The Godmother of AI on jobs, robots, and why world models are next | Dr. Fei-Fei Li

0:00 Lotta people call you the godmother of AI. The work you did actually was the spark that brought us out of AI winter. In the middle of twenty fifteen, middle of twenty sixteen. Some tech companies avoids using the word AI because they were not sure if AI was a dirty word. Twenty seventeen ish was the beginning of conversation. Companies calling themselves AI companies. There's this line I think this was when you're presenting to Congress. There's nothing artificial about AI. It's inspired by people, it's created by people, and most importantly it impacts people. It's not like I think AI will have no impact on jobs or people. In fact, I believe that Whatever AI does currently or in the future is up to us. It's up to the people. I do believe technology is a net positive for humanity, but I think every technology is a double edged sword. If we're not doing the right thing as a society, as individuals, we can screw this up as well. You had this breakthrough insight of just okay, we can train machines to think like humans, but it's just missing the data that humans have to learn as a child. I chose to look at artificial intelligence through the lens of Visual intelligence because humans are deeply visual animals. We need to train machines with as much information as possible on images of objects. But objects are very, very difficult to learn.

1:20 A single object can have infinite possibilities that is shown on an image. In order to train computers with tens and thousands of object concepts, you really need to show it millions of examples. Today, my guest is Dr. Fei Fei Li, who's known as the godmother of AI. Feife has been responsible for and at the center of many of the biggest breakthroughs that sparked the AI revolution that we are currently living through. She spearheaded the creation of ImageNet, which was basically her realizing that AI needed a ton of clean labeled data to get smarter. And that data set became the breakthrough that led to the current approach to building and scaling AI models.

2:01 She was chief AI scientist at Google Cloud, which is where some of the biggest early technology breakthroughs emerged from. She was director at Sale, Stanford's artificial intelligence lab, where many of the biggest AI mines came out of. She's also co creator of Stanford's Human Centered AI Institute, which is playing a vital role in the direction that AI is taking. She's also been on the board of Twitter. She was named one of Time's one hundred most influential people in AI. She's also on the United Nations advisory board, I could go on. In our conversation, Fei Fei shares a brief history of how we got to today in the world of AI, including this mind blowing reminder that

2:37 Nine to ten years ago. Calling yourself an AI company was Basically a death knell for your brand. Because no one believed that AI was actually gonna work. Today it's

2:46 Completely different. Every company is an AI company. We also chat about her take on how she sees AI impacting humanity in the future. How far current technologies will take us. Why she's so passionate about building a world model and what exactly world models are. And most exciting of all, the launch of the world's first large world model, Marble, which just came out as this podcast comes out.

3:08 Anyone can go play with us at marble dot world labs dot ai. It's insane. Definitely check it out. Fei Fei is incredible and way too under the radar for the impact that she's had on the world, so I am really excited to have her on and to spread her wisdom with more people. A huge thank you to Ben Horowitz and Condoleza Rice. For suggesting topics for this conversation.

3:27 If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. With that, I bring you Doctor Fei Fei Li. After a short word from our sponsors. This episode is brought to you by Figma, makers of Figma Make. When I was a PM at Airbnb, I still remember when Figma came out.

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4:33 Make code-backed prototypes and apps fast with Figma Make. Check it out at Figma dot com slash Lenny. Did you know that I have a whole team that helps me with my podcast and with my newsletter? I want everyone on my team to be super happy and thrive in the roles. JustWorks knows that your employees are more than just your employees. They're your people. My team is spread out across Colorado, Australia, Nepal, West Africa, and San Francisco.

4:58 My life would be so incredibly complicated, to hire people internationally, to pay people on time and in their local currencies, and to answer their HR questions 24-7. But with JustWorks, it's super easy. Whether you're setting up your own automated payroll, offering premium benefits, or hiring internationally, Just Works offers simple software and 24-7 human support from small business experts for you and your people. They do your human resources right, so that you can do right by your people. Just works. For your people. Fay Faye, thank you so much for being here and welcome to the podcast.

5:34 I'm excited to be here, Lenny. I'm even more excited to have you here. It is such a treat to get to chat with you. There's so much that I wanna talk about. You've been at the center of this AI explosion that we're seeing right now for so long.

5:47 We're gonna talk about a bunch of the history that I think a lot of people don't even know. About how this whole thing started. But let me first read a quote from Wired about you, just so people get a sense. And in the intro, I'll share all of the other epic things you've done, but I think this is a good way to just set context. Fay Fey is one of the a tiny group of scientists, a group perhaps small enough to fit around a kitchen table. who are responsible for AI's recent remarkable

6:09 advances. A lot of people call you the godmother of AI. And Unlike a lot of AI leaders, you're an AI optimist. You don't think AI is gonna replace us, you don't think it's gonna take all our jobs, you don't think it's gonna kill us.

6:22 So I thought it'd be fun to start there. Just what's your perspective on how AI is going to impact humanity? Over time. Yeah, okay, so Lenny, let me be very clear. I'm not a utopian. So it's not like I think AI will have no impact on jobs or people, in fact.

6:40 I'm a humanist. I believe that Whatever AI does. In currently or in the future is up to us. It's up to the people.

6:50 So I do believe technology is a net positive. For humanity, if you look at the long course of civilization. I think we are an Fundamentally we're an innovative species.

7:04 That we You know, if you look at from You know, written record. thousands of years ago um to to now. Humans just kept innovating ourselves and innovating our tools.

7:17 And with that, we make lives better, we make work better, we build civilization. And I do believe AI is part of that. So that's where the optimism comes from. But I think every technology is uh is um a double edged sword and uh

7:35 If we're not doing the right thing. А за спіші As a society As communities, as individuals. We can screw this up as well.

7:46 Mm. There's this line I think this was when you're presenting to Congress. There's nothing artificial about AI. It's inspired by people, it's created by people, and most importantly, it impacts people. Uh I don't have a question there, but what a what a great line. Yeah, I I f I feel pretty deeply I

8:02 You know, I started um working AI two and a half decades ago, and I've been having students for the past two decades. And almost every student who graduates I remind them. you know, when they graduate from my lab that

8:18 Your field is called artificial intelligence, but there's nothing artificial about it. Coming back to the point you just made about how it's kind of up to us about where this all goes. What is it you think we need to get right? H how do we set things on a path? I know this is a a very difficult question to answer, but just what should What what's your advice? What do you think we should mind? How many hours do we have? How do we align AI? There we go. Let's solve it. I think people should be responsible individuals no matter what we do. This is what we teach our children and this is what we need to do.

