Teaching AI to Understand the Physical World, with Dr. Fei-Fei Li of World Labs

Summary

Podcast Summary: No Priors – "Teaching AI to Understand the Physical World" with Dr. Fei-Fei Li

Date: June 5, 2025
Hosts: Sarah Guo, Ilana Nesher
Guest: Dr. Fei-Fei Li (World Labs, Stanford University)

---

Overview: Main Theme and Purpose

This episode features Dr. Fei-Fei Li, renowned AI researcher and co-founder of World Labs, in a wide-ranging discussion about spatial intelligence and the next frontiers in artificial intelligence. The conversation explores what it means to create AIs that understand, reason about, and interact within the physical (3D) world—a leap beyond today's text and image-based models. Dr. Li shares her motivations, delves into the challenges and impacts of 3D world modeling, and offers insights from her storied career, including her founding work on ImageNet and human-centered AI.

---

Key Discussion Points and Insights

Why Launch World Labs? The Mission and Timing

• Building New Technology: Dr. Li was compelled to found World Labs as she sees this as a “critical and fun and exciting moment to build some extraordinary technology that everybody can use,” especially with spatial intelligence (“the kind of 3D world models that can empower so many people and use cases”) ([00:46]).
• Pioneer Team: She highlights her excitement at working with a group of “extraordinarily brilliant young technologists.”

Defining Spatial Intelligence

• What is Spatial Intelligence? Dr. Li defines it as “the ability to understand, reason, interact and generate 3D worlds”—fundamental to human and animal intelligence and crucial for AI to be complete ([01:40]).
• Plausibility & Physics: Spatial intelligence requires that generated worlds be “realistically accurate or plausible,” capturing geometry and physics ([03:59]).

Quote:
“Without spatial intelligence, AI would be incomplete.” — Dr. Fei-Fei Li [01:40]

The 3D Generation Challenge

• World Models: World Labs is focused on building foundational models for 3D world generation—“cracking one of the hardest problems in AI”—believing this unlocks a host of spatial intelligence applications ([02:59]).

Neuroscience and Visual Intelligence

• Rooted in Biology: Spatial intelligence is deeply rooted in evolutionary biology and cognition, and remains a fundamentally difficult problem even in animals and humans ([04:40]).
• Visualization Limitations: Even humans can struggle to recreate complex 3D worlds from memory—training and talent are critical ([04:40]).

The Unsolved Frontiers: Beyond Language and Spatial Intelligence

• Language Solved, Spatial Next: Dr. Li notes language modeling is largely “solved to a huge extent,” but spatial intelligence and emotional intelligence remain difficult ([07:08]).
• Distributed Intelligence: There’s interest in distributed forms of intelligence, both biological and artificial, questioning the centralization seen in current models ([08:56]).

Robotics, Simulation, and the Data Pyramid

• Robotics Future: Dr. Li is confident “humanity will move into an age where we have it with robots”—with robots of many shapes, not just humanoids ([09:56]).
• Multimodal Learning & Simulation: Robotic intelligence will be built from hybrid data sources—simulation and synthetic data are underrated assets ([09:56]).
• Haptics Matter: The underappreciated need for haptics (“especially if we want to do manipulation, not just navigation”)—integrating touch with vision/perception ([10:52]).

Quote:
“Robotics is a highly multimodal system... haptics... is absolutely critical.” — Dr. Fei-Fei Li [10:52]

Robot Morphology: Many Forms vs. Human-like

• Optimization: Dr. Li predicts diversity in robotic forms—matching form to task is key for energy efficiency (robots underwater should be like fish, etc.) ([12:28]).

Commercial Applications of 3D World Modeling

• Creativity Empowered: The biggest near-term impact may be “superpowering” creativity: “designers, 3D artists, VFX artists, marketing talents, game developers...” ([13:42]).
• Metaverse, AR/VR: The bottleneck for immersive experiences is content creation—3D modeling/foundation models could break this wide open ([14:50]).

World Models and Reinforcement Learning

• Generalization via World Models: Plausible 3D world models can enable scalable reinforcement learning for more generalizable agents ([15:44]).
• Design as RL: Design tasks are deeply spatial and benefit from optimization and RL approaches ([15:44]).

