Dr. Fei-Fei Li: Why world models come next, not bigger LLMs

1. 0:00 – 5:31

   Introduction to Dr. Fei-Fei Li

   1. LRLenny Rachitsky0:00

      A lot of people call you the godmother of AI. The work you did actually was the spark that brought us out of AI winter.

   2. FLDr. Fei-Fei Li0:05

      In the middle of 2015, middle of 2016, some tech companies avoids using the word AI because they were not sure if AI was a dirty word. (laughs) 2017-ish was the beginning of companies calling themselves AI companies.

   3. LRLenny Rachitsky0:22

      There's this line, I think this was when you were 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."

   4. FLDr. Fei-Fei Li0:30

      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.

   5. LRLenny Rachitsky0:56

      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.

   6. FLDr. Fei-Fei Li1:03

      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. 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.

   7. LRLenny Rachitsky1:36

      Today my guest is Dr. Fei-Fei Li, who's known as the godmother of AI. Fei-Fei 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 label data to get smarter, and that dataset became the breakthrough that led to the current approach to building and scaling AI models. She was chief AI scientist at Google Cloud, which is where some of the biggest early technology breakthroughs emerged from. She was director at SAIL, Stanford's Artificial Intelligence Lab, where many of the biggest AI minds came out of. She's also co-creator of Stanford's Human-Centered AI Institute, which is playing a vital role in a direction that AI is taking. She's also been on the board of Twitter. She was named one of Time's 100 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 nine to 10 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 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. Anyone can go play with this at marble.worldlabs.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 Condoleezza Rice for suggesting topics for this conversation. 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 Dr. 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 and how much it improved how we operated as a team. Suddenly, I could involve my whole team in the design process, give feedback on design concepts really quickly, and it just made the whole product development process so much more fun. But Figma never felt like it was for me. It was great for giving feedback and designs, but as a builder, I wanted to make stuff. That's why Figma built Figma Make. With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers. Figma Make is a different kind of vibe coding tool. Because it's all in Figma, you can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds. Stop spending so much time telling people about your product vision, and instead show it to them. Make code-backed prototypes and apps fast with Figma Make. Check it out at figma.com/lenny. Did you know that I have a whole team that helps me with my podcast and with my newsletter? I want everyone on that team to be super happy and thrive in their roles. Justworks knows that your employees are more than just your employees. They're your people. My team is spread out across Colorado, Australia, and Nepal, West Africa, and San Francisco. 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, Justworks 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. Justworks, for your people.

2. 5:31 – 9:37

   The evolution of AI

   1. LRLenny Rachitsky5:31

      Fei-Fei, thank you so much for being here and welcome to the podcast.

   2. FLDr. Fei-Fei Li5:34

      I'm excited to be here, Lenny.

   3. LRLenny Rachitsky5:36

      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 want to talk about. You've been at the center of this AI explosion that we're seeing right now for so long. 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.Fei-Fei 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 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. 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?

   4. FLDr. Fei-Fei Li6:29

      Yeah. Okay. So let me, 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, 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. 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 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. 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, if we're not doing the right thing as a species, as a society, as communities, as individuals, we can screw this up as well.

   5. LRLenny Rachitsky7:46

      Hmm. There's this line, I think this was when you were 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. (laughs)

   6. FLDr. Fei-Fei Li7:59

      Yeah. I, I f- I feel pretty deeply. I... 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 graduates from my lab that, "Your field is ca- artificial intelligence, but there's nothing artificial about it."

   7. LRLenny Rachitsky8:23

      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? How, how do we set things on a path? I know this is a, a very difficult (laughs) question to answer, but just what should... W- what's your advice? What do you think we should keep in mind?

   8. FLDr. Fei-Fei Li8:36

      Yeah, like, how many hours do we have? (laughs)

   9. LRLenny Rachitsky8:38

      (laughs) How do we align AI? There we go, let's solve it.

   10. FLDr. Fei-Fei Li8:41

       Yeah. So, 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 as grownups 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, we're, we're 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 it is going to impact your individual life, it is going to impact your community, it's gonna impact the f- the society and the future generation, and caring about it as a responsible person is the first but also the most important step.

3. 9:37 – 17:25

   The birth of ImageNet

   1. FLDr. Fei-Fei Li9:37

   2. LRLenny Rachitsky9:37

      Okay. So let me... Let me actually take a step back and kind of go to the beginning of AI. Most people started hearing and caring about 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.

   3. FLDr. Fei-Fei Li9:51

      Three years ago almost, uh, one more month three years ago. (laughs)

   4. LRLenny Rachitsky9:54

      Wow. Okay.

   5. FLDr. Fei-Fei Li9:55

      Yeah.

   6. LRLenny Rachitsky9:55

      And that was ChatGPT coming out? Is that-

   7. FLDr. Fei-Fei Li9:56

      Yes.

   8. LRLenny Rachitsky9:56

      ... the milestone that you have in mind?

   9. FLDr. Fei-Fei Li9:57

      Yeah.

   10. LRLenny Rachitsky9:57

       Okay, cool. That's exactly how I saw it. But very few people know there was 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, "Okay, this i- 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 we're in 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 of what the world was like before ImageNet, then just the work you did to create ImageNet, why that was so important, and then just what happened after.