8:51 as grown ups as well, no matter which part of the AI development or AI deployment or or AI application you are participating in. And most likely many of us, especially as technologists, were we in multiple points We should act like responsible individuals and uh And care about this. Actually care a lot about this. I think everybody today should care about AI because

9:20 It is going to impact your individual life. It is going to impact your community, it's gonna Has the The society and the future generation. Um

9:30 Caring about it as a responsible person. Is The first but also the most important step. Okay, so let me let me actually take a step back and kinda go to the beginning of AI. Most people started hearing and caring about

9:45 AI. Is what it's called today. Just like I don't know, a few years ago when ChatGPT came out, maybe it was like three years ago. Three years ago, almost uh one more month. Wow. Okay. That was JAT GPT coming out. Is that the milestone you have in mind? Okay, cool. That's exactly how I saw it. But very few people know there is a long, long history of people working on it was called machine learning back then and there's other terms, and now it's just everything's AI. And there was kind of like a long period of just a lot of people working on it. And then there's this what people refer to as the AI winter, where people just gave up. Almost most people did and just

10:16 Okay, this I but this idea isn't going anywhere. And then the work you did actually was essentially the spark that brought us out of AI winter and is directly responsible for the world where now of just AI is all we talk about. As you just said, it's gonna impact everything we do. So I thought it'd be really interesting to hear from you, just kind of like the brief history. Uh

10:35 What the world was like before ImageNet. And just the work you did. to create image net, why that was so important, and then just what happened after. It is for me hard to keep in mind that AI so new for everybody.

10:50 Well I lived my entire professional life in AI. It's there's A part of me that is just It's so satisfying to see. A personal curiosity that I started

11:04 Barely out of teenagehood. and and now has become a Transformative. force of our civilization. It generally is a civilizational level uh technology so

11:17 So that journey is about About thirty years or twenty something, twenty plus years, and uh It's it's just Very satisfying.

11:27 So where did it all start? Well, I'm not even the first generation AI researcher. The first generation really dates back to the fifties and sixties and you know, Alan Turing was ahead of his time. by in the forties by asking Daring humanity with the question can we Is their thinking machines, right?

11:48 And of course he has a specific way of uh testing this concept of thinking machine which is A conversational Check out. Which

11:58 To his standard, the We now have a thinking machine. But Uh that was just a more Anecdotal.

12:06 Inspiration The field really began in the fifties, um when computer scientists came together and look at How we can Use computer програms and algorithms. To uh

12:20 To build these programs that can do things That Have been only incapable by human cogniz. So um

12:30 And and that was the beginning in the founding fathers, the Dartmouth uh workshop in the nineteen fifty six. You know, we have Professor John McCarthy who later came to uh Stanford who coined the term artificial intelligence. And between the fifties, sixties, seventy's and eighties. It was the early days of AI exploration.

12:53 And we had logic systems, we had uh Exper systems We also had Early exploration of your network. And then it came to

13:04 Around The late eighties, the nineties and the the very beginning of the twenty first century. that stretch about twenty years is actually the beginning of machine learning. It's the marriage between Computer programming and statistical

13:22 as uh learning. And that marriage brought a very, very critical concept. into AI, which is that Purely rule based

13:35 Um a program is not gonna account for the vast amount of cognitive capabilities that we imagine computers can do. So we have to use machines to learn the patterns.

13:51 Once the machines can learn the patterns. It has the hop. to do war things. For example If you give it Three cats.

13:59 The hope is not just for the machines to recognize these three cats. The hope is the machines can Recognize the fourth cat, the fifth cat, the sixth cat. And all the other cats. And that's a learning ability. That is

14:13 Fundamental to humans and meaning animals. And we as a field realize We need machine learning. So that was up till the beginning of The twenty first century.

14:26 I entered the field of AI literally in the year of two thousand. That's when my uh PhD began at Caltech. And so I was one of the first generation machine learning researchers. And we were already studying This concept of machine learning, especially can your network. I remember that was one of my first courses. in uh a cartake is called neural network.

14:49 But it was very painful. It was still smack in the middle of the so called AI winter, meaning the public didn't look at this too much. There wasn't that much funding. But there was also a lot of ideas flowing around. And I think two things happened to myself.

15:07 that brought my own career So close to the birth of modern AI. Is that um I chose to look at Artificial intelligence through the lens of visual intelligence.

15:19 Because uh humans are Deeply visual animals. We can talk a little more later, but so much of our intelligence.

15:29 is built upon visual perceptual spatial understanding not just language per se I think they're complementary So I chose to look at visual intelligence and um my PhD and my early Uh, Professor Yers I um

15:45 My students and I are very committed to a North Star problem, which is solving the problem of object recognition. because it's a building block. For the perceptual world, right? We go around the world. interpreting reasoning and interacting with it. More or less

16:02 at the object level. We don't interact with the world at the molecular level. We don't interact with the world as So um We sometimes do, but we rarely, for example, if you wanna lift a tea pot.

16:15 You don't say okay the tea pot is made of a hundred pieces of porcelain and let me work on these a hundred pieces. You look at it as as one object and and interact with it. So object is really important. So Um I was among the first uh uh researchers to identify this as a north star problem. But I think what happened is that

16:39 As a student of AI, and a researcher of AI. I was working on all kinds of mathematical models. including your network, including Bayesian network, including many Many models. And there was one singular pain point.

16:56 Is that these models don't have data to be trained on. And uh As a field we were so focusing on these models, but It dawned on me. The human

17:07 Learning. As well as the evolution. Is Actually, a big data learning process. Humans learn with so much experience. you know, constantly in the evolution if you look at time

17:21 Animals evolve with just Experiencing the world. So I think my student and And I conjectured. That

17:30 uh very critically overlooked ingredient of bringing AI to life. Is big data. And then we began this image that project in two thousand six, two thousand seven. We were very ambitious. We wanna Get the entire internet's image data on objects.

17:49 Now, granted, Internet was a lot smaller than today. So we I felt like that ambition was at least Not too crazy. No, it's totally delusional to uh To think a couple of graduate student and a professor can do this. But uh And that's what we did. We curated very carefully fifteen millimetes on the internet.

18:12 created a taxonomy of twenty two. Thousand concepts. Borrowing. Other researchers work like uh linguists work on Wordnet and it's a particular way of uh dictionarying.

18:26 uh words. And we combine that. into image that and we open source that to the research community we held an annual image, that challenge to

18:39 Encourage everybody to participate in this. We continue to do our own research. А тві твомент. that many people think was the beginning of the deep learning or birth of modern AI. Because a group of Toronto researchers led by Professor Jeff Hinton

18:57 participated in ImageNet Challenge, use the Image Net big data. And two GPUs from Nvidia. And created successfully the first neural network algorithm That's can It didn't fundamental it didn't hold totally solve, but made a huge progress.