Challenges in Building 3D World Models

• Data Scarcity: Unlike text/image data, 3D world data is rare—requiring sophisticated data engineering, acquisition, and synthesis ([16:41]).
• Productization Difficulty: 3D is complex and active, making it harder to package for end-users compared to language/text tools ([16:41]).

Human-Centered AI & Social Impact

• Human Collaboration: Dr. Li’s vision is for “AI to collaborate and superpower people,” especially in sectors like healthcare ([32:46]).
• Ethics and Values: The foundation must be justice, prosperity, and human relationships, with AI as a tool for amplification—not replacement ([32:46]).

Quote:
“I want to build a world that AI collaborates and superpowers people. I still believe our… world needs to be human centered.” — Dr. Fei-Fei Li [32:46]

---

Notable Career Reflections and Memorable Moments

Revisiting ImageNet and "Fearless" Research

• ImageNet Origin Story: Dr. Li shares the painstaking process of assembling the original 101-object dataset for her PhD, using a dictionary for category selection and help from her mother to clean data ([19:38]).

Quote:
“At some point I got so desperate I just asked my mom… She helped me to do some of that.” — Dr. Fei-Fei Li [21:25]

• The Impact of ImageNet: She reflects on “early struggles” (including skepticism and tenure worries), mechanical Turk, eventually being validated by the field’s embrace and breakthroughs like AlexNet ([23:36]).
• Language-Image Convergence: She celebrates the “captioning and writing stories of the visual world” breakthroughs in her lab (with Andrej Karpathy and Justin Johnson), something she once thought a “lifetime problem”—solved far sooner than expected ([25:31]).

Career Lessons for Researchers

• Moonshots Still Matter: Dr. Li argues that creativity and risk-taking in academia are still possible and necessary:

“Be fearless. Scientists, technologists, and entrepreneurs have to be fearless…” ([28:12])
“If you're too rational, it's not courageous enough… but if you're completely crazy, then… many things… can go wrong…” ([28:53])

Building World Labs Culture

• Who to Hire: Fei-Fei Li seeks talented engineers, researchers, and product talent, emphasizing cognitive diversity and fearlessness ([29:51]).
• Assessing Fearlessness: You can sense it in candidates’ questions, ambition, and their comfort with uncertainty ([31:10]).

---

Timestamps for Key Segments

| Segment / Topic | Timestamp | |---------------------------------------------------|-------------| | Why Start World Labs + What is Spatial Intelligence| 00:46–02:54 | | 3D Generation as a Foundational AI Challenge | 02:59–03:28 | | Neuroscience Perspective on Spatial Intelligence | 04:18–06:43 | | Where AI is Still Lacking (esp. Emotional Intelligence) | 07:08–07:40 | | Robotics, Simulation, and Haptics | 09:56–11:53 | | Robot Morphology & Form Factor Discussion | 11:53–13:25 | | Commercial Applications of World Models | 13:42–15:33 | | Challenges in 3D World Data and Engineering | 16:22–18:09 | | ImageNet Creation and Career Milestones | 19:38–25:31 | | Advice to Researchers – “Be Fearless” | 28:12–29:32 | | Building a Diverse, Fearless Team at World Labs | 29:51–31:10 | | Human-Centered AI & Future Optimism | 32:05–34:45 |

---

Notable Quotes

“Without spatial intelligence, AI would be incomplete.”
— Dr. Fei-Fei Li [01:40]

“Language is solved to a huge extent, and 3D to me is as critical and difficult as language. ...the entire space of emotional intelligence is something that I don't even know how to begin to solve.”
— Dr. Fei-Fei Li [07:08]

“Robotics is a highly multimodal system... what is truly underappreciated in my opinion is haptics.”
— Dr. Fei-Fei Li [10:52]

“We should be a little more imaginative than just humanoids… My hypothesis is… the requirements of different tasks are so vast that… sticking with one form is energy inefficient.”
— Dr. Fei-Fei Li [12:28]

“Be fearless. Scientists, technologists, and entrepreneurs have to be fearless.”
— Dr. Fei-Fei Li [28:12]