   11. FLDr. Fei-Fei Li10:44

       It is, for me, hard to keep in mind that AI is so new for everybody when 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 barely out of teenage-hood and, and now has become a transformative force of our civilization. It generally is a civilizational-level, uh, technology. So, so that journey is about, about 30 years or 20-something, 20-plus years and, uh, it's, it's just very satisfying. So where did it all start? Well, I'm not even the first generation AI researcher. The first generation really date back to the '50s and '60s, and you know, Alan Turing was ahead of his time by, in the '40s, by asking daring humanity with the question, "Can we... Is there thinking machines," right? And of course he has a specific way of, uh, testing this concept of thinking machine which is a conversational chatbot which to his standard we now have a thinking machine, but, uh, that was just a more-... anecdotal inspiration. The field really began in the '50s, um, when computer scientists came together and look at how we can use computer programs and algorithms to, uh, to build these programs that can do things that have been only capable by human cognition. So, um, and- and that was the beginning and the founding fathers, the Dartmouth, the workshop in the 1956. Uh, you know, we have Professor John McCarthy, who later came to, uh, Stanford, who coined the term artificial intelligence. And between the '50s, '60s, '70s, and '80s, it was the early days of AI exploration and we had logic systems, we had, uh, expert systems. We also had early exploration of neural network. And then it came to around the late '80s, the '90s, and the- the very beginning of the 21st century. That stretch, about 20 years, is actually the beginning of machine learning. It's the marriage between computer programming and statistical as- uh, learning. And that marriage brought a very, very critical concept into AI, which is that purely rule-based, um, uh, 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. Once the machines can learn the patterns, it has the hope to do more things. For example, if you give it three cats, 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 fundamental to humans and many animals. And, uh, we- we as a field realize we need machine learning. So that was up till the beginning of the 21st century. I entered the field of AI literally in the year of 2000. That's when my, uh, PhD began at Caltech and so I was one of the first generation of machine learning researchers. Uh, we were already studying this concept of machine learning, especially the neural network. I remember that was one of my first courses in, uh, at Caltech. It's called neural network. 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 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 because, uh, humans are deeply visual animals. We can talk a little more later, but so much of our intelligence is built upon visual, perceptual, spatial understanding. Not just language per se. I think they're complementary. So I choose to look at visual intelligence and, um, my PhD and my early, uh, professor years, I, um, 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 at the object level. We don't interact with the world at the molecular level. We don't interact with the world as s- um, we sometimes do, but we rarely... For example, if you want to lift a teapot, you don't say, "Okay, the teapot is made of 100 pieces of porcelain and let me work on these 100 pieces." (laughs) You look at this 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 as a student of AI a- and then a researcher of AI, I was working on all kinds of mathematical models, including neural network, including Bayesian network, including many, many models and there was one singular pain point, 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 that human learning as well as evolution is actually a big data learning process. Humans learn with so much experience, you know, constantly and evolution, if you look at time, animals evolve with just experiencing the world.

4. 17:25 – 23:53

   The rise of deep learning

   1. FLDr. Fei-Fei Li17:25

      So I think my students and- and I conjectured that a very critically overlooked ingredient of bringing AI to life is big data. And then we began this ImageNet project in 2006, 2007. We were very ambitious. We want to get the entire internet's image data o- objects. Now, granted internet was a lot smaller than today. (laughs) So we- I feel like that ambition was at least not too crazy. Now it's totally delusional to, uh, to think w- a couple of graduate student and a professor can do this. But, uh...And that's what we did. We curated, very carefully, 15 million images on the internet, created a taxonomy of 22,000 concepts, borrowing other researchers' work, like, uh, linguists's work on WordNet, and it's a particular way of, uh, dictionary-ing, uh, words. And we combined that into ImageNet, and we open sourced that to the research community. We held an annual ImageNet challenge to encourage everybody to participate in this. We continued to do our own research. But 2012 was the moment 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 Geoff Hinton participated in ImageNet challenge, used the ImageNet big data and two GPUs from NVIDIA and created, successfully, the first neural network algorithm that can... It didn't fundamental- i- it didn't wh- uh, totally solve, but made a huge progress towards solving the problem of object recognition. And that combination of the trio technology, uh, big data, neural network and GPU was kind of the golden recipe for modern AI. And then fast forward, the- the- the public moment of AI, which is the ChatGPT moment. If you look at the ingredients of what brought ChatGPT to- to the- to the, uh, world, technically, it still use these three ingredients. Now its internet scale data, mostly texts, is a much more com- complex, uh, neural network, um, architecture than 2012, but it's still neural network and a lot more GPUs, but it's still GPU. So these three ingredients are still to- uh, at the core of modern AI.

   2. LRLenny Rachitsky20:16

      Incredible. I have never heard that full story before. I love that it was two GPUs was the first (laughs) ... I love that,

   3. FLDr. Fei-Fei Li20:23

      Yeah.

   4. LRLenny Rachitsky20:24

      ...

   5. NANarrator20:24

      (laughs)

   6. LRLenny Rachitsky20:25

      uh, an- and now it's, I don't know, hundreds of thousands, right? That are-

   7. FLDr. Fei-Fei Li20:28

      Oh, yeah.

   8. LRLenny Rachitsky20:28

      Uh, orders of magnitudes more powerful, uh-

   9. FLDr. Fei-Fei Li20:30

      Yep.

   10. LRLenny Rachitsky20:31

       And those two GPUs, were... They just bought. They were like gaming GPUs. They just went to the-

   11. FLDr. Fei-Fei Li20:34

       Yes.