19:16 towards solving the problem of object recognition. And that combination of the tree technology. uh big data neuron and GPU. Воз

19:29 kind of the golden recipe for modern AI. And then Fast forward the the the public moment Of

19:38 AI. Which is the Chad GPT moment. If you look at the ingredients of what brought Chad Gt to to the

19:49 technically is still use these three ingredients. Now it's internet scale data, mostly text. Is A much more comp complex uh neural network um architecture than

20:03 twenty twelve, but it's still neural network. And a lot more GPUs, but It's still GPU, so these three ingredients. are still to the at the core of modern AI. Incredible. I have never heard that

20:18 Full story before. I love that it was two GPUs was the fur I love that. Yes. Uh and now it's I don't know, hundreds of thousands, right? That are uh orders of magnitudes more powerful. Uh and those two GPUs were they just bought they were like gaming GPUs. They just went to the like the game store, right? That people use for playing games. As you said, this continues to be in a large way the way models get smarter. Some of the fastest growing companies in the world right now I've had them all mostly on the podcast, Mercor and Surge and Scale.

20:47 Like they do this, they continue to do this for labs. Just give'em more and more label data of the things they're most excited about. Yeah, I remember um Alex uh Wong from Scale very early days, I probably still has his emails when he was starting Scale, he uh He was very kind. He keeps s sending me emails about How you mentioned that inspired scale. I was very pleased to see that. One of my other favorite takeaways from what you just shared is just such an example of

21:13 high agency and just doing things. That's kind of a meme on Twitter. Just you can just do things. You're just like, Okay, this is Probably necessary to move AI and It's called machine learning back then, right? Was that the term most people used? I think it was interchangeably it's true, like

21:28 I do remember the companies, the tech companies I I'm not gonna name names, but I was I was uh In a conversation in one of the early days I think it is in the

21:39 Middle of twenty fifteen, middle of twenty sixteen. uh some tech companies avoids using the word AI because they were not sure if AI was a dirty word. And I remember I was actually Encouraging everybody to use the word AI because To me, that is one of the most Audacious question humanity has ever asked.

22:03 In our quest for science and technology, and I feel very proud of this term, but Yes, uh at the beginning some people were not sure. What year was that roughly when AI was dirty work? Sixteenth. I think that was Less than ten years ago.

22:20 Um some people start calling it AI. But I think if you look at the Silicon Valley tech company. Companies if you trace their marketing term.

22:31 I think Twenty seventeen ish was the beginning of companies calling themselves AI companies. That's incredible. Just how The world has changed. Yeah. Now you can't not call yourself an AI company.

22:48 Yeah. Oh man. Okay. Is there anything else around the history that early history that you think people don't know, that you think is important before we Chat about where you think things are going and the work that you're doing.

23:01 I think as all histories, you know, I'm keenly aware that uh I am recognized for being part of the history, but there are so many heroes and so many researchers. We're talking about generations. of researchers there. Іно і май о со мні. who have in z inspired me, which

23:22 I I talked about in my book. But I do feel Our culture, especially Silicon Valley. Tends to A sign

23:31 Um Achievements. To a single person. Well while I think it has value. Um but it's

23:39 It's just to be remembered AI is a field of At this point, seventy years old. And we have gone through many generations. Um nobody. No one.

23:50 Um could have gotten here by themselves. Okay. So let me ask you this question. It feels like we're always on this.

23:57 precipice of A GI this kind of vager people throw around and A GI is coming, it's gonna Take over everything. How What's your take on how far you think we might be from AGI? Do you think we're gonna get there? on the current trajectory on do you think we need more breakthroughs? Do you think the current

24:11 Approach will get us there. Yeah, this is a very interesting term, Lenny. Um I don't know if anyone has ever defined AGI. You know, there are many different definitions, including

24:27 you know, some kind of superpower for machines all the way to Can um Machines can become economically viable. agent in in the society Uh uh

24:40 In other words, making salaries to live. Is that the definition of AGI? А за сієнтіст, аїс ви серісли. And I enter the field because I was inspired by this audacious question of Machines.

24:57 Think and do things. In the way that human cop humans can do. Forмі, that's always the North Star of Ai. From that point of view, I don't know what's the difference between AI and AGI.

25:10 I think we've done Very well. In achieving parts of the goal. including conversational AI But I don't think we have completely conquered

25:21 All the goals. uh of of AI. And I think our founding fathers, L Alan Thuri I wonder if Alan Turing is around today and you ask him to contrast AI versus AGI. He might just shrugg and said Well I asked the same question back in nineteen forties, so

25:41 So I don't wanna g get onto a rabbit hole of defining AI versus AGI. I feel AGI is more a marketing term. And then a scientific term. А за сайтіст та технологіст.

25:55 AI is my North Star. Feels North Star and I'm happy people call it whatever name they want to call it. So let me ask you maybe maybe this way. Like you described, there's kind of these components that from ImageNet and Alexnet kind of took us to where we're today.

26:13 GPUs essentially. Data, label data. Just like the algorithm of the model. There is also just the transformer feels like an important step in that. trajectory.

26:24 Do you feel like those are the same components that'll get us to I don't know, ten times smarter model something that's like life changing for the entire world. Or do you think we need more breakthroughs? I know we're g we're gonna talk about world models, which I think is a Component of this, but Is there anything else that you think is like oh

26:39 This little plateau or okay, this will take us just need more data, more compute, more GPUs. Oh no, I definitely think we need more uh innovations. I I think scaling loss of more data, more GPUs and Bigger Current model architecture.

26:55 Is there's still a lot to be done there. But I absolutely think we need to innovate more. Um there's not a single Deeply scientific. discipline in human history.

27:07 That has arrived at a place that says we're done we're done innovating and AI is Wa one of the if not the youngest discipline in in human civilization in terms of science and technology we're still scratching the surface.

27:24 Uh, for example Um Like I said, we're gonna segue into world models. Today you take a A model. And and

27:33 And run it through a a video of A couple of office rooms. And ask the the model to count the number of chairs. This is something a Taldenberg could do, or maybe maybe a a a uh elementary school kid could do.

27:48 And AI could not do that, right? So um there's just so much AI today could not do. Then let alone thinking about How did you know Um Someone like Isaac Newton.