“I want to build a world that AI collaborates and superpowers people. I still believe our… world needs to be human centered where love, relationship, just prosperity across all communities… and these are really important values.”
— Dr. Fei-Fei Li [32:46]

---

Conclusion

This episode offers a compelling look into the next big paradigm in AI—spatial intelligence—and the foundational work needed to make AI systems that understand and interact with our 3D world. Dr. Fei-Fei Li shares not only technical aspirations but also her philosophy around research courage, human-centered values, and practical applications ranging from creativity to healthcare. Her optimism and realistic take on both technological and social challenges make this essential listening for anyone interested in the frontiers of AI.

Transcript

[Sarah Guo]

Hi, listeners, and welcome back to no Priors. Today's guest is Dr. Fei Fei Li, a pioneer in computer vision and deep learning. She created ImageNet, the groundbreaking data set that helped spark the deep learning revolution. Fei Fei is a Stanford professor and the co director of the Stanford Institute for Human Centered AI. She's also led AI Google Cloud, advised international policymakers, and recently co founded World Labs, a company dedicated to developing spatially intelligent AI. Fei Fei, thank you for joining us today.

[Dr. Fei-Fei Li]

Well, thanks for inviting me. This is going to be fun.

[Sarah Guo]

So you have made extraordinary contributions to science and policy over the past two decades. I'll start with the biggest question, like why start a company now?

[Dr. Fei-Fei Li]

Because in my heart I want to build. I see this as such a critical and fun and exciting moment to build some extraordinary technology that everybody can use and I believe so much in spatial intelligence and the kind of 3D world models that can empower so many people as well as so many use cases. And I think that's just, it's going to be really exciting. And I can do that with an extraordinary, extraordinarily brilliant group of young technologists.

[Sarah Guo]

I want to come back to the people you're working with because I know some of your co founders and was trying to convince them desperately to start a company a while back, and then they were like, oh, no, we have a bigger mission now with Fei Fei. What is spatial intelligence? Can you define it for a broader audience?

[Dr. Fei-Fei Li]

Spatial intelligence to me is the ability to understand reason and interact and generate 3D worlds. Because our world, fundamentally, no matter how you say we can project it, fundamentally, is 3D, and it's 3D because physically it's 3D. And digitally, if there is a true 3D representation, then we can make a lot of things happen more easily, whether it's designing or creation or navigation or simulation or the experiencing of AR VR. All this to me is part of spatial intelligence. And again, I think it's what really excites me is humans have spatial intelligence. It's part of our core intelligent capabilities. Animals have a spatial intelligence. The entire journey of evolution also is deeply intertwined with the evolution of spatial intelligence. So it's so fundamental. Without spatial intelligence, AI would be incomplete.

[Ilana Nesher]

How does that translate into what you're doing with your company? Or is there anything you can share in terms of what that means relative to what you're building?

[Dr. Fei-Fei Li]

Yeah, so we're cracking one of the hardest problem in AI, which is actually making world models that are fundamentally 3D, because once you can crack that problem, you can unlock a lot of spatial intelligence problems. So we are the first company we know of that is solving the 3D generation foundation model problem.

[Sarah Guo]

I have many questions, but since you are describing this first as 3D's criticality to understanding the world, does that imply you feel that the world models that world Labs will create or others in academia or in companies will create or will someday be realistically accurate, represent physics and understanding of the world that we can do many more things with?

[Dr. Fei-Fei Li]

Yeah, it should. It should be realistically accurate or plausible. So you can create a fantastical world, but it should be plausible because the geometry and the physics of it need to be plausible. And that is fundamental to spatial intelligence.

[Sarah Guo]

Does that imply you have a particular point of view, from a neuroscience perspective, of how fundamental visual. I mean, you've always been a leader in computer vision, but in how important visual intelligence is versus, let's say, large language models and textual intelligence?