   12. LRLenny Rachitsky20:34

       ... like, the game store, right?

   13. FLDr. Fei-Fei Li20:35

       Yeah.

   14. LRLenny Rachitsky20:35

       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, Merkle and Surge and Scale. Like, they do this... They continue to do this for laughs.

   15. FLDr. Fei-Fei Li20:49

       Yeah.

   16. LRLenny Rachitsky20:49

       Just give 'em more and more labeled data of the things they're most excited and interested in.

   17. FLDr. Fei-Fei Li20:52

       Oh yeah. I remember, um, Alex, uh, Wong from-

   18. LRLenny Rachitsky20:55

       Mm-hmm.

   19. FLDr. Fei-Fei Li20:56

       ... 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 ImageNet inspired Scale. I was very pleased to see that.

   20. LRLenny Rachitsky21:09

       One of my other favorite takeaways from what you just shared is just such an example of 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?

   21. FLDr. Fei-Fei Li21:25

       I think it was interchangeably. It's true. Like, I do remember the companies, the tech companies, I- I'm not gonna name names, but I wa- I was, uh, in a conversation in one of the early days, I think it is in the middle of 2015, middle of 2016. Uh, some tech com- companies avoids using the word AI because they were not sure if AI was a dirty word (laughs) . And I remember, I was actually re-encouraging everybody to use the word AI, because, to me, that is one of the most audacious question humanity has ever asked in our quest for science and technology. And I feel very proud of this term (laughs) . But yes, uh, at the beginning, some people were not sure.

   22. LRLenny Rachitsky22:12

       What year was that roughly when AI was a dirty word?

   23. FLDr. Fei-Fei Li22:14

       2016. I think 'cause I-

   24. LRLenny Rachitsky22:17

       2016? Less than 10 years ago.

   25. FLDr. Fei-Fei Li22:17

       ... was, that was the changing, like, um, some people start calling it AI. But I think if you look at the Silicon Valley tech compa- companies, if you trace their marketing term, I think 2017-ish was the beginning of companies calling themselves AI companies.

   26. LRLenny Rachitsky22:40

       That's incredible.

   27. FLDr. Fei-Fei Li22:41

       (laughs)

   28. LRLenny Rachitsky22:41

       Just how the world has changed.

   29. FLDr. Fei-Fei Li22:43

       Yes.

   30. LRLenny Rachitsky22:43

       Now you can't not call yourself an AI company-
5. 23:53 – 29:51

   The future of AI and AGI

   1. FLDr. Fei-Fei Li23:53

   2. LRLenny Rachitsky23:54

      Okay. So let me ask you this question. It feels like we're always on this precipice of AGI, this kind of vague term people throw around, "AGI is coming. Is it 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 we're on? Do you think we need more breakthroughs? Do you think the current approach will get us there?

   3. FLDr. Fei-Fei Li24:13

      Yeah, this is a very interesting term, Lenny. Um, I don't know if anyone has ever defined AGI.

   4. LRLenny Rachitsky24:22

      (laughs)

   5. FLDr. Fei-Fei Li24:23

      (laughs) You know, there are many different definitions including, you know, some kind of superpower for machines, all the way to, can, um, machines c-can become economically viable agent in, in a society? W- uh, in other words, making salaries to live. Is that the definition of AGI? As a scientist, I, I take science very seriously, and I enter the field because I was inspired by this audacious question of, can machines think and do things in the way that human c- humans can do? For me, that's always the north star of AI. And from that point of view, I don't know what's the difference between AI and AGI. 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 all the goals, uh, of, of AI. And I think our founding fathers, the Alan Turing, I wonder if Alan Turing is around today and you ask him to contrast AI versus AGI, he might just shrug and said, "Well, I asked the same question (laughs) back in 1940." So, so I don't wanna g- get onto a rabbit hole of defining AI versus AGI. I feel AGI is more a marketing term than a scientific term. As a scientist and technologist, AI is my north star, i- is my field's north star, and I'm happy people call it whatever name they want to call it. (laughs)

   6. LRLenny Rachitsky26:05

      Hmm. 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 are today, GPUs essentially, data, label data, just like the algorithm of the model. There's also just the transformer feels like an important step in that trajectory. Do you feel like those are the same components that'll get us to, I don't know, 10 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, this will plateau, or okay, this will take us... just need more data, more compute, more GPUs?

   7. FLDr. Fei-Fei Li26:44

      Oh, no. I definitely think we need more, uh, innovations. I, I think scaling laws of more data, more GPUs, and bigger current model architecture 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 that has arrived at a place that says, "We're done. We're done innovating." And AI is o-one of the, if not the youngest discipline in, in human civilization, in terms of science and technology. We're still scratching the surface. Uh, for example, um, like I said, we're gonna segue into world models. Today, you take a, a model and, and, and run it through a, a video of, uh, a couple of office rooms and ask the, the model to count the number of chairs, and this is something a toddler could do, or maybe, maybe a, a, a, a elementary school kid could do. 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 look at the movements of the celestial bodies a-and, and derive an equation or, or a set of equations that governs the movement of all bodies. Uh, that level of creativity, extrapolation, abstraction, 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 coming to a teacher's office and have a conversation about motivation, passion, what to learn, what's the problem that's, that's, you know, really, uh, bothering you, that conversation, as powerful as, 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-

   8. LRLenny Rachitsky28:56

      Mm-hmm.