28:01 Look at the movements of the celestial bodies. А андер на екван. or or set of equations. that governs the movement of all bodies. That level of creativity, extrapolation, abstraction

28:18 We have no way of Enabling AI to do that today. And then let's look at emotional intelligence. If you look at A student come into a teacher's office and have a conversation about motivation, passion, what to learn, what's the

28:36 problem that's the That's you know really uh bothering you. That конверсін. As powerful as

28:44 as today's conversational bots are You don't get that level of emotional Cognitive intelligence. Uh from today's AI. So There's a lot we can do better, um

28:57 And I do not believe work on innovating. Uh Demis had this really interesting interview recently from DeepMind slash Google. Where if someone asked him just like, What do you think? Yeah. How far away from A GI? What does it look like when through there? He had a really interesting way of approaching it is

29:11 If we were to give um the m most cutting edge model all the information until the end of The twentieth century. See if it could come up with all the breakthroughs Einstein had. And so far we're never near that. No, we're not in fact.

29:24 It's even worse. Let's give A I All the data, including modern instruments data of celestial bodies, which Newton did not have. And give it to that and just ask AI to create the six seventeenth century.

29:41 set of equations on the laws of Well. Body movements. Uh today's AI can all do that. Mm.

29:49 All right, we're ways away, is what I'm here. Okay, so let's talk about world models. This is uh to me this is just another really Amazing example of you being ahead of where People And uh

30:00 So you were way ahead on okay, we just need a lot of clean data for AI and neural networks to learn. Uh, you've been talking about this idea of world models for a long time. You started a company to build uh essentially there's language models. This is a different thing. This is a world model, we'll talk about what that is. And now uh as I was preparing for this, Elon's like talking about world models, Jensen's talking about world models. I know Google's working on this stuff. You've been at this for a long time.

30:24 And you actually just launched something that's gonna you're we're gonna talk about uh Right before this podcast airs. Um Talk about what is a world model, why is it so important. I'm very excited to see that more and more people are talking about world models like Yelan, like Jensen.

30:41 Um I have been thinking about Really, how to push AI forward all my life, right? And The large language models uh that came out of

30:55 Uh the research world and then open AI and and all this. For the past few years. What Extremely inspiring, even for a researcher like mi.

31:07 I remembered when GPT Two came out That was in I think late two thousand Twenty?

31:15 I was um Co director um I still am, but I was at that time full uh full time co director of Stenphers uh human center AI інститут. And I I remember it was You know, the public was not aware of the power of the large language model yet.

31:33 But as researchers, we were seeing it. We're seeing the future. And I had pretty long conversations with my naturage processing colleagues. Like Percy Leo and Chris Manning, we were talking about

31:46 How critical This technology is gonna be And the Stanford uh AI institute, human center AI institute, HI was the first one to establish a full Research center.

31:58 on foundation model. We were Percy Long and And many researchers let the first uh academic paper um foundation model so So it was just very inspiring for me. So of course

32:11 I come from the world of Visual intelligence. And I was just thinking there's so much we can um Push forward. on beyond language because

32:21 Humans Oh. Humans Have use our sense of spatial intelligence a world understanding to do so many things.

32:33 And they are beyond language. Think about a very chaotic first responder scene. Whether it's fire or some traffic accident or or some natural disaster.

32:47 And It's If you Immerse yourself in the scene and think about how people organized themselves to to rescue people to stop further disasters to

33:00 Poo down fires to to A lot of that is movement is Spontaneous understanding of Objects, worlds, h human um

33:12 situation awareness Language is part of that, but a lot of those situations w language cannot get you to put down the fire. So that is what is that? I I was thinking a lot, and in the meantime, I was doing a lot of robotics research. And I it came it dawned on me. That the linchpin

33:34 Of connecting The additional intelligence in addition to language And connecting Embodied AI, which are robotics. Connecting visual intelligence.

33:48 Із сфер интоліз. About Understanding the world. And that's when Um, I think I um

33:56 It was twenty twenty four. I gave a TED talk. About spatial intelligence a world models. And uh I start Formulating this idea. uh back in twenty twenty two, um based on my robotics and computer vision research.

34:14 And then one thing that was really clear to me. Is that I really want to work with the brightest Uh Technologist?

34:23 Um And move as fast as possible to bring this technology to life. And that's when we founded this company called World Labs. And you can see the the the word world is in the title of our company because we believe so much in World modelling and spatial intelligence. People are so used to just chat bots and that's a large language model. The simple way to understand a world model is you

34:46 basically describe a scene and it generates an infinitely The uh explorable world. the thing you launch, which we'll talk about. But just is that a simple way to understand it? That's part of it, Lenny. I think a simple way to understand a world model Uh is that

35:02 This model can allow anyone to create Any worlds in their mind's eye. By prompting Whether it's an image or sentence. And also be able to interact in this world.

35:18 Whether you're browsing and walking or or picking objects up or or or changing Changing things. Uh Азва to reason within this world, for example, if

35:32 If the person consuming if the agent consuming this output of the world model is a robot. It should be able to plan its path and And the help to W you know

35:45 tidy the kitchen, for example. So so world model is A A foundation. That's That you can use.

35:55 to reason to interact and to create Worlds. Great. Yeah. So robots feels like that's Mm, potentially the next big focus for AI researchers and just like the impact on the world and

36:09 what you're saying here is uh this is a key missing piece of making robots. actually work in the real world, understanding how the world works. Yeah. Well first of all, I do think there's more than robots that's exciting. Um so but I agree with everything you just said. I think uh world modeling and spatial intelligence is a key missing piece of uh uh embody I also think let's not underestimate that.

36:36 Humans are embodied agents. And humans can be augmented. by AI's uh intelligence. Just like today humans are languals, but were very much augmented by AI when helping us to, you know. do language tasks including software engineering. I I think that uh

36:57 We shouldn't underestimate or maybe it's It's Um We tend not to talk about How humans as an embodied ад.

37:06 can actually benefit so much from world models and spatial intelligence um models as well as robots can So the big unlocks here robots. Which uh A huge deal. If this works out, I imagine each of us has robots doing a bunch of stuff for us, goes into you know, they help us with

37:24 disasters, things like that. Uh games obviously is a really cool example, just like Infinitely playable games that you just invent out of your head. And then Creativity feels like just like being fun, having fun, being creative, thinking of magic. wild new worlds and environments.

37:39 And also design. Humans design from machines to buildings to homes. And also scientific discovery, right? There is so much Um I I like to use the example of the discovery of the structure of DNA. If you look at one of the most important

37:58 piece in uh DNA's discovery history. is the X ray diffraction photo. That was captured by Rosalind Franklin. And it was a flat two D photo of a structure that looks like It looks like a cross with

38:14 D uh diffractions. You can you can uh Google those photos. But with that Two D flat. Photo.

38:23 humans, especially two important humans, James Watson and Francis Crick. In addition to their other information. was able to reason In three D space.

