is what you pursued. I mean you pursued a PhD in in artificial intelligence or you know, already starting in two thousand three and really began to think about machine superintelli This is the the the the um subject of your thesis, machine superintelligence. Was the I mean 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 nineteen ninety nine. So I ended up working for a company uh called Intelligences. And they were doing various things, including the sort of document classification and and so on. Um, but one of the things they were interested in is building um artificial intelligence systems that were very general and capable, building a thinking machine or something, if you like. And um

They they didn't I'd 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 Um I spent a lot more time thinking about the subject and I actually became convinced particularly after reading The Age of Spiritual Machines by Ray Kurdswell.

That artificial there's very powerful in 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 fifty percent chance of um what I call AGI now by about twenty twenty eight. And that's also about the time that I propose the term artificial general intelligence.

And and 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. Um and I like that definition because I think it's very intuitive. We have a good understanding of the sorts of cognitive things that people can typically do. And we can look around at at existing systems and s you know see that they can do some of the sorts of cognitive tasks that people can do, but they can't do others. And so we have some sort of You know, intuitive scenes.

of the kind of breadth and capability that you would need. to be classified as an artificial general intelligence. Okay, so we're about four years away from seeing whether your prediction is right. We're gonna come back to that um in later in in this conversation, but I wanna talk about what you would go on to do because while you were in graduate school, you met um Demis Asab is still a Google today. And and Mustafa Sulemon in the news now because he was just

Brought on by Microsoft to head up their AI program. And together you founded a company. Uh called Deep Mind. in the UK. Yeah. What was the goal? What was the the the presumably was to develop an A G I Yes, our business plan from September Thousand and Ten.

Uh had our logo on the front. Uh and Deep mind, and it had one sentence. Which is Build the world's first artificial general intelligence.

Mm. 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 to don't know this game and I don't know it very well. This is a much more complex game than chess. This is a this was a huge challenge in trying to figure out how to actually Build that. Can you explain why you you guys were focused on go? Yeah. Um, to be clear, we did work on many projects. Yeah. A broad range of things. But um, you know, uh Alpha Go ended up being of course one of the big famous ones.

Um yeah, it was a it was a longstanding 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 and so on and find very good combinations. Um 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. they 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 Um search techniques, something called MCTS.

With Deep learning. And the deep learning would actually learn uh 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 out 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 become better and better at anticipating which are the likely good moves.

And at any given point in time who 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 most 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, you know, 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. It would lead through to the breakthrough in performance.

We're gonna 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 twenty fourteen, only a few years after its founding. Deep mind was acquired. By Google.

This is from a business perspective an amazing story, but you had only around seventy five employees, I think, at DeepMind. And Google acquired the company for between reportedly five and six hundred fifty million dollars and then and then you became For about Nine years sort of a an independent Part of Google. Um and we'll talk about what why why that changed recently, but essentially working on different

projects including projects around drug development and and working with with trying to model proteins that could could potentially offer life saving cures. Yeah, yeah, so there was another uh big success, which is um protein folding. So your body is largely built out of proteins. These are the molecules um that that are the building blocks of, you know, most of the 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 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 a sphere that contains something. You can build all sorts of interesting structures out of these shapes.

sort of like three D 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 uh would often require several years of research to find out what the shape was. And cost Maybe two hundred thousand dollars.

Um, 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 the knowledge of the atoms.

And compute. What the shape is directly. Mm. Um, rather than go through this laborious process of several years of experiments and all these sorts of things. And so that's what's 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. Um, and we thought using Uh advanced machine learning, deep learning techniques and so on. That we had a shot at solving this problem. And yeah, long story short, we we spent a few years working on it and we we basically solved the problem, yeah. And and j just just for some clarification around it, I mean what does it mean in in practical terms? I mean is it

Is it ready to go in terms of Enabling. Treatments and drug development? Uh yeah, it's not quite that direct. Um what we did is we folded all the proteins known, yeah uh to science and we released them all to the public for free. So you can find them all online on the internet and you can look at the shapes and so on. And we've had about one point seven million researchers um use that resource. Um 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 your biology. And you can see how they maybe interact with each other and all sorts of things like this. So it's It's not like it you suddenly can solve the problem, but But before when you're operating the dark and you didn't even have any idea what any of these look like, or let alone 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 gonna connect into this other protein and act on it in certain ways. And you can then s target specific things that that look like they're gonna be, you know, very interesting and so on. So it's uh 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 twenty seventeen in in the field of of AI research? Um my understanding is that large language models were s essentially introduced. And and I guess that was like a turning point in the acceleration of The development Towards

Artificial general intelligence. Uh I think it was. We a Google had invented an extremely powerful algorithm called Transformer. Mm-hmm. 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 um had appreciated. And they just basically scaled it up and scaled it up and scaled it up and um You know, it just kept on scaling, basically. And so It was uh

It 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 a significant chunk of text from from the internet. Um and so that that came as a surprise to many people. They didn't think it would go it would go quite so far, yeah. So last year in twenty twenty three. Deep Mind, 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 and maybe even some other competitors in the space to to really kind of

ramp up what Google could do around artificial intelligence. Um Did part of that feel like you were joining an arms race that that you know, did it feel like okay, all hands on deck, we've gotta compete against these other companies? Yeah, so So what happened basically was that

Yeah, it was becoming clear that ex extremely scale dark models were gonna become a really important thing in the future. Um And you know, for Google Uh 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. Um, we needed to You know. come together. We were both had a lot of uh expertise in this sort of thing.

And use all the You know, resources. um in terms of all the people together and then all the compute and everything to make the best models we possibly could. Around that time, Shane.

