No Priors Ep. 117 | With Co-Director of Stanford's HAI & Founder of World Labs Dr. Fei-Fei Li

1. 0:00 – 3:00

   Why and what Fei-Fei is building

   1. SGSarah Guo0:00

      (music plays) 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 dataset 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 at 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.

   2. FLFei-Fei Li0:33

      Well, thanks for inviting me. This is gonna be fun.

   3. SGSarah Guo0:36

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

   4. FLFei-Fei Li0:46

      Because in my heart I wanna 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 gonna be really, um, exciting and I can do that with an extraordinarily, uh, uh, e- extraordinarily brilliant group of young technologists.

   5. SGSarah Guo1:23

      I wanna come back to, you know, the people you're working with because I, I, uh, know some of your co-founders and was, uh, you know, 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?

   6. FLFei-Fei Li1:40

      Spatial intelligence, to me, is the ability to, um, to understand reason and interact and generate 3D worlds because our world fundamentally, no matter how you say we can project it, fundamentally it's 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 curation or navigation or, or simulation or, or the experiencing of, um, uh, 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. We are... it's part of our, uh, core intelligent capabilities. Animals have a spatial intelligence. The, the entire journey of evolution also is, um, deeply intertwined with the evolution of spatial intelligence, so it's so fundamental. Without spatial intelligence, AI would be incomplete.

   7. EGElad Gil2:54

      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?

   8. FLFei-Fei Li2:59

      Yeah,

2. 3:00 – 6:44

   World models at World Labs

   1. FLFei-Fei Li3:00

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

   2. SGSarah Guo3:28

      I have many questions, but since you, um, are, you know, describing this, uh, first as, you know, th- the, you know, 3D's criticality to just sort of understanding the world, um, does that imply you, you feel that the world models that, you know, World Labs will create or, or others in academia or in, uh, companies will create, will someday be, like, you know, realistically accurate? Like represent physics and understanding of the world that we can do-

   3. FLFei-Fei Li3:57

      It should.

   4. SGSarah Guo3:57

      ... many more things with?

   5. FLFei-Fei Li3:59

      Yeah, it should. It, it should be realistically accurate or pla- uh, uh, plausible so you can create a fantastical world, but it should be plausible 'cause the geometry and the physics of it need to be plausible and, uh, um, and that is fundamental to spatial intelligence.

   6. SGSarah Guo4:18

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

   7. FLFei-Fei Li4:40

      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, um, what is the problem animal has to so- 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 m- manipulation. We can do a lot of things, and all this is spatial intelligence. To me, that's, um, that's just rooted in, in our intelligence. What is interesting is it's not a fully solved problem, even animals. We, uh, for example, uh, for humans, right, um, if I ask you to close your eyes right now and draw out or, or, or build a 3D model of the environment around you-

   8. SGSarah Guo6:04

      Mm-hmm.

   9. FLFei-Fei Li6:04

      ... it's not that easy.We don't have that much capability to generate extremely complicated 3D model till we get trained. You know, there are some of us, whether they're architects or, or designers or just people with a lot of training and a lot of talent, and that's, that's, uh, that's a hard thing to do. And imagine you do it at your fingertip much more easily and allow much more, uh, fluid, uh, interactivity and editability. That would just be a whole different, uh, world for he- people, no pun intended.

   10. EGElad Gil6:43

       Are there other

3. 6:44 – 9:16

   Missing gaps in the AI future

   1. EGElad Gil6:44

      big areas like, um, 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, um, focus on over time, or people should build out? I was just wondering, in addition to sort of 3D and world generation, and other, other big problems like that, 'cause it feels like there are a few big things that we've solved for over time, and other things we're working on.

   2. FLFei-Fei Li7:08

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

   3. EGElad Gil7:31

      I know a lot of people who haven't solved it, so. (laughs)

   4. FLFei-Fei Li7:34

      That's when AGI is achieved. (laughs)

   5. SGSarah Guo7:37

      I can tell you the training data for that is not gonna come from Silicon Valley people.

   6. FLFei-Fei Li7:42

      (laughs) Don't underestimate Silicon Valley. (laughs) Yeah, so-

   7. SGSarah Guo7:48

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

   8. FLFei-Fei Li7:52

      Yeah. No, that I agree. But these are the three, three big buck- buckets. To be honest, it's... I don't know. What are you thinking on this, Sara?