38:36 And Duse A highly three dimensional double helix structure of the DNA, and that structure. cannot possibly be too deep. You cannot

38:49 Think in two D and deduce that structure. You have to think in Свиді спейшо. Um Use the the human spatial intelligence. So I think even in scientific discovery.

39:02 um spatial intelligence or AI Assistance spatial intelligence is critical. This is such an example of of I think it was Chris Dixon that had this line that the next big thing Is gonna start off feeling like a toy. when ChatGPT just came out, if like I remember Sal Moment just tweeted it's like Here's a cool thing we're playing with, check it out.

39:21 Now it's the fastest growing product all of history changed the world. Yeah. Uh And it's oftentimes the things that just look like, Okay, this is cool. Uh That it's fun to play with and end up changing the world most.

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40:27 Cinch is already helping major brands send RCS messages around the world. And they're helping Lenny's podcast listeners get registered first, before the rush hits the US market. Lear more at get started at cinch.com/slash Lenny. That's S-I-N-C-H. Dotcom Slash Lenny. I reached out to Ben Horowitz, who

40:47 Loves what you're doing, a big fan of yours. Uh they're investors I believe in. Yeah. We we've known each other for for many years. But yes, right now they're investors of uh Warlapse. Amazing. Okay. So I asked him what I should ask you about, and he suggested ask you why is the bitter why is the bitter lesson alone not Likely to work. For robots.

41:08 So first of all, just explain what the bitter lesson was. in the history of AI and then just why that won't get us to where we want to be with robots. So well first of all there are many bitter lessons. But the bitter lessons everybody refers to is a uh It's a paper written by Richard Sutton, who won the Turing award recently.

41:29 And he does a lot of reinforcement learning and Richard has said, right, if you look at the the history, especially the algorithmic development of AI. It turns out simpler model with a ton of data. Always win.

41:44 At the end of the day, instead of The the um The you know, more complex model with less data. I mean that was actually This paper came years after image that that to me was

41:59 Not bitter. It was a sweet lesson. That's why I built uh image that because I believe that uh Big data plays that role. So Why a can bitter lesson work in robotics along

42:12 Well first of all, um I think we need to give credit to Where we are today. Robotics is very much in the early days of Experimentation. It's not the the research is not nearly as mature as, say, language models.

42:30 So Many people are still um Experimenting with different algorithms, and some of those algorithms are driven by big data. So I do think big data will continue to play a role.

42:45 In robotics. And um But what is hard? For robotics, there are a couple of things. One is that

42:55 It's harder to get data. It's a lot harder to get data. You can say, Well, there's web data. This is where The latest robotics research is Using web videos, and I think web videos do do play a role. But if you think about what may language model worth a very

43:14 As someone who does computer vision and and spatial intelligence and robotics, I'm very jealous of my colleagues in um in language because They had this perfect Set up. Where their training data are in Words eventually tokens.

43:31 And then Thank you Produce a model? The Alpha's words. So you have this perfect alignment between

43:40 What you hope to get, which we call objective function. And what your training data looks like. But robotics is different. Even spatial intelligence is different. You will hope to get actions out of robots.

43:56 But your training data lacks Actions. In three D worlds. And that's what robots have to do, right? Actions in three D worlds. So you have to um

44:08 Find different ways. Two fit a uh what do they call a a a a s Square in a round hole. That's the thing.

44:19 What we have is tons of web videos. So then we have to start talking about Oh Adding, supplementing Data such as

44:30 Телеопераційн да or synthetic data. Со да робот артран. With this Hypothesis of Bitterless, which is

44:40 Large amount of data. I think there's Still hope? Because even what we are doing Um in world modeling.

44:48 Will really unlock a lot of this information for robots. But I think we have to be careful because we're at the early days of this. And bitter lesson is still to be Test it? Uh because we haven't

45:03 fully figure it out. the data. Four. Another part of the bitter lesson of robotics I think we should be so So realistic about is

45:15 Again, compared to language models or even spatials. Robots are physical systems. So Robots are closer to self driving cars. than a large language model.

45:28 I mean that's very important to recognize. That means that Інодер фортворк. We not only need brings. We also need the physical body. We also need

45:42 Аплікачин сценаріоз If you look at the the the the the history of self driving car Um my colleague Sebastian Thrum uh uh to stal's car

45:54 uh to win the first starpa challenge in two taus or two taus It's twenty years since that. Prototype. of a self driving car.

46:07 Би небо то драйв а Хандер Терті Майс. І на Навада дезер. To today's Waymo. And um А на стріт в Сан Франціско.

46:17 And we're not even done yet. There's still a lot. So that's a twenty year journey. And self-driving cars are much simpler robots. They're just metal boxes running on two D surfaces. And the goal is not to Touch anything. Robot is

46:33 Three D Things. running in three D world and the goal is to touch things. So The dirty is gonna be

46:42 You know, there's many aspects. Elements. Um Of course one could say Well

46:48 The self driving car early algorithm were pre deep learning era. So deep learning is accelerating. uh the brains. And I think that's true. That's why I mean robotics. That's why I mean spatial intelligence and I'm excited by it. But in the meantime, the car industry is very mature.

47:07 And Productising. also involves the mature Use cases, supply chains, hardware. So I think it's a very interesting time to work in these problems.

47:20 But it's true, Ben is right. We may Still. Be subject to A number of bitter lessons.

47:28 Doing this work, do you ever just feel off for the way the brain works and is able to do all of this. for us just the complexity just to get a A machine to just walk around and not hit things and fall. Does it just give you more respect for what we've already got? Totally. We we operate on

47:47 About twenty watts. That's dimmer than any light bulb in in the room I'm in right now. And Yet we can do so much. So I think Actually the more I work in AI, the more I respect humans.

48:03 Let's talk about this uh Product you just launched called Marble, a very cute name. Talk about what this is, why this is important. I've been playing with it. It's incredible. We'll link to it and for folks to check it out. What is marble? Yeah, I'm very excited. So first of all, Marbo is uh one of the first product that World Apps uh has rolled out.