You signed a letter. that was signed by many other people in in the AI industry, warning of extinction risk. The it's this is the qu one of the quotes says 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 and Anthropic and Everyone signed it and and and

I I have Again. This technology is is going to happen, right? But part of me is like, Okay, all these people who are creating this 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 gonna keep

moving forward and marching forward. And so To me there's sort of a dis Again, I'm not and 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 that's sort of ringing the alarm bells and then you know and then uh a couple hours later going back to your desk and doing your job.

Yeah, well 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 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. Uh, 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 a a a 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. There we could possibly get some things wrong, right? And so we need to take it very, very seriously.

We didn't 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 bottom. 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 leasings 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 I get nervous when I hear a future of maximum human flourishing. It it really Feels in some ways like a false promise. Not to say that parts of that won't happen, but I ex i a version of that quote is almost entire exactly what Eric Schmidt said when your model beat the go in twenty sixteen. You know, this is gonna usher in an era where humanity is the winner. And and I I'm not trying to be cynical here. And 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 do 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 are built, 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 w massive warfare, too. Right. 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 up. 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 and try to avoid the downsides.

We're gonna 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 DeepMind. I think sometimes we humans take an a historical perspective on things because we're looking at 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 Акцію try and take a historical perspective for a second because

You could argue that our brains, our human brains. are not m more intelligent than a human brain thirty thousand years ago. M maybe marginally, right? Like But but Could a human thirty thousand years ago in the right environment, like um uh 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 but what we're talking about, if especially if your prediction is right and it's four years away. is a machine that could go from early Homo sapiens 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 f smarter, faster it can get. There may be Limits and

To how You know, quickly certain things can happen. Um we don't know what those limits are yet, so you know, I don't want to make too many promises, but Yeah, there may be certain you know, limitations and to certain scaling.

uh certain exponential costs and so on as certain things develop. But At a high level I agree. Um The The w one way I think about it is this.

The human brain consumes something like twenty watts. Um it weighs weighs a few pounds. Um, and it sends signals uh via axons inside the brain, their electrochemical wave propagations, they travel at about thirty meters per second. And the cycling the the frequency of the of the signals uh is on the order of a hundred hertz. Now if you compare that to just a present day supercomputer.

Instead of twenty watts, you can have easily twenty megawatts. So you're going to 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 thirty meters per second.

You can send signals at the speed of light. three hundred thousand kilometers per second. Instead of a frequency of transmission and a signal of a hundred hertz. You can be, you know, a billion hertz, ten 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.

No. 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. So I think the answer is we don't know, but I also think That it's not implausible. To imagine.

A near future, not so distant, but uh 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 but rather

What can't it do? Yeah. Right? What can't it think? So that definition of AGI to me is the is the bar, that's the entry. And to 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 the scaling of capability Tops out.

uh at a certain rate and and you don't actually go that much beyond humans. But in other dimensions that 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

uh far more intelligent 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 that are opening up so that

these very capable systems can solve Cancer. Create clean energy systems like Solving Fusion can do all sorts of amazing things.

To benefit humans. That is 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. Two thousand and five when people would listen to me, you know.

AGI is a thing. It is coming and and when it comes It's gonna be so important. that this is handled very, very carefully.

So that humanity can get Benefits of this. I know the Biden administration put together an executive order last year which is sort of a a one of the most robust kind of attempts to build guardrails still a lot of

People don't think that it's that's n enough. Um 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 I mean is there an answer? Like what c 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 um you know, the White House and and ten Downing Street and You know

the the halls of power around the world How do you build in a protective system? Yeah, it's 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 a a very broad range of people from society have to engage with.

That's really the only way to do it. It's not gonna be solved. by some people in a company somewhere. It's not how it's gonna work. It's something that society as a whole needs to engage with. because that's the nature of the transformation, uh, which is working. And it would be inappropriate, I think, if if it it was like, you know, society was relying on and uh it's 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 it 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. 'Cause for a long time a lot of people didn't take this seriously. Take seriously the idea that general intelligence in machines is actually coming.

What does that mean? What are some of the implications of that? What we need to start Are there certain things that just shouldn't be allowed?

I just shouldn't Rule. policies or regulations or other stuff like that. So we can try to navigate this Um as wisely as possible. All right, this might be a weird question, Shane, but

When you think about you who you are, your family. Are there It's gonna sound I'm gonna sound like I'm a survivalist or crazy conspiracy theorist, 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

A GI 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. Um, I led the AGI safety group here at Deep Mind.

For many years. Um and was always advocating publicly for people to take this Topic seriously? Um I personally think about the subject quite a lot and different approaches and the pros and cons of them and discuss this with various people. And I engage with other companies and with government. Um, 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 but Move to 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 that's that's not okay. There's uh there's something is happening and it's big in the world and it's gonna affect it's gonna affect a lot of people. It's gonna affect everyone. So The 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 and a way which is Probably beneficial. To the world. That's that's the point of leverage I have. There's no no use going trying to build a bunker somewhere. That's that's that's not gonna that's not gonna get very far. I hear you. I think that's my I wouldn't put it such a such a extreme spectrum. I I think with these technologies there's actually a wide range of possibilities. There are some some very, very good ones and there's

there's probably many mixed scenarios. So if you look at again something like the internet Yeah, 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.

I think realistically there 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 I think the positive benefits can be amazing, but we need to take it seriously if we're gonna navigate this well. That's Shane Leg, co-founder and chief AGI scientist at Google DeepMind. Shane Lake, 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 Ramtina Erablui. It was edit by John Isabella with research help from Carla Esteves. Our audio engineer was Cina Lafredo. Our production staff also includes Alex Chung, Casey Herman, Chris Massini, Carrie Thompson, Chase Howard, Mia Agadello, Neva Grant, and Catherine Cypher. I'm Guy Raz, and you've been listening to how I built this lab.

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