   9. FLDr. Fei-Fei Li28:57

      ... and I do not believe we're done innovating. (laughs)

   10. LRLenny Rachitsky29:00

       Uh, Demis had this really interesting interview recently from DeepMind/Google where someone asked him just like, "What do you think, uh, how far are we from AGI? What does it look like?" And went through there. He had a really interesting way of approaching it, is if we were to give, um, the most cutting edge model all of th- information until the end of the 20th century, see if it could come up with all the breakthroughs Einstein had, and so far, we're nowhere near that, where they can just-

   11. FLDr. Fei-Fei Li29:22

       No, we're not. Uh, in fact, it's even worse. Let's give AI all the data, including modern instruments' data of-

   12. LRLenny Rachitsky29:31

       Mm-hmm.

   13. FLDr. Fei-Fei Li29:31

       ... celestial bodies, which Newton did not have, and give it to that, and just ask AI to create the 6th, 17th century set of equations on the laws of, uh, bodily movements. Uh, today's AI cannot do that.

   14. LRLenny Rachitsky29:48

       Mm-hmm. All right. We're a ways away is what I'm hearing.

   15. FLDr. Fei-Fei Li29:50

       Yeah.

6. 29:51 – 40:45

   Introduction to world models

   1. FLDr. Fei-Fei Li29:51

   2. LRLenny Rachitsky29:51

      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 end up. So you are 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, 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?

   3. FLDr. Fei-Fei Li30:33

      I'm very excited to see that more and more people are talking about world models like Elon, like Jensen. 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, uh, the research world and then OpenAI and, and all this for the past few years were extremely inspiring, even for a, a researcher like me. I remembered when GPT-2 came out, and that was in, (smacks lips) I think late 2020. I was, um, co-director, um, I still am, but I was at that time f- uh, full-time co-director of Stanford's, uh, Human-Centered AI Institute, and I, I remember it was, you know, the public was not aware of the power of the large language model yet. But as researchers, we were seeing it, we're seeing the future. And I had pretty long conversations with my natural language, (laughs) processing colleagues like Percy Liang and Chris Matting. We were talking about how critical this technology is gonna be. And Stanford, uh, AI Institute, Human-Centered AI Institute, HAI, was the first one to establish a full research center on foundation model. We were, Percy Liang and, and many researchers, led the first, uh, academic paper on foundation model. So, so it was just very inspiring for me. So of course, 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 humans, um, humans have used our sense of spatial intelligence and world understanding to do so many things, 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, and it's, if you e- immerse yourself in the scene and think about how people organize themself to, to rescue people, to stop further disasters, to put down fires, to, to... A lot of that is movements, is, is spontaneous understanding of objects, worlds, hu- uh, human, and s- uh, uh, uh, situational awareness. Language is part of that, but a lot of those situations, l- language cannot get you to put down a 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 ca- it dawned on me that the linchpin of connecting the additional intelligence, adi- in addition to language, and connecting embodied AI, which are robotics, connecting visual intelligence is this sense of spatial intelligence about understanding the world. And that's when, um, I think I, um, it was 2024, I gave a TED Talk about spatial intelligence and world models, and, uh, I start formulating this idea, uh, back in 2022, um, based on my robotics and computer vision research. And then one thing that was really clear to me is that I really wanna work with the brightest, uh, technologist and, and move as fast as possible to bring this technology to life, and that's when we (laughs) 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 modeling and spatial intelligence.

   4. LRLenny Rachitsky34:41

      People are so used to just chatbots and that's a large language model. As a simple way to understand a world model is you basically describe a scene and it generates an infinitely d- uh, explorable world. We'll link to, uh, the thing you launched, which we'll talk about, but just, is that a simple way to understand it?

   5. FLDr. Fei-Fei Li34:56

      That's part of it, Lenny. I think a simple way to understand a world model, uh, is that this model can allow anyone to create any worlds in their mind's eye by prompting whether it's an image or a sentence, and also be able to interact in this world, whether you are browsing and walking or, or picking objects up or, or, or changing, changing things, uh, as well as to reason within this world. For example, if, if the person consuming, uh, if the agent consuming this output of the world model is a robot, it should be able to plan its path and, and help to, uh, you know, tidy the kitchen, (laughs) for example. So, so world model is a, a foundation that, that you can use to reason, to interact, and to create worlds.

   6. LRLenny Rachitsky36:00

      Great. Yeah. So robots feels like that's, um, potentially the next big focus for AI researchers and just, like, the impact on the world, and 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.

   7. FLDr. Fei-Fei Li36:17

      Yeah. Well, first of all, I do think there's more than robots that's exciting.

   8. LRLenny Rachitsky36:22

      Yeah.

   9. FLDr. Fei-Fei Li36:22

      Um, uh, uh, so, but I agree with everything (laughs) you just said. I think, uh, world modeling and spatial intelligence is a key missing piece of, uh, uh, embodied AI. I also think, let's not underestimate that humans are embodied agents.... and humans can be augmented by AI's, uh, intelligence. Just like today, humans are language animals, but we're very much augmented by AI when helping us to, you know, do language tasks, including software engineering. I, I think that, uh, we shouldn't underestimate... Or maybe it's, it's, um... We tend not to talk about how humans, as an embodied agents, can actually benefit so much from world models and spatial intelligent, uh, models as well as robots can.