   9. EGElad Gil8:03

      I think it depends a lot on, um, what you encam- i- encapsate in each model. So I agree with your framework in terms of those three, and then certain things like, um, you know, the spatial intelligence, I'm assuming also delves into different types of physics simulation and simulations of the world, and that, you know, like, those are big areas that I think a lot of people aren't working on that I think are really interesting or important, so... Um, and there's, 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 or... yeah.

   10. FLFei-Fei Li8:32

       Right. And also, someone 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 a, um... You know, even if you look at animals, it's not just, uh, the compute in the brain per se, right?

   11. EGElad Gil8:52

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

   12. SGSarah Guo9:11

       You've also done work in, in this field, Fei-Fei, of, of robotics and, like, physical

4. 9:16 – 16:15

   Robotics and physical intelligence

   1. SGSarah Guo9:16

      intelligence. I think of the data hierarchy for, you know, robotics foundation models and actuation as, you know, people wanna, of course, use video, right? Because that is what is available to us. There's a big question on, like, simulation and how much you can get from that today. Perhaps people, or do not see the future of like the quality and the physics that are going to be available to us. Um, and then there's, you know, close to embodied, uh, like different forms of tele-op and then, like, 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?

   2. FLFei-Fei Li9:52

      Yeah. Great question. First of all, I, like you said, do work in robotics, especially in my lab at, uh, Stanford. I have no doubt that humanity will move into an age where we cohabit with robots, and also-

   3. SGSarah Guo10:05

      Okay. Okay.

   4. FLFei-Fei Li10:05

      ... the world, the word robot is not humanoid per se. Re- robots take in all kind of forms and shapes, and actually a few years ago my lab wrote a really fun paper about morphological intelligence, is where the, the morphology of a, a, an agent actually can change by optimizing the tasks they're trying to achieve. So, so we should be a little more imaginative than just human- uh, uh, humanoids. Having said that, how to train robot, uh, you mentioned this whole data, some people call it data pyramids or data cakes or whatever, I agree. I think it's gonna be a hybrid of, uh, many different forms of data. I also think, uh, simulation is underrated. It's, um, uh, 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 simulated, uh, simulation and synthetic data. I also think we have to be, um, also aware that unlike language models or even unlike, um, spatial intelligence foundation models, robotics is a highly multimodal, um, uh, uh, system that I think what is truly underappreciated, in my opinion, is haptics, is there's so much, especially if we wanna do manipulation not just navigation, I think haptics data and the ability to really integrate haptics into vision and perception and spatial, uh, data is, is absolutely critical.

   5. EGElad Gil11:48

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

   6. FLFei-Fei Li12:23

      I think we're gonna gradient descending to optimization of productivity and efficiency. My hypothesis is that the requirements of different tasks are so vast that having very few form or, or sticking with one form is energy, 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, and trivial example. If we put robots under water, they should not (laughs) be the shape of humans. They'd better be in the shape of fish, right? (laughs) Just think about energy efficiency. And the same with flying. I don't think human form is, uh, our air- airplanes are becoming more and more robots. And so I, I, I do think there's gonna be diversity.

   7. SGSarah Guo13:21

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

   8. FLFei-Fei Li13:38

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

   9. SGSarah Guo15:29

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

   10. FLFei-Fei Li15:40

       I actually do think this. This is, uh, like I said, AI is not complete, uh, without spatial intelligence because, uh, um, humans interact in, um, in, uh, 3D worlds, and in the digital world, we need all kinds of interaction. You know, take design as a example. It's a deeply, you know, it has, um... When we are thinking about design, there is so much we are optimizing for in our mind's eye, whether it's beauty or efficiency or optimization or, or whatever it is, and that lends itself

5. 16:15 – 19:08

   Greatest challenges of 3D

   1. FLFei-Fei Li16:15

      pretty naturally to RL, um, settings.