[Dr. Fei-Fei Li]

I actually do. I think from a neural and cognitive science point of view, that spatial intelligence is a really hard problem that evolution has to solve for animals. And what's really interesting is I think animals have solved it to an extent, but not fully solved it. It's one of the hardest problem because what is the problem animal has to solve? Animals have to evolve the capability of collecting lights in something which we call eyes, mostly. And then with that collection of eyes, it has to reconstruct a 3D world in their mind somehow so that they can navigate and they can do things. And of course they can interact. For humans, we're the most capable animal in terms of manipulation. We can do a lot of things. And all this is spatial intelligence. To me, that's. That's just rooted in our intelligence. What is interesting is it's not a fully solved problem, even in animals. For example, for humans, if I ask you to close your eyes right now and draw out or build a 3D model of the environment around you, it's not that easy. We don't have that much capability to generate extremely complicated 3D model till we get trained. There are some of us, whether they're architects or designers or just people with a lot of training and a lot of talent, and that's a hard thing to do. And imagine you do it at your fingertip much more easily and allow much more fluid interactivity and editability. That would just be a whole different world for people. No pun intended.

[Ilana Nesher]

Are there other big areas like spatial intelligence that you feel haven't been as developed as they could be from a model perspective or other sort of missing Gaps that you think, in general, as we think, as we build this sort of AI future, we should focus on over time or people should build out. I was just wondering, in addition to sort of 3D and world generation and other big problems like that, because it feels like there are a few big things that we've solved for over time and other things we're working on, we're.

[Dr. Fei-Fei Li]

Sort of solving language, I would say. Language is solved to a huge extent, and 3D to me is as critical and difficult as language. So what else that's not solved? I mean, the entire space of emotional intelligence is something that I don't even know how to begin to solve.

[Ilana Nesher]

I know a lot of people who haven't solved it.

[Dr. Fei-Fei Li]

So that's when AGI is achieved. Yeah. So that's another one.

[Sarah Guo]

And I can tell you the training data for that is not going to come from Silicon Valley. People.

[Dr. Fei-Fei Li]

Don'T underestimate Silicon Valley. Yeah.

[Sarah Guo]

So I'll put myself in this bucket, but I think we probably need a broader set of people.

[Dr. Fei-Fei Li]

Yeah, no, that I agree. But these are the three. Three big buckets. To be honest, It's. I don't know. What do you think, Ilana Nesara?

[Ilana Nesher]

I think it depends a lot on what you encapsulate in each model. So I agree with your framework in terms of those three. And then certain things, like the spatial intelligence, I'm assuming, also delves into different types of physics, simulation and simulations of the world. And those are big areas that I think a lot of people aren't working on that I think are really interesting or important. And there's sort of the macro and the micro scale of that. The micro scale eventually becomes material sciences and other very different types of things from what you're talking about, where it's more molecular modeling.

[Dr. Fei-Fei Li]

Right. And also somewhat goes out the current definition of AI, which I do think they'll be empowered by AI. Of course, there's robotics, but robotics is very much a system integration problem as much as, even if you look at animals, it's not just the compute in the brain per se. Right.

[Ilana Nesher]

Yeah. A lot of these things seem much more distributed in terms of spatial intelligence relative to specific systems that animals have. And in some cases, to your point, not. Not as centralized as one would think. So it's very interesting to start thinking in terms of those models of more distributed intelligence across an organism versus a cns. But yeah, I think it's very interesting stuff.

[Sarah Guo]

You've also done work in this field, Fei Fei of robotics and physical intelligence. I Think of the data hierarchy for robotics foundation models and actuation as people want to of course use video. Right, because that is what is available to us. There's a big question on simulation and how much you can get from that. Today, perhaps people do not see the future of the quality and the physics that are going to be available to us. And then there's close to embodied different forms of teleop and then embodied data collection. Is that the hierarchy you have in your mind or do you think people underestimate simulation and world models for the future?