48:22 Warm Labs is a foundation frontier model company. We are funded by four co founders who have deep technical history. Michael Thunders, Justin Johnson Uh Christoph Uh Lasner and Ben Mildenhall. We all come from

48:38 the research field of AI, computer graphics, computer vision. And uh we believe that spatial intelligence and world modeling is as important, if not more, to uh language models and uh complementary. to to language model. So we wanted to

48:56 Sees this opportunity to create deep uh tech research lab that can connect The dance between Um frontier models with products. So

49:09 Marble is An app. That's built upon our frontier models. We've spent a year and plus Building the world's first uh generate a model that can output

49:24 Genuinely Three D worlds that's a very, very hard problem. Um And uh And

49:31 It was a very hard process. We uh We have a team of incredible founding team of incredible technology from You know Incredible uh uh teams. And then around um

49:48 A month or two ago We saw the first time. That we We can just prompt with a sentence and image. and multiple images and create

50:00 Worlds that we can just navigate in yeah if you put it on goggle, which we have an option to let you do that. You can't even walk around, right? So it was Even though we've been building this for for

50:13 quite a while. It's was still just awe inspiring. And we wanted to get into the hands of uh people who need it. А ну винода со мені крейтер дізайнер. People who are thinking about

50:27 uh robotic simulation people who are thinking about different use cases of uh navigable, interactable Um Immersive worlds game developers

50:39 Well find this useful. So we uh develop developed Marble as a first step. It's it's again still very early. Uh but it's the world's first uh model doing this and it's the world's first uh product. that allows people to just uh

50:57 Prompt. We call it prompt to world. Well, I've been playing around it it is insane. Like you could just have a little shire world where you just infinitely walk around Middle earth basically, and there's no there's no one there yet. But uh it's insane. You just go anywhere, there's like dystopian world. I'm just looking at all these examples. Yes. Uh and my favorite part actually, I don't know I don't know if there's a feature or bug. You can see like the dots of the world.

51:20 before it actually renders with all the textures. And I just love to like you get a glimpse into what is going on with this model. Because this is where as a researcher I've I I'm learning. Because the the the the dots that lead you into the world

51:38 Was uh An intentional feature uh візуаліzation It w is not part of the motto. It's uh the motto actually just generates the world. But we we were trying to find a way to guide people into the world.

51:54 And a number of engineers uh work on different versions. But we converged on the dot and So many people, you're not the only one. Told us how delightful that experience is. And it it was really satisfying for us to hear

52:10 That this intentional visualization feature that's Not just the big hardcore model. Actually has delighted our users. Wow. So you add that to make it more uh Like to have humans understand what's going on more get more delightful.

52:25 Wow, that is hilarious. It makes me think about a lems in the way they it's not the same thing, but they talk about what they're thinking and what they're doing. It is. It is. It also makes me think about just the matrix. Like it's exactly the Matrix experience. I don't know if that was your inspiration. Um well, like I said, a number of engineers worked on that. It could be there in inspiration. It's in their uh

52:48 It's in their subconscious. Yeah. Okay, so just for folks that may wanna play around with us, maybe use it, what's like what are some applications today that folks can start using today. What's what's your goal with this launch? Yeah, so um we do believe that world modeling is very horizontal. But we're already seeing some really exciting uh use cases.

53:08 Virtual production. For movies. Because what they need are three D Uh World.

53:15 that they can align with the camera. So when the actors are acting on it uh they can, you know, they can uh position the camera and shoot the the segments really well and uh We're already Seeing um incredible use. In fact

53:32 I don't know if a you have seen our launch video showing Marble. It was produced by a virtual uh production company. We we collaborated with Sony. And they use marble things to shoot those videos, so Oh we were collaborating with those uh uh technical artists and directors and they were saying this has cut our

53:54 uh production time by Forty X In fact it has to be X. Yes. In fact it has to because we only had one month to work on this project and uh and there were so many things they were trying to shoot. So So using marble really, really significantly accelerated

54:14 the production of virtual virtual production for VFX and movies. That's one use case is we are already seeing our users Putting uh Taking our marble thing and taking the mesh export. And putting games.

54:28 You know, whether it's games on VR or games uh Just just just fun games that they they have developed. We have had um We s we're showing uh An example of uh

54:41 Robotic similation because Uh when I was I mean I'm still am a researcher doing robotic uh training. One of the biggest pain point is to create synthetic data for training robots.

54:56 And these synthetic data needs to be very diverse. They need to come from different environments with different objects to manipulate. And uh And one path to it. Is is to ask com uh computers to simulate.

55:10 Otherwise humans have to You know. uh build every single asset for robots. That that's just gonna take A lot longer. So we already have researchers reaching out and wanting to use marble to create those synthetic environments.

55:26 We also have unexpected um Uzer uh outreach In terms of uh how they wanna use Marbot. For example

55:37 А психологіст тім колтас. To use marble to do psychology research, it turned out some of the psychiatric patients they study They need to

55:49 Understand how their brain responds to different immersive things. of different features, uh, for example, messy things or cleansings or or whatever you name it. And it's very hard for researchers to get their hands on um these kind of immersive things and it will take them

56:09 too much budget to uh to to create. And Marble is a really almost instantaneous way. Of

56:19 Getting so many of these um eksperimental uh environments into their hands. So we're seeing um Uh, we're seeing multiple use cases at this point, but the

56:31 The VFX, the game developers, the simulation. Uh uh developers as well as designers are very excited. This is very much the way things work in AI. I've had other AI leaders on the podcast and it's always like Put things out there early, as soon as you can to discover where the big use cases are.

56:48 the head of Chat GPT told me how when they first put out Chad GPT He was just scanning TikTok to see how people were using it and all the things they were talking about, and that's what convinced them more to lean in and And help them see how people actually want to use it. I love this last use case of like for therapy. I'm just imagining like

57:05 dealing with heights or snakes or spiders, which It's amazing. A friend of mine last night literally called me and talked about his height scare and asked me if Marble should be used. That's amazing you went straight there. That's that's you know,'cause I'm imagining all the like the exposure therapy uh stuff. Like this could be so good for that. Uh That is so cool.

57:31 Okay, so let me uh I should have asked you this before, but I think There's a qu there's gonna be a question of just how does this differ from things like VO three? and other video generation models. It's Pretty clear to me, but I think it might be helpful just to explain how this is different from all the video AI

57:44 Tools people have seen. Warlap's thesis is that spatial intelligence is fundamentally very important, and spatial intelligence is not just Uh uh it's not just about videos. In fact the world is not passively watching videos passing by, right? Um

58:04 I I love uh Plato has the allegory of the cave analogy. To describe vision, he said that. Imagine A prisoner tied on his chair. uh not not very uh humane but um

58:21 Uh In fr in a cave. Watching a full Life Theatre.

58:28 Uh On the in front of him. But the actual live theater that actors are acting is behind his back. It was just lit.

58:37 So that the projection of the theat uh the action. is on a on a wall of the cave. And and then The goal the the the task of this prisoner is to figure out what's going on.

58:50 It's a pretty extreme example, but it really shows uh it describes What Vision Is about is that to make sense of the three D world or four D world out of two D.

59:05 So Spatial intelligence to me. Is deeper than Only creating. That flat

59:13 Two D world. Spatial intelligence. to me is the ability Two Great.