   10. LRLenny Rachitsky37:15

       So the big unlocks here, robots, which, uh, a huge deal. If this works out, imagine each of us has robots doing a bunch of stuff for us, goes and, you know... They help us with 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-

   11. FLDr. Fei-Fei Li37:35

       Yeah.

   12. LRLenny Rachitsky37:35

       ... thinking of mag- wild new worlds and environments.

   13. FLDr. Fei-Fei Li37:39

       And also design.

   14. LRLenny Rachitsky37:40

       Mm-hmm.

   15. FLDr. Fei-Fei Li37:41

       Humans design from machines to buildings to homes. And also scientific discovery, right? Uh, there is so much, uh... I, I like to use the example of the discovery of the structure of DNA. If you look at one of the most important piece in, uh, DNA's discovery history is the x-ray diffraction photo that was captured by Rosalind Franklin, and it was a flat 2D photo of a structure that looks like... It looks like a cross with, uh, d- uh, diffractions. You can, you can, uh, Google those photos, but with that 2D flat photo, humans, especially two important humans, James Watson and Francis Crick, in addition to their other, uh, information, was able to reason in 3D space and deduce a highly three-dimensional double helix structure of the DNA, and that structure cannot possibly be 2D. You cannot think in 2D and deduce that structure. You have to think in 3D spatial, um... Use the, the human spatial intelligence. So I think even in scientific discovery, um, spatial intelligence or AI assistant spatial intelligence is critical.

   16. LRLenny Rachitsky39:07

       This is such an example o- 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 Sam Altman just tweeted, it was like, "Here's a cool thing we're playing with. Check it out." Now it's the fastest growing product in all of history, changed the world.

   17. FLDr. Fei-Fei Li39:24

       Yeah.

   18. LRLenny Rachitsky39:24

       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.

   19. FLDr. Fei-Fei Li39:32

       Yeah.

   20. LRLenny Rachitsky39:33

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7. 40:45 – 48:02

   The bitter lesson in AI and robotics

   1. LRLenny Rachitsky40:45

      I reached out to Ben Horowitz, who loves what you're doing, a big fan of yours. Uh, they're investors, I believe, in-

   2. FLDr. Fei-Fei Li40:51

      Yeah. We- we've known each other for, for many years.

   3. LRLenny Rachitsky40:54

      (laughs)

   4. FLDr. Fei-Fei Li40:54

      But yes-

   5. LRLenny Rachitsky40:55

      Okay, cool.

   6. FLDr. Fei-Fei Li40:55

      ... right now they are investors of, uh, War Labs.

   7. LRLenny Rachitsky40:57

      Amazing. Okay. So I asked him what I should ask you about, and he suggested I ask you, why is the bitter... L- why is the bitter lesson alone not likely to work for robots? 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 wanna be with robots.

   8. FLDr. Fei-Fei Li41:16

      So, well, first of all, there are many bitter lessons, but... (laughs)

   9. LRLenny Rachitsky41:21

      (laughs)

   10. FLDr. Fei-Fei Li41:21

       But the bitter lessons everybody refers to is a, um, is a paper written by Richard Sutton, who won the Turing Award recently, 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 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... Uh, this paper came years after ImageNet. That, to me, was not bitter, it was a sweet lesson. That's why I built, uh, ImageNet, because I believe that, uh, big data plays that role. So why, uh, can bitter lesson work in robotics alone? 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, 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 in robotics.And, um, but what is hard for robotics, there are a couple of things. One is that it's harder to get data. It's a lot harder to get data. You can say, "Well, there is 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 make language model work, a very... 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 have this perfect setup where their training data are in words, eventually tokens, and then they produce a model that outputs words. So you have this perfect alignment between 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, but your training data lacks actions in 3D worlds, and that's what robots have to do, right, actions in 3D worlds. So you have to, um, find different ways to fit a, uh, what do they call, a, a, a, a square in a round hole.

   11. LRLenny Rachitsky44:17

       (laughs)

   12. FLDr. Fei-Fei Li44:17

       That (laughs) ... What we have is tons of web videos, so then we have to start talking about, uh, adding supplementing data such as tele-operation data or synthetic data so that the robots are trained with this hypothesis of bitter lesson, which is large amount of data. I think there's still hope, because even what we are doing, um, in world modeling, will really unlock a lot of this, uh, information for robots. But I think we have to be careful because we're at the early days of this and the bitter lesson is still to be tested, uh, because we haven't f- fully figured out the data for. Another part of the bitter lesson of robotics I think we should be so, so realistic about is, again, compared to language models or even spatial models, robots are physical systems. So robots are closer to self-driving cars than a large language model, and that's very important to recognize. That means that in order for robots to work, we not only need brains, we also need the physical body, we also need application scenarios. If you look at the, the, the, the history of self-driving car, um, my colleague, Sebastian Thrun, uh, uh, took Stanford's car to win the first DARPA challenge in 2006 or 2005. It's 20 years since that prototype of a self-driving car being able to drive 130 miles in the Nevada desert to today's Waymo and, um, on the street of San Francisco. And we're not even done yet. There's still a lot. So that's a 20-year journey. And self-driving cars are much simpler robots. They're just metal boxes running on 2D surfaces, and the goal is not to touch anything. Robot (laughs) is 3D things running in 3D world, and the goal is to touch things. So the journey is gonna be... You know, there's many aspects, elements. And of course one could say, "Well, 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'm in robotics, that's why I'm in spatial intelligence and I'm excited by it. But in the meantime, the car industry is very mature and productizing also involves the mature use cases, supply chains, the hardware. So I think it's a very interesting time to work in these problems. But it's true, Ben is right. We might still be subject to a number of bitter lessons. (laughs)

   13. LRLenny Rachitsky47:28

       Doing this work, do you ever just feel awed 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?