[Dr. Fei-Fei Li]

Yeah, great question. First of all, like you said, I do work in robotics, especially in my lab at Stanford. I have no doubt that humanity will move into an age where we have it with robots. And also the world robot is not humanoid per se. Robots taking all kind of forms and shapes. Actually, a few years ago my lab wrote a really fun paper about morphological intelligence is where the morphology of an agent actually can change by optimizing the tasks they're trying to achieve. So we should be a little more imaginative than just humanoids said that how to train robot. You mentioned this whole data. Some people call it data pyramids or data cakes or whatever. I agree. I think it's going to be a hybrid of many different forms of data. I also think simulation is underrated. Actually, it's not underrated by a lot of experts and people in the field. If you look at a lot of robotics companies, they are working on simulation and synthetic data. I also think we have to be also aware that unlike language models or even unlike spatial intelligence foundation models, robotics is a highly multimodal system that I think what is truly underappreciated in my opinion is haptics is there's so much, especially if we want to do manipulation, not just navigation. I think haptics, data and the ability to really integrate haptics into vision and perception and spatial data is absolutely critical.

[Ilana Nesher]

One thing that you said that I thought was really interesting is how many different, what are the different morphological forms that a robot may adopt? And there's sort of two counterarguments people make in terms of the potential future. One argument is that from a supply chain perspective and managing builds and scale of manufacturing, you're going to have many fewer form factors. And the other argument is the economic value of specialization is very high and therefore there'll be thousands and thousands of different form factors as we move to sort of a robot driven future. Do you have a point of view on where we're likely to land between those two viewpoints?

[Dr. Fei-Fei Li]

I think we're going to gradient descend into optimization of productivity and efficiency. My hypothesis is that the requirements of different tasks are so vast that having very few form or sticking with one form is energy inefficient. And a lot of tasks can be done and should be done by much more energy efficient form factors. Just an extreme and trivial example of if we put robots underwater, they should not be in the shape of humans, they better be in the shape of fish. Just think about energy efficiency. The same with flying. I don't think human form is our airplanes are becoming more and more robots and so I do think there's going to be diversity.

[Sarah Guo]

Robotics is one potential application for the future. You're scientist first, but also did the Twitter board involved in startups. What are the near term commercial applications that you can imagine for generating 3D worlds?

[Dr. Fei-Fei Li]

I believe creativity is a vastly exciting area where humans can be superpowered by AI and by spatial intelligence. Here I draw analogy with software engineering. If you look at today's success of LLMs in software engineering, including applications like Cursor and Windsurf and all that, what you see is a lot of collaboration between AI and humans. And then the collaboration comes in different levels of skill sets and all that. And I think creativity will be similar is that whether we're Talking about designers, 3D artists, VFX artists, or even marketing talents and game developers, there's so much need in designing and creating 3D space, and this is fundamentally such a hard problem even for the trained skilled people, that having a collaborator will be extremely fun if we do it right. And so I see creativity as an area that is really exciting. I also do think that a lot of what we're waiting for for Metaverse or XR AR VR is content creation. I understand hardware itself needs to continue to evolve, but I also think software we're looking for content creation and that lends itself so naturally to to 3D modeling and 3D or generative spatial models. And that's another interesting area to look into.

[Sarah Guo]

Do you have a strong point of view on whether or not world models are an interesting answer to scalable RL for more generalizable agents?

[Dr. Fei-Fei Li]

I actually do think this is like I said, AI is not complete without spatial intelligence because humans interact in 3D worlds and in the digital world we need all kinds of interaction. Take design as an example. It's a deeply. When we are thinking about design, there's so much we are optimizing for in our mind's eye, whether it's beauty or efficiency or Optimization or whatever it is. And that lends itself pretty naturally to RL settings.

[Sarah Guo]

What are the biggest challenges in, I guess, trying to go down this path of designing and training world models? I imagine one is like, you worked on images, you worked on video, but we have images and we have video, and we don't have lots of 3D worlds in a format I assume you're building.

[Dr. Fei-Fei Li]

Yeah, data is absolutely a challenge. You're totally right about that. To create world models, 3D foundation models, we require more and more sophisticated data engineering, data acquisition, data processing, and data synthesis. I am envious of my NLP LLM colleagues that the data is so abundant on the Internet, and we don't necessarily have that luxury. So that's definitely one challenge. Another one challenge is that 3D. This is kind of ironic, right? Every one of us use 3D every day, like, in so many settings. Basically, you open your eye and the whole life that you experience is 3D, even when we type on the computer or stare at a screen all the time. Yet it's still not as easy a form factor to deliver in the hands of people compared to language. The language is just so easy. And it's also a very active form of. It's not a passive consumption of viewing. Nobody wakes up and say, I'm just gonna sit here and watch 3D, you know? So that creates challenges for productization and how to do it in the right way.