59:20 Reason. Interact makes sense. Of deeply spatial world, whether it's two D Or three D.

59:29 Or four D. including dynamics and all that so So World Lab is focusing on that, and of course. Um the ability to create Videos per se.

59:40 could be part of this, and in fact uh Just a couple weeks ago we rolled out the world's first uh Real time Demoble. real time video generation on a single

59:52 uh H one hundred GPU. So we We We part of our technology includes that. But I think Marble is very different because We really want

1:00:02 Craters. Designers Developers. Two

1:00:08 Having their hands A model that can give them Uh Worlds with three D structure so they can use it for for their work. And that's where

1:00:18 That's why Marbot is so different. The way I see it is it's a it's a platform for a ton of uh Opportunity to do stuff. Uh as you described Videos are just like here's a one off video that's very fun and cool.

1:00:30 And he could And that's it. That's it. And then you move on. By the way, we could in Marble we could allow people to export in video form. So you could actually, like you said. You go into a world so so so let's say it's a hobbit uh cave. You can actually, especially as a creator, you have such a

1:00:49 uh specific way of Uh Uh Moving the camera in a trajectory in the director's mind, right? And then you can export that uh from Marble into uh a video.

1:01:02 What does it take to Create something like this. Just like how big is the team, how many How many GPUs do you work in, like anything you can share there. I don't know how much of this is private information, but just what does it take to create something like this that you've launched here? It takes a lot of brain power. So well we just talk about twenty watts per brain. It's uh so from that point of view, it's it's a small number, but but it's actually an incredible, you know, it's uh half billion years of evolution to g give us those power.

1:01:31 Um We have a team of thirty ish people now and uh We are predominantly uh Researchers and research інженер.

1:01:43 And uh but we also have designers and and product we We actually really believe that we wanna create a company that's Anchored in the deep tack. of spatial intelligence.

1:01:56 But Ah, we We are акціо Building serious products. Um so so we have

1:02:05 We have this uh Integration of R and D and productisation. And of course we use You know, a ton of GPUs.

1:02:19 Well, congrats on the launch. I know this is a huge milestone. I know this took a ton of work. So I just want to say congrats to you and your team. Let me talk about your founder journey for a moment. So you're a founder of this company started how many years ago? A couple years ago? Two, three years ago? Oh, a year ago. A year ago. A year. Okay, eighteen month. Yeah. Okay. Well something you wish you knew. before you started this that you wish you could like whisper into that you're a fee fee of eighteen months ago.

1:02:46 Well, I continue to wish I know The future of technology. I think actually that's one of our founding advantage is that we see the future. earlier in general than than most people, but still, man, this is so exciting and so uh Amazing that But what's unknown and what's coming.

1:03:09 But I know the reason you're asking me this question is not about the future of technology. You're probably more You know, look, I I did not Start a company. Of the scale.

1:03:22 a twenty year old. So, you know, I started a dry cleaner when I was nineteen, but that's a little smaller scale. We gotta talk about it. And and then I, you know Um, funded Google Cloud AI and then I founded uh Institute at Stanford, but those are different beasts. I did feel I was a little more prepared as a uh a founder

1:03:46 Oh the The grinding journey? that um that I um compared to maybe um Maybe the the the the twenty year old founders.

1:03:57 But I still I'm surprised and and and uh It puts me into paranoia sometimes. that how intensely competitive uh AI.

1:04:11 Landscape is. From From the model, the technology itself, as well as talents. And you know, when I founded the company. Um We did not have this.

1:04:25 incredible stories of how much certain talents would cost, you know. Um So these are things that continue to surprise me and uh And I have to be very alert. about. The competition you're talking about is yeah, p the the competition for talent.

1:04:43 The speed at which just the how things are moving. Yeah. Yeah. You mentioned this point that I want to come back to that you're If you just look over the course of your career, you were like at All of the major

1:04:56 Uh collections of humans that Led to So many of the breakthroughs that are happening today. Obviously we talk about ImageNet also just sale at Stanford is where a lot of the work happen at Google Cloud, which a lot of the breakthroughs happened.

1:05:09 What brought you to those places, uh like for people looking for how to advance in their career, be at the center of the future. Just like is there a through line there of just What pulled you from place to place and pulled you into those

1:05:23 groups that might be helpful for people to hear. Yeah, this is actually a great question, Lenny,'cause I do think about it and uh obviously we talked about it's curiosity and passion that brought me to AI. That is more a scientific North Star, right? I did not care if AI was a thing or not. So so that was one part. But How did I end up choosing

1:05:48 Um In the particular places I work in, including starting World labs. Is I think I'm very grateful.

1:06:00 To myself or maybe to my Parents Jings. And I'm an intellectually very fearless person. And I have to say, when I hire young people, I look for that.

1:06:13 Because I um I think that's a very important quality. If one wants to make a difference. Із ванамейка дифринс.

1:06:25 You have to еpt. that you're creating something new, or you're diving into something new. People haven't done that. And if you Have that self awareness. You almost have to allow yourself to be

1:06:39 Fearless. And to be courageous. So when I uh for example Um Came to Stanford.

1:06:49 You know, in the world of academia. I was very close to this thing called tenure. Um, which is you know, have the job forever in in at Princeton. But I

1:07:02 I choose to choose to come to Sver because I love Princeton's by Ama Mater. It's just at that moment. There are people who are so amazing at Stanford.

1:07:15 And the Silicon Valley ecosystem was so amazing. Yeah. I was okay to take a risk of restarting my tenure clock. Um

1:07:26 Going to Um becoming the first uh female director of sale I was actually relatively speaking, a very young faculty at that time.

1:07:37 And I wanted to do that'cause I care about that community. I didn't Spend too much time thinking about all the failure cases. Um obviously I was very lucky that the more senior faculty supported me, but I just wanted to make a difference. And then going to Google was similar. I wanted to work with people like

1:07:59 Jeff Dean, Jeff Hinton. And um All these incredible demists, the the incredible people. Um I

1:08:10 You know, so so the same with world apps I I I have this passion, and I also believe that. People with the same mission can do incredible things. So that's how it guided my through I don't

1:08:26 Overthink. Oh. all possible things that can go wrong because that's too many. I feel like that's an important element of this is not Focusing on the downside, focusing more on

1:08:38 The people, the mission, what gets you excited, what do you think? Uh, I I do yeah, I do wanna say one thing to all the young talents in AI, the engineers, the researchers out there, because Some of you apply to world apps. I I feel very privileged you considered worse. I do find many of the young people today Think about

1:09:01 Every single aspect of a equation when they decide on jobs. At some point, maybe you know, maybe Maybe that's the way they wanna do it, but sometimes I do wanna encourage young people to Focus on what's important because I find myself

1:09:20 Um Costily in mentor mode when I talk to job job candidates, not necessarily recruiting or not recruiting, but just in mentoring mode. Вона сі а інкредил ян талант. Who

1:09:35 overfocusing them. Every minute dimension and aspect of considering the job when When maybe the most important thing is

1:09:48 Where's your passion? Do you align with the mission? Do you believe and have faith in this team? And And just

1:09:57 Just focus on the impact and And you can make an the kind of work and team you can you can Work with. Yeah, it's tough. It's tough for people in the AI space. Now there's just so much so much at them, so much news, so much happening, so much FOMO.