   14. FLDr. Fei-Fei Li47:44

       Totally. We, we operate on about 20 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.

8. 48:02 – 51:00

   Introducing Marble, a revolutionary product

   1. FLDr. Fei-Fei Li48:03

      (laughs)

   2. LRLenny Rachitsky48:03

      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 in- for folks to check it out. What is Marble?

   3. FLDr. Fei-Fei Li48:14

      Yeah. I'm very excited. So first of all, Marble is, uh, one of the first product that World Labs, uh, has rolled out. World Labs is a foundation frontier model company. We are funded by four co-founders who have deep technical history. My co-founders, Justin Johnson, uh, Christoph, uh, Lassner, and Ben Mildenhall, we all come from 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 models, so we wanted to seize this opportunity to create deep, uh, tech research lab that can connect the dots between, um, frontier models with products. So, Marble is an app that's built upon our frontier models. We've spent a year and plus building the world's first, uh, generative model that can output genuinely 3D worlds. That's a very, very hard problem, um, and, uh, and it- it- it- it was a very hard process. Uh, we, uh, we have a team of incredible, founding team of incredible technologists from, you know, incredible, uh, uh, teams. And then around, um, just a month or two ago, we saw the first time that we, we can just prompt with a sentence and a image, and multiple images and create worlds that we can just navigate in. I- if you put it on Goggle, which we have an option to let you do that, you can even walk around it, right? So it was... Even though we've been building this for, for, for quite a while, it was still just awe-inspiring, and we wanted to get into the hands of, uh, people who need it. And then we know there are so many creators, designers, people who are thinking about, uh, robotic simulation, people who are thinking about, uh, different use cases of, uh, navigable, interactable, um, uh, immersive worlds, game developers will find this useful. So we, uh, devel- 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, prompt, we call it prompt two worlds.

9. 51:00 – 1:01:01

   Applications and use cases of Marble

   1. FLDr. Fei-Fei Li51:00

      (laughs)

   2. LRLenny Rachitsky51:01

      Well, I've been playing around with 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, (laughs) there's no one there yet, but, (laughs) , uh, it's insane. You just go anywhere, there's, like, dystopian world. I'm just looking at all these examples.

   3. FLDr. Fei-Fei Li51:14

      Yes.

   4. LRLenny Rachitsky51:14

      Uh, and my favorite part actually, I don't know, I don't know if this is a feature or bug, you can see, like, the dots of the world 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. Basically create, like-

   5. FLDr. Fei-Fei Li51:26

      That is so cool to hear-

   6. LRLenny Rachitsky51:29

      Yeah.

   7. FLDr. Fei-Fei Li51:29

      ... because this is where as a researcher I've, I, I'm learning-

   8. LRLenny Rachitsky51:33

      Mm-hmm.

   9. FLDr. Fei-Fei Li51:33

      ... because the, the, the, the dots that lead you into the world was a- an intentional feature, uh, visualization. It w- it's not part of the model. It's, uh... The model actually just generates the world.

   10. LRLenny Rachitsky51:49

       Oh, okay.

   11. FLDr. Fei-Fei Li51:50

       But we, we were trying to find a way to guide people into the world, and a number of engineers, uh, worked on different versions, but we converged on the dot, and so many people, (laughs) you're not the only one, told us how delightful that experience is, and it, it was really satisfying for us to hear that this intentional visualization feature that's not just the big hardcore model actually has delighted our users.

   12. LRLenny Rachitsky52:19

       Wow, so you add that to make it more, uh, like, to have humans understand-

   13. FLDr. Fei-Fei Li52:24

       To have fun.

   14. LRLenny Rachitsky52:24

       ... what's going on more, and more delightful.

   15. FLDr. Fei-Fei Li52:25

       Yes.

   16. LRLenny Rachitsky52:25

       Wow. That is hilarious. (laughs) It makes me-

   17. FLDr. Fei-Fei Li52:27

       Yeah.

   18. LRLenny Rachitsky52:27

       ... think about LMs and the way they... It's not the same thing, but they talk about what they're thinking and what they're doing.

   19. FLDr. Fei-Fei Li52:32

       Yes. It is. It is. Yeah.

   20. LRLenny Rachitsky52:34

       It also makes me think of just The Matrix. Like-

   21. FLDr. Fei-Fei Li52:37

       Yeah.

   22. LRLenny Rachitsky52:37

       ... it's exactly (laughs) The Matrix experience.

   23. FLDr. Fei-Fei Li52:39

       Yeah.

   24. LRLenny Rachitsky52:39

       I don't know if that was your inspiration.

   25. FLDr. Fei-Fei Li52:41

       Um, well, like I said, a number of engineers worked on that.

   26. LRLenny Rachitsky52:44

       (laughs) Fair enough.

   27. FLDr. Fei-Fei Li52:44

       It could be their inspiration. (laughs)

   28. LRLenny Rachitsky52:46

       It's in their, it's in their, uh, it's in their subconscious.