[Sarah Guo]

Were you ever, like, a Second Life player or anything?

[Dr. Fei-Fei Li]

I'm not a gamer, but my kids love Minecraft.

[Sarah Guo]

I was gonna ask you if there was, like, a world that you want to experience or imagine.

[Dr. Fei-Fei Li]

That's a great question, Sarah. You know, I would love to see worlds. I love seeing worlds I don't see, for example, like, zooming in and in and into, like, microscopic worlds, or, you know, go into the inside of an engine, you know, knowing how the actual engine is. I know, of course, I know theoretically how it works, but seeing it with my own eyes, experiencing it, or even. You might laugh at this. I want to be inside a dishwasher and just experience what that is. All this can be done in a virtual way. If we managed to create world models of anything.

[Sarah Guo]

Okay, I think a lot, and I both want to talk a little bit about your past career and maybe some insights for anyone doing research or trying to have an impact within AI. Right before this, I asked Andrej Karpathy what I should ask you, and he said, fei. Fei is really magic about ambition and thinking about data. You should ask her about her PhD like, and the creation of that 101 data set with Pietro, because it's instructive. So I have to ask you about that.

[Dr. Fei-Fei Li]

You know, first of all, I have to say it's always really the greatest thing when you're a student, is more well known and achieving so much more than you can. It makes me so proud, so very proud of Andre. I'm surprised he remembers my PhD work. So, yes, it's true. It's, well, gosh, it goes back to 2003 ish. And the world was just barely scratching the surface of Internet. And data was not much of a thing. But doing computer vision, my PhD work was really trying to get object recognition to work. That's the problem of calling out cats and dogs and microwaves and chairs and all that when you're presented with a picture. And we were beginning to hypothesize that data matters, but we had no idea. There's no scaling law. We had no idea how far data can go. All we wanted is if we have a machine learning algorithm, whether it's a neural network or Bayes Net at that time was very popular, or support vector machine. We need some data to train. There was no data to train. As a PhD student, you want to graduate? And Pietro was like, well, Fei. Fei, curate a data set. And I was thinking, yeah, I do need to curate a data set, because every dataset out there is so tiny. I'm just not convinced. And Pietro and I were just talking. Is it 15 different things or 30 different things? And then God forbid, the PhD advisor set the three digit number 100. And I was like, you know, that's a lot of work. But deep in my heart, I know he's right. From a mathematical point of view, is pushing the model to generalize. We need enough data at least. So I did write about this process in my book the Worlds. I see that I stumbled upon a dictionary somehow. And it really was for my own English study that the dictionary, I think it's the Webster dictionary, if I'm not wrong. It just kind of randomly has depiction of a visual depiction of some words. I don't even know what rule they follow, to be honest. Some are flowers, some are bicycles, some are dogs. I was like, okay, this is actually, you can call it a cheat or a tool. I grabbed 101 of those words. And that really made my PhD advisor kind of chuckle because he's like, yeah, you just want to do one more than I asked for to dare me. So that's what I did. And I got to say that I still remember I downloaded or you know, tried, you know, from Google and Google was so new at that point and the Google image search were so terrible at that point, you know, compared to today. And I had to do so much cleaning at some point I got so desperate I just asked my mom to do clean the image cleaning because I wrote a little interface on the computer. She doesn't know computer, but at least she knows click, click. So she helped me to do some of that.

[Ilana Nesher]

I mean you've had one of the most storied careers in AI and to your point, many of your students have similarly gone on to do really great things across the field, across industry, across the world. What are two or three moments that you think of when you think back on your career to date? And obviously there's still a lot of career to come. But I'm just sort of curious, I mean obviously there's a lot of things that you did in terms of sort of image and visual recognition related systems and also. But I'm just sort of curious, like when you think, think of the last 20 years, what stands out the most? Just given everything that you've done.