   2. SGSarah Guo16:18

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

   3. FLFei-Fei Li16:37

      Yeah. Data is absolutely a challenge. You're totally right about that. Um, you know, to create world models, 3D foundation models, uh, we, we require more and more sophisticated data engineering, data acquisition, data processing, and data, uh, synthesis. So um, uh, I am envious of my, uh, NLP/LLM colleagues that their (laughs) , the data is so abundant on the internet, and we don't necessarily have that luxury. So that's definitely one, um, one challenge. Another one challenge is that, um, 3D i- th- th- this is kind of, um, ironic, right? Every one of us use 3D every day, like, in so many settings, and basically you open your eye and, and, and the whole life that you experience is 3D.

   4. SGSarah Guo17:30

      Okay.

   5. FLFei-Fei Li17:30

      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, uh, 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? (laughs) So, um, that, uh, creates challenges for, for, for productization and how to do it in the right way.

   6. SGSarah Guo18:05

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

   7. FLFei-Fei Li18:11

      I'm not a, I'm not-

   8. SGSarah Guo18:11

      (laughs)

   9. FLFei-Fei Li18:11

      ... a gamer, but my kids love Minecraft.

   10. SGSarah Guo18:15

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

   11. FLFei-Fei Li18:20

       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, um, going into the inside of a engine (laughs) , you know, knowing how the, the, the actual engine is, is... Like, I know... Of course, I know theoretically how it works, but seeing it with my own eyes, experiencing it or even, um... You might laugh at this, I wanna be inside a dishwasher (laughs) and just experience what, what that is. All this can be done in a, in a virtual way if we manage to create, you know, world models of anything.

   12. SGSarah Guo19:03

       Okay. I w- I, I think Elad both... Uh, Elad and I both want to talk a little bit about your past

6. 19:08 – 23:05

   Fei-Fei’s work in PhD in imagenet

   1. SGSarah Guo19:08

      career and maybe some, uh, insights for anyone doing research or trying to, you know, have an impact within AI. Right before this, I asked Andrej Karpathy what I should ask you, and he said, you know, um, "Fefi is really magic about ambition and thinking about data. You should ask her about her PhD, like, a- and the creation of that 101 dataset with Pietro, um, because it's instructive." So I have to ask you about that.

   2. FLFei-Fei Li19:34

      You know, first of all, I have to say it's always really s- the greatest thing when your student is, is, um, more well-known and, and achieving so much more than, than you can. It makes me so proud, so very proud of Andrej. Um, I was... So I'm surprised he (laughs) remembers my PhD work. So yes, it's true, it's, um... Well, gosh, it goes back to 2003-ish, and the world was just barely scratching the surface of, uh, internet and data was not much of a thing, but doing computer vision, we were... My PhD work was really trying to get object recognition to work. That's the, 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, um, and we were beginning to hypothesize that data matters, but we had no idea. There's no scaling law. We had no idea, um, uh, you know, how far data can go. All we wanted is if we have a machine learning algorithm, whether it's a neur- neural network or a Bayes' net, at that time was very popular, or a support vector machine, we need some data to train, and there was no data to train. And as a PhD student, you wanna, you know, graduate and, uh, and Pietro was like, "Well, Fefi, curate a dataset," and I, and, you know, um, I was thinking, yeah, I do need to curate a dataset because every dataset out there is so tiny, I'm just not convinced. And p- Pietro and I were just talking, you know, is it 15 different things or 30 different things? And then god forbid the PhD advisor said the three-digit number 100 and I was like, "You know, that's a lot of work," but I, deep in my heart, I know he's right from a mathematical point of view, is pushing the, the model to generalize, we need enough data at least. So, you know, um, I did write about this process in, in my book, uh, 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 hono- uh, 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, um, and that really made my PhD advisor kind of chuckle because he's like, "Ah, yeah, you just wanna do one more than I asked for to, you know, dare me." (laughs) So that's what I did. And I gotta say that I still remember I downloaded or, you know, tried th- y- you know, from Google, and Google was s- 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 (laughs) -

   3. EGElad Gil22:50

      (laughs)

   4. FLFei-Fei Li22:50

      ... the, the (laughs) , the image cleaning 'cause I, 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, to do some of that.

   5. EGElad Gil23:01

      Yeah, I mean, you've had one of the most storied careers in AI. And

7. 23:05 – 29:33

   Special moments in career

   1. EGElad Gil23:05

      to your point, many of your students have similarly gone on to do really great things across the field, across the industry, across, you know, the world. Um, what are two or three moments that y- you think of, uh, 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, uh, 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?