   29. FLDr. Fei-Fei Li52:50

       Yeah.

   30. LRLenny Rachitsky52:50

       Okay, so just for folks that maybe wanna play around with this, 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?
10. 1:01:01 – 1:10:05

    The founder’s journey and insights

    1. FLDr. Fei-Fei Li1:01:01

       uh, a video.

    2. LRLenny Rachitsky1:01:02

       What does it take to create something like this? Just like how big is the team? How many, how many GPUs are you working? 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?

    3. FLDr. Fei-Fei Li1:01:12

       It takes a lot of brain power. (laughs) So, but we just talk about 20 watts per brain. It's, uh ... So from that point of view it, it's a small number but, (laughs) -

    4. LRLenny Rachitsky1:01:24

       (laughs)

    5. FLDr. Fei-Fei Li1:01:24

       ... but it's actually-

    6. LRLenny Rachitsky1:01:25

       It sure is.

    7. FLDr. Fei-Fei Li1:01:25

       ... an incredible, you know, it's a half billion years of evolution to get, give us those power. Um, it, we have a team of 30-ish people now and, uh, we are predominantly, uh, researchers or research engineers 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 tech of spatial intelligence but, uh, we, we, we are actually building serious products. Um, so, so we have, we have this, uh, integration of R&D and productization. And, of course we use, you know, a ton of GPUs. (laughs)

    8. LRLenny Rachitsky1:02:15

       That's a g-

    9. FLDr. Fei-Fei Li1:02:16

       Makes-

    10. LRLenny Rachitsky1:02:16

        That's the technical term.

    11. FLDr. Fei-Fei Li1:02:16

        ... Jensen happy to hear. (laughs)

    12. LRLenny Rachitsky1:02:20

        (laughs)

    13. FLDr. Fei-Fei Li1:02:20

        Yeah.

    14. LRLenny Rachitsky1:02:20

        Well, congrats on the launch. I know that's a huge milestone. I know this took a ton of work-

    15. FLDr. Fei-Fei Li1:02:23

        Thank you.

    16. LRLenny Rachitsky1:02:23

        ... so I just wanna 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. You started how many years ago? Couple years ago? Two, three years ago?

    17. FLDr. Fei-Fei Li1:02:33

        Uh, a year ago. Uh, a year-

    18. LRLenny Rachitsky1:02:34

        A year ago?

    19. FLDr. Fei-Fei Li1:02:35

        ... plus. Uh, yes.

    20. LRLenny Rachitsky1:02:35

        A year? Okay, wow.

    21. FLDr. Fei-Fei Li1:02:36

        Probably 18 month, yeah.

    22. LRLenny Rachitsky1:02:38

        Okay. What's something you wish you knew before you started this that you wish you could, like, whisper into the ear of Fei-Fei of (laughs) 18 months ago?

    23. FLDr. Fei-Fei Li1:02:46

        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, that what's unknown and what's coming. But I know the reason you're asking me this question is not about the future (laughs) of technology. You're probably more ... You know, look, I, I did not start a company of this scale at 20 year old. So, you know, I started a dry cleaner when I was 19, but (laughs) that's a little smaller scale.

    24. LRLenny Rachitsky1:03:30

        We gotta talk about that.

    25. FLDr. Fei-Fei Li1:03:31

        And, (laughs) and then I, you know, um, founded Google Cloud AI and then I founded a institute at Stanford, but those are different beasts. I did feel I was a little more prepared as a, uh, a founder of the, the grinding journey that, um, that I, um, compared to maybe, um, maybe the, the, the, the 20 year old founders. But I still, I'm surprised and, and, and, uh, it puts me into paranoia sometimes that how intensely competitive, uh, AI 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 these incredible stories of how much certain talents would cost, you know? (laughs) Um, so these are things that continue to surprise me and, uh, and I have to be very alert about.

    26. LRLenny Rachitsky1:04:39

        So the competition you're talking about is, yeah, p- the competition for talent, the speed at which just th- how things are moving.

    27. FLDr. Fei-Fei Li1:04:45

        Yeah.

    28. LRLenny Rachitsky1:04:46

        Yeah. You mentioned this point that I wanna come back to that you're ... If you just look over the course of your career, you were, like, at all of the major, uh, collections of humans that led to so many of the breakthroughs that are happening today. Obviously we talk about ImageNet, also just SAIL at Stanford is where a lot of the, uh, work happened. Google Cloud, which a lot of the breakthroughs happened. 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 groups that might be helpful for people to hear?

    29. FLDr. Fei-Fei Li1:05:25

        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, um, in the particular places I work in, including starting World Labs, is I think I'm very grateful to myself (laughs) or maybe to my parents' genes, I'm, I'm an intellectually very fearless person. And I have to say when I hire young people, I look for that, because I, um, I think that's a very important quality if one wants to make a difference, is that when you wanna make a difference, you have to accept 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 fearless and to be courageous. So, when I, uh, for example, um, came to Stanford, 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, I choose to, chose to come to Stanford because I love Princeton. It's my alma mater. It's just at that moment, there are people who are so amazing at Stanford, and the Silicon Valley ecosystem was so amazing, that I...... was okay to take a risk of restarting my tenure clock. Um, going to, um, becoming the first, uh, female director of SAIL, I was actually, relatively speaking, a very young faculty at that time and I wanted to do that 'cause I care about that community. I didn't spend too much time thinking about (laughs) 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 Jeff Dean, Geoff Hinton and, um, all these incredible, uh, Demis, the, the incredible people, um, uh, uh, you know. And so, so the same with World Labs, 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, through line. I don't overthink of all possible things that can go wrong because that's too many. (laughs)

    30. LRLenny Rachitsky1:08:34

        I feel like that's an important element of this, is not focusing on the downside, focusing more on the people, the mission, what gets you excited, what do you think, uh, the curiosity-
11. 1:10:05 – 1:14:24

    Human-centered AI at Stanford

    1. LRLenny Rachitsky1:10:05

       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 new, so much happening, so much FOMO.