[Dr. Fei-Fei Li]

Oh, thank you for asking that question. Of course, Image that is one of those images that is consists of multiple moments from the early struggles and being told I will not get tenure to actually realizing I'm as a mechanical Turk comes to rescue to the moment of Alex Nett winning and also to a couple of years ago I was at an event in Toronto with Geoff Hinton and he said publicly how that was so defining and he was almost a little bit apologetic. That image that was not as recognized as neural networks. So that journey is very validating. And for scientists the validation is not about recognition or awards, it's that you made a difference. Like that conjecture that no one believed in, that hypothesis that no one believed in. We were able to make it happen. So that's one thread just to make.

[Sarah Guo]

Sure for any people from the business world that are not familiar with it. Imagenet is a large scale dataset with millions of labeled images. Images across thousands of categories, not just 101. Right.

[Dr. Fei-Fei Li]

15 million labeled images.

[Sarah Guo]

15 million labeled images. Thank you. Fei. Fei. That led to amazing breakthroughs in deep learning, in particular alexnet and lots of progress in the field of.

[Ilana Nesher]

Yeah, driven a lot of machine vision forward. And I actually remember in 2016 or 2017 I used to show a slide which was the history of AI or back then it was CNNs and RNNs and just GANs were kind of going. And I had Imagenet and Alexnet as like one of the seminal moments of this very small number of events that really defined AI progress. And obviously now we have Transformers as part of that and maybe diffusion models or something. But it was such a big breakthrough.

[Dr. Fei-Fei Li]

Yeah, thank you. Another moment I'm very proud of was actually Andre and also Justin Johnson and their dissertations. It's where in my opinion, the first time that language and images converged by captioning and writing stories of the visual world. It was significant for me for two reasons. One is that I literally thought, I kid you not, at the end of my PhD, I thought if I can live to a 100 year old, that was the problem we might be able to solve, which is storytelling of pictures. So I entered my career like my first year assistant professor thinking, okay, I'm going to do imagenet to solve object recognition and then I'm going to spend the rest of my entire career solving this problem of storytelling. And then by the time Andre and then a little later Justin Johnson entered my lab, that was around 2013, 2014, the beginning of deep learning. And then suddenly the combination of sequential model at that point is lstm. It's not transformer models, but LSTM and CNN just had this blasted open the image captioning work and Andre and my work were the first together with Google's. That was out of the door. And that was really, to me, I almost had. It made me so proud. I almost had a crisis which is like, what am I going to do for the rest of my 70 years or 65 years? So that was really exciting how fast the field has, you know, evolved.

[Sarah Guo]

Can I ask you one more question about this? Just because you have, you know, made this amazing progress, like very efficiently, right. Like you and I have offline talked before about how you feel it's really important for there to be, you know, moonshots and creativity in AI research beyond like very large funded corporate labs, let's say. And you know, you pointed to several moments that they come from like creativity and research in academia. What advice do you have for people about whether or not there's still opportunity for that or, you know, it's all just $10 billion training runs from here.

[Dr. Fei-Fei Li]

My singular advice, and I still say that in my comedy in my lab is be fearless. I think scientists and technologists and entrepreneurs have to be fearless. You know, eventually you have to figure out do you need 10 billion dollar runs? Or then you come to Sarah and to ask for funding probably a lot, but both. Yeah, yeah. Or you have to figure out, you know, I don't know, data. Sometimes fearless is this very interesting position where you're somewhat delusional and crazy, but somewhat just rationally bold. And it kind of is in between. Because if you're too rational, it's not courageous enough, you're not identifying problems that are big enough. But if you're completely crazy, then, I don't know, there's many things that can go wrong. So be fearless, be courageous. To me, that is, you know, even as old as I am, that's how I feel. I started my startup World Apps is I want to be fearless and solve this problem of spatial intelligence.

[Ilana Nesher]

As part of problem solving, you've worked with some of the best AI researchers in the world over time and best engineers. How do you think about that in the context of your company? Like, what sorts of people are you trying to hire? Are there open roles currently? Undoubtedly. It's an amazing team. I'm just curious, like, what sorts of folks you want to add and how you're thinking about that over time.