   2. FLFei-Fei Li23:32

      Oh, thank you (laughs) for asking that question. Of course, ImageNet is one of those, uh... ImageNet is consists of multiple moment from the early struggles and being told I will not get tenure to, um, to actually realizing Amazon Mechanical Turk comes to rescue, to the moment of AlexNet winning, and also to... A couple of years ago, I was at a event in, uh, Toronto with, uh, Geoff Hinton and he said publicly, like, how that was so defining and he, he was almost a little bit...... um, apologetic that even though that was not as recognized as, uh, 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 ma- able to make it happen. So that's one thread.

   3. SGSarah Guo24:35

      Just to make sure for any, like, you know, people from the business world that are not familiar with it, ImageNet was a large-scale... is a large-scale dataset with millions of labeled images across thousands of categories, not just 101, right? That-

   4. FLFei-Fei Li24:47

      15 million labeled images.

   5. SGSarah Guo24:50

      15 million labeled images, thank you, Fei-Fei, that, you know, led to, um, uh, amazing breakthroughs in deep learning, in particular AlexNet and lots of progress in the field of, um, uh, computer vision overall.

   6. EGElad Gil25:01

      Yeah, it drove a lot of machine vision forward and I actually remember, um, in 2016 or twen- 2017, I used to show a slide which was the history of AI or, you know, back then it was CNNs and RNNs and just GANs were, you know, kinda going and-

   7. SGSarah Guo25:13

      Yeah.

   8. EGElad Gil25:13

      ... I had, uh, ImageNet and AlexNet as, like, one of the seminal moments of, you know, this very small number of events that really defined AI progress. And obviously now-

   9. FLFei-Fei Li25:22

      Yeah.

   10. EGElad Gil25:22

       ... we have transformers as part of that and maybe diffusion models or something, but it's... it was, uh, such a big breakthrough.

   11. FLFei-Fei Li25:27

       Yeah. Thank you. Another moment I'm very proud of was actually Andre and, um, and also, uh, also Justin Johnson and, and their dissertations. It's where, in my opinion, the first time that language and images converged by captioning and writing stories of, of, uh, 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 when... if I can live to 100 year old, that was the problem we might be able to solve, which is storytelling of pictures. So I entered my, my, my career, like, my first year, um, uh, assistant professor thinking, "Okay, I'm gonna do ImageNet to solve object recognition and then I'm gonna s- spend the rest of my entire career solving this problem of, uh, storytelling." And then by the time Andre and then a little later Justin Johnson entered my lap, that was around 20, um, 13, 2014, the beginning of deep learning, and then suddenly the combination of, um, sequential model, at that point is, uh, LSTM. It's not (laughs) transformer models, but LSTM and CNN just had this... blasted open the image captioning, um, um, 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- I- I almost had... it was... made me so proud I almost had a crisis which is like, "What am I gonna do for the rest of my 70 years or 65 years?" (laughs) It's, uh... so that was really exciting how, how fast the field has, uh, has, um, um, you know, evolved.

   12. SGSarah Guo27:28

       Can I ask you one more question about this just because you have, um, uh, you know, made this amazing progress, like, very efficiently, right? Like, you and I have, uh, offline talked before about how, um, it... yo- 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, y- you know, y- you, you pointed to several moments that... they come from, like, uh, 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?

   13. FLFei-Fei Li28:07

       My singular advice, and I still say that in my company, in my lab, um, 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 runs (laughs) or then you come to Sara an- and to ask for funding.

   14. SGSarah Guo28:30

       (laughs) Probably a lot of both, yes.

   15. FLFei-Fei Li28:32

       (laughs) Yeah. Um, 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, and it, it kind of is in between because if you're too rational, uh, it's not courageous enough, you're not identifying problems that are, are big enough, but you're... if you're completely crazy then, I don't know (laughs) , there's so many, many things, um, that can go wrong. So, so be fearless, be courageous. I... to me that is, um, you know, even as old as I am, that's how I feel. I started my, uh, startup World Labs is I want to be fearless and solve this problem of spatial intelligence.