    2. FLDr. Fei-Fei Li1:10:11

       That's true.

    3. LRLenny Rachitsky1:10:11

       Uh, 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 or where's the who's gonna win, I don't know. I wanna make sure I ask you about the work you're doing today at Stanford at the HCI. I think it's the-

    4. FLDr. Fei-Fei Li1:10:29

       HAI.

    5. LRLenny Rachitsky1:10:29

       HAI, Human Centered AI Institute. What are you, what are you doing there? I know this is a thing you do on the side still.

    6. FLDr. Fei-Fei Li1:10:36

       So yes, I, HAI, Human Centered AI Institute, was co-founded by me and a group of, uh, faculty like, uh, Professor John Etchemendy, Professor James Landay, um, Professor Chris Manning, back in 2018. I was actually finishing my last, last, uh, sabbatical at Google, um, and, uh, it was a very, very important decision for me because I could have stayed in industry but my time at Google taught me one thing, is AI is gonna be a civilizational technology. And they, 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, 2018, to talk about the need for a guiding framework to develop and to, uh, to apply AI. And that framework has to be anchored in human benevolence, is human centeredness. 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 Google, uh, should, um, be a thought leader, uh, to create this human centered AI framework and to, um, to actually embody that in our research, education and policy and in, uh, ecosystem work. So I founded HAI. It, uh, you know, after, uh... Fast-forward after six, seven years, it has become the world's largest AI institute that does human centered, um, uh, research, education, uh, ecosystem outreach and policy, uh, im- uh, imp- uh, impact. Uh, it involves hundreds of faculty across all eight schools at Stanford, from medicine to education to sustainability to business to engineering to humanities to, uh, law, 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 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 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, th- this technology is, we need to bring everybody on board. So, we created multiple programs from congressional bootcamp to, um, AI Index Report, to policy briefing. And we espe- especially, uh, participated in policymaking, including, um, advocating for a, um, a national AI research cloud bill that was passed in the first Trump administration and partici- participating in state-level, uh, regulatory A- AI discussions. So, there's a lot we did and, and I continue to be, um, one of the, the leaders even though I'm much less involved operationally, because I care not only we create this technology, but we use it in the right way.

12. 1:14:24 – 1:18:16

    The role of AI in various professions

    1. LRLenny Rachitsky1:14:24

       Wow, I was not aware of all that other work you were doing. Uh, as you're talking, I was reminded Charlie Munger had this quote, "Take a simple idea and take it very seriously." I feel like you've done that in so many different ways and, 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 wanna leave listeners with?

    2. FLDr. Fei-Fei Li1:14:52

       I, I'm very excited by AI, 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 teacher, middle schoolteacher, if I'm a nurse, if I'm an 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? And I think this is the most important question of AI. And I find that in Silicon Valley, we tend not to speak heart to heart with people, with people like us, and, and not like us in Silicon Valley, but, like, all of us. We tend to just toss around words like infinite productivity, or 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, it's a resounding yes. Everybody has a role in AI. It depends on what, what you do and what you want. 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, if you are a young artist and your passion is storytelling, uh, embrace AI as a tool. In fact, embrace Marble, who I hope it becomes a tool for you, um, because 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 the most incredible tool to tell your story in the most unique way is important. And th- that voice needs to be heard. If you're a farmer 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, how H- AI is applied. You, you work with people that you can, uh, you know, encourage all of, all of you to use AI, uh, to make life easier for you. 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 our healthcare workers should be greatly augmented and helped by AI technology, whether it's smart cameras to feed more, uh, in- information, or robotic assistance. Because our nurses are overworked, over-fatigued, and as our society ages, we need more help for, for people to be taken care of. So, AI can play that role. So, I just wanna say that it's so important that, um, even a technologist like me, um, are sincere about that everybody has a role in

13. 1:18:16 – 1:19:33

    Conclusion and final thoughts

    1. FLDr. Fei-Fei Li1:18:16

       AI.

    2. LRLenny Rachitsky1:18:17

       What a beautiful way to end it. Such a tie back to where we started about how it's up to us, and take indivi- 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?

    3. FLDr. Fei-Fei Li1:18:34

       Well, World Labs' website is www.worldlabs.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. You can find our job posts, uh, link there. You can, uh, you know, we're in San Francisco. We love to work with the world's best talents.

    4. LRLenny Rachitsky1:19:02

       Amazing. Fei-Fei, thank you so much for being here.

    5. FLDr. Fei-Fei Li1:19:04

       Thank you, Lenny.

    6. LRLenny Rachitsky1:19:06

       Bye, everyone. (instrumental music) 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 at lennyspodcast.com. See you in the next episode.

Episode duration: 1:19:33