[Dr. Fei-Fei Li]

Yes, we have open roles and we would love to hire the best engineers as well as product thinkers at this point for our company. So if you're an engineer or AI researcher or product talent out there passionate about joining the most talented team and solving this problem, please join us. So who do we hire? First of all, we really do hire in diversity of thinking and this is where you as an AI company. But if you look under the hood, we've got computer graphics experts, we've got computer vision experts, we've got data experts, we've got generative AI experts, we've got machine learning infra experts, we've got optimization. So it's actually really important to hire a diverse group of really talented people because a problem as hard as spatial intelligence is not a homogeneous problem. Like it takes talents of all kinds of background to solve it. And then I also just like I look for fearlessness, like, you know, we all have.

[Ilana Nesher]

How do you do that? Like, how do you identify if somebody has fearlessness in their background or in their thinking processes?

[Dr. Fei-Fei Li]

It's in their background. You talk to them. You can sense someone as fearless. You know, you can sense what drives them. You know, you can sense the questions they ask. If they are. If they start to asking you a lot of things about, I don't know how to get this done. I mean, of course you have to ask those questions because you want to get it done, but if, if you sense that it comes from the, the, the, the, the, the point of view of being scared of solving that, then that's not fearlessness, but those fearless people, they are creative, they're ambitious, they're not afraid of the uncertainty or the unknown. And I really love that.

[Sarah Guo]

Well, I think a lot and I, we try to make a business of doing business with fearless people and hopefully those that are technically creative. One last broader question for you because I think an important part of your work has been also thinking about how to bring more people into AI. Co directing the Stanford center for Human Centered, Human Centered and Artificial Intelligence. What is your most like if you picture, not to use a pun on the book, but if you picture the world several years out from your last set of predictions, what's your most optimistic view of what human centered AI looks like?

[Dr. Fei-Fei Li]

Yeah, thanks for asking. In fact, that is another point of my career I feel very proud of is the founding of Human Centered AI Institute. Hai. And also the continued movement towards that way of thinking. I think I want to build a world that AI collaborates and superpowers people. I still believe our world, our human world needs to be human centered where love, relationship, just prosperity across all communities. These are really important justice and these are really important values. And I don't think any piece of machinery, whether it's AI or airplane or biotech should take those away. But with those critical values in mind, having AI to superpower us is really, really important because there's so many unsolved problems. One application area I had worked on is healthcare. For example, at Stanford, if you look at healthcare from drug discovery to cure diseases to diagnosis that can reach all people in the world, to treatment that can be accessible to all people, people in the world to the whole healthcare delivery, how to make aging better, how to take care of chronic diseases, how to deal with mental health, all of this, we do not have an issue of excessive humans or anything. We're lacking help, we are lacking scientific discovery, we're lacking diagnosis, we're lacking precision medicine, we're lacking safer and more effective ways of healthcare delivery and aging help and all that. And I, that's what I believe. I think AI is a tool to help people.

[Sarah Guo]

Yeah, I think a lot. And I are collectively invested in a series of companies that I hope will be useful here, from a bridge to open evidence to latent. But as you said, there's a, there's a huge spectrum of problems. And yes, honestly I've been less optimistic about the adoption of, you know, generally technology and healthcare for the last 15 years. But it does feel like this time it's different. And actually it's just massively net good here.

[Ilana Nesher]

Yeah. I actually started a digital health company before this. So my hope is finally a lot of the things that people have been talking about for decades will come to fruition, and it seems like AI is a great delivery mechanism for that.

[Dr. Fei-Fei Li]

Totally. Totally.

[Sarah Guo]

Well, thank you so much, Fei.

[Ilana Nesher]

Fei, it was fantastic.

[Sarah Guo]

This has been inspiring and great to hear a little bit more about World Labs as well.

[Dr. Fei-Fei Li]

Thank you. Thank you, Hilal. Thank you, Sarah.

[Sarah Guo]

Find us on Twitter nopriorspod. Subscribe to our YouTube channel. If you wanna see our faces, follow the show on Apple, Podcasts, Spotify, or wherever you listen. That way you get a new episode every week and sign up for emails or find transcripts for every [e-mail].