   16. EGElad Gil29:28

       As part of problem solving, you've worked with some of the best, uh, AI researchers in

8. 29:33 – 32:05

   Building teams

   1. EGElad Gil29:33

      the world over time, the best engineers, um, 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? I mean, Dataly, it's an amazing team. I'm just curious, like, what sorts of folks you wanna add and how you're thinking about that over time.

   2. FLFei-Fei Li29:47

      Yes, we have open roles and we would love to hire the best engineers and... as well as product thinkers, um, at this point for, for our company. So if you're a, a engineer or AI researcher or product, uh, talent out there passionate about joining the, the most talented team and, 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 know, we... you call us a 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, you know, uh, generative AI experts. We've got, uh, machine learning infra experts. We've got optimization. We've got... So it's actually really important to, uh, to, to hire, um, a diverse group of really talented people because a problem as hard as spatial intelligence is not a homogenous problem. Like, it takes talents of all kinds of background to solve it. And, and then I also just eli- like, I look for fearlessness.

   3. EGElad Gil30:57

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

   4. FLFei-Fei Li31:03

      It's in their background. You talk to them. You can sense someone is fearless. You know, i- i- i- 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," and... I mean, of course you have to ask those questions 'cause you wanna get it done. But if, if you sense that it comes from the, the, the, the, the, the point of view of, um, being scared of solving that, then that's not fearlessness. But, um, but those fearless people, they are creative, they're ambitious. They, they, they, they can... They're not afraid of, um, the uncertainty or the unknown. A- and I really love that.

   5. SGSarah Guo31:57

      Well, I think Elade and I, you know, we try to make, make a business of, uh, doing business with fearless people, and hopefully those

9. 32:05 – 35:46

   Human-centered AI

   1. SGSarah Guo32:05

      that are t- technically creative. Um, uh, one, one last broader question for you, because e- and I think an important part of your work, um, has been also, um, thinking how, how to bring more people into AI, you know, co-directing the, um, Stanford Center for, um, uh-

   2. FLFei-Fei Li32:21

      Human-centered.

   3. SGSarah Guo32:21

      ... Human-Centered in Artificial Intelligence. Uh, what is your most... Like, if you picture, you know, not to use a pun on the book, but if you picture the world, like, several years out from your, your last set of predictions, what's your most optimistic view of, uh, what human-centered AI looks like?

   4. FLFei-Fei Li32:39

      Yeah. Thanks for asking. In fact, that is another point of my career I feel very proud of is the, the founding of, uh, Human-Centered AI Institute, HAI, and also the continued movement towards that, uh, way of thinking. I think I wanna build a world that AI collaborates and superpowers people. I still believe our world, our human world needs to be human-centered, you know, where love, relationship, um, just prosperity across, you know, all communities. These are really import- justice and a- these are really important values, and I don't think any piece of machinery, whether it's AI or airplane or, or biotech, should take those away. But with that, those critical values in mind, having AI to superpower us is, is really, really important 'cause there's so many unsolved problems. Um, one application area I, um, I had worked on is, um, is, uh, healthcare, for example, at Stanford, right? If you look at healthcare, from drug discovery to cure diseases, to diagnoses that can reach all people in the world, to treatment that can be accessible to all 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.

   5. SGSarah Guo34:20

      (laughs) Yeah.

   6. FLFei-Fei Li34:20

      You know, 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.

   7. SGSarah Guo34:38

      Yeah. I think Elade and I are, um, collectively invested in a series of companies that I, I hope will be useful here, from Abridge to Open Evidence to Latent. But as you said, there's a, there's a huge spectrum of problems. And-

   8. FLFei-Fei Li34:50

      Yes.

   9. SGSarah Guo34:50

      ... honestly, I've been less optimistic about the adoption of, you know, generally technology in 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.

   10. EGElad Gil35:04

       Yeah. I actually started a digital health company before this, and 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. So...

   11. FLFei-Fei Li35:15

       Totally. Totally.

   12. EGElad Gil35:17

       Yeah.

   13. SGSarah Guo35:17

       Well, thank you so much, Fei-Fei.

   14. EGElad Gil35:18

       It was fantastic.

   15. SGSarah Guo35:19

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

   16. FLFei-Fei Li35:24

       Thank you. Thank you, Elade. Thank you, Sarah.

   17. SGSarah Guo35:27

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Episode duration: 35:46