1:10:11 That's I could see the stress. And so I think that advice is really important. Just like what will actually Make you feel fulfilled in what you're doing, not just where's the Fastest growing company, where is the Who's gonna win? I don't know.

1:10:23 I wanna make sure I ask you about the work you're doing today at Stanford. at the H C I It's AI. H A I human Centered AI institute.

1:10:32 What are you what are you doing there? I know this is a thing you do on the site still. So yes, I uh HAI Human Center AI Institute Was co-founded by me and a group of uh faculty like uh Professor John H. Mendi, Professor James Landy. Um

1:10:49 Professor Chris Manning back in twenty eighteen, I was actually finishing my Last about a go at Google, um And uh It was a very, very important decision for me because I

1:11:05 Could have stayed in industry, but My time at Google taught me one thing is AI is gonna be a civilization or technology. And they w it's It dawned on me how important this is to humanity. to the point that I actually wrote a piece in New York Times that year, twenty eighteen, to talk about

1:11:25 The need for a guiding framework to develop And to And that framework has to be anchored in human Benevolence is human centeredness.

1:11:39 And I felt that Stanford uh one of the world's top University In the heart of Silicon Valley. That gave birth to important companies from Nvidia to

1:11:51 Google. Uh Should um Бі а сот лідер. Mm.

1:11:56 uh to create this human centered AI framework and to Um to actually embody that in our research education and policy and in ecosystem work. So I found it

1:12:11 H A I it uh you know after Fast forward after six seven years. It has become the world's largest. AI institute that does human centered Um

1:12:24 Oh Research, education. uh ecosystem outreach and policy uh In uh imp uh impact.

1:12:34 It involves hundreds of faculty across Uh eight schools at Stanford, from medicine to education to sustainability to business to engineering. to humanities to uh Мо

1:12:49 And uh We we support researchers, especially at the interdisciplinary area. from digital economy to uh legal studies to political science to discovery of new drugs uh to to

1:13:06 new algorithms to that's beyond transformers. We also actually put a very strong focus. On um on policy because when we started HAI I realized that

1:13:20 Silicon Valley did not talk to Washington, DC. and or Brussels. or other parts of the world. And it's r given how important this The technology is we need to

1:13:33 Bring everybody on board. So we created multiple programs from конгрешнол буткем Two Um А індекс репорт.

1:13:43 to policy briefing. А ви спешли uh participated in policy making, including um advocating for a um a national AI research cloud bill. That was passed in the first

1:14:00 Trump administration. And participate. participating in state level uh regulatory AI discussions. So there's a lot we did and

1:14:10 And I continue to be um One of the The leaders, even though I'm much less involved operationally. Because I care. Not only are we create this technology, but we use it in the right way.

1:14:24 Wow, I was not aware of all that other work you were doing. Uh as you're talking, I was reminded Charlie Monger And this quote. Take a simple idea and take it very seriously. I feel like you've done that in so many different ways and

1:14:38 And stayed with it. And it's Unbelievable the impact that you've had in so many ways over the years. I'm gonna skip the lightning round and I'm just gonna ask you one last question. Is there anything else that You wanted to share anything else you want to leave the listeners with? I I'm very excited by

1:14:54 A I Lenny. Uh I wanna answer one question that I When I travel around the world Everybody asks me, is that If I'm a musician, if I'm a

1:15:06 teacher, middle school teacher. If I'm a nurse. If I'm a accountant, if I'm a farmer. Do I have a role in AI or is AI just gonna take over my life or my work?

1:15:21 And I think this is the most important question of AI. And I find that in Silicon Valley. We tend not to Спік харту харт вид піпо. With

1:15:34 People like us, and not like us in Silicon Valley, but like all of us. We tend to Just toss around words like Infinite productivity. Or

1:15:45 Infinite leisure time. Or Or you know infinite power or whatever. But at the end of the day, AI is about people. And when people ask me that question

1:15:59 It's a resounding yes. Everybody has a role in AI. It depends on what What you do? And what you want.

1:16:07 But no technology should take away human dignity. And the human dignity and agency should be at the heart of The development The deployment As well as the governance of every technology, so

1:16:23 If you are a young artist. And your passion is storytelling. Embrace AI as a tool. In fact, embrace Marble, who I hope it becomes a tool for you. Um Because

1:16:39 The way you tell your story. is unique and this the world still needs it. But how you tell your story. How do you Use

1:16:48 The most incredible tul. To tell your story in the most unique way. is important. And that voice needs to be heard. If you're a farmer.

1:16:59 Near retirement. AI still matters. Because You're a citizen. You can participate in your community. You should have a voice in how AI is used.

1:17:11 how he a is applied You work people. That you can uh you know encourage all of All of you. to use AI

1:17:22 А то мек лиф ізіє форю. If you're a nurse. I hope you know that at least in my uh career. I have worked so much in healthcare research because I feel

1:17:36 Ar health care workers. should be greatly Augmented and helped. By AI technology. Whether it's smart cameras to feed more

1:17:46 uh in information or robotic assistance. Because our nurses are overworked. Over fatigued. Ан азар соціаті жа

1:17:57 We need more help. for for people to be taken care of. So AI can play that role. So I just want to say that it's so important. That

1:18:07 Um Even the technology like me. Um Ar sincere about that everybody has a role in AI.

1:18:17 What a beautiful way to end it. Such a tie back to where we started. About how it's up to us and take individual responsibility for what AI will do in our lives. Final question, where can folks find marble? Where can they go maybe? Uh try to join uh World Labs if they want to. What's the website? Where do people go?

1:18:34 Well, WorLab's website is www.world apps.ai And you can find Um you can find our research progress there. We we have technical blogs. You can find Marble the product there, you can sign in there.

1:18:51 You can find our job post link. There. You can uh You know, we're in San Francisco. We love to work with the world's best talents. Amazing. Fay Faye, thank you so much for being here.

1:19:04 Thank you, Lenny. Hi everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show.

1:19:26 at Lenny's podcast dot com. See you in the next episode.

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