I think the ability to learn is even more important. >> Yeah, >> AI has really changed it. For example, my startup when we interview a software engineer honestly how much I personally feel the degree they have matters less to us now is more about what have you learned what tools do you use how quickly can you superpower yourself in using these tools and a lot of these are AI tools what's your mindset towards using these tools matter more to Dr. Lee, it is nice to see you. Thanks for making the time. >> Hi, Tim. Very
nice to be here. Very excited. >> And we were chatting a little bit before we started recording about how miraculous and I suppose unfortunate it is. It's somehow we managed to spend three years on the same campus and didn't bump into each other. >> I know. And now I'm wondering which college you were at and which clubs. >> Oh, yeah. I was Forbes. I was in Forbes College. No, I was Forbes, too. >> Okay, this is for people who don't know what the hell we're talking about. There are these residential colleges where students are split
up when they come into the school. And Forbes was way out there in the sticks, right next to a fast food spot like 7-Eleven called Waw Wa >> Waw Wa >> and next to the commuter train. And then there's something called eating clubs at Princeton. People can look them up, but they're effectively co-ed fraternity/sorities where you also eat unless you want to make your own meals. And I was in Terrace. >> I was not any of that. But for those of you wondering why we didn't meet, we should say we were very studious students who
were only in the libraries. >> Yeah, we were very studious. I actually made my whatever it was $6 an hour at guest library working up in the attic. >> Tim, I work in the same library. I don't understand why we did not meet. >> That's really hilarious. Okay. So, well, now we're meeting. >> Did you change name or something? Maybe we did meet. >> I did didn't change my name, but here we are. >> Yes, >> we've reunited. That's wild that we didn't bump into each other. I was also gone for a period of time
because I went to Princeton in Beijing >> and went to the what was it? Capital University of Business and Economics after that. And so I was gone for a good period of time and then took a year off before graduating with the class of 2000. So still we had a lot of overlap. >> Yes. >> But let's hop into the conversation and this is a very perhaps typical way to start but in your case I think it's a good place to start which is just with the basics chronologically. Where did you grow up? And could
you describe your upbringing? Because based on my reading, your parents were pretty atypical for Chinese parents in my experience. Certainly. >> Yes. You know, a lot. >> Yeah. Could you speak to that please? >> I would say my childhood and leading up to the formative years is a tale of two cities. I grew up in a town in China called Chundu. I was born in Beijing but most of my childhood was spent in Chundu where it's very famous for panda bears and at the age of 15 my mom and I joined my dad in a
town called Precipity New Jersey. So I went from a relatively typical middleclass Chinese family, Chinese kid >> to become a new immigrant in a completely different world of all places, New Jersey, >> to learn a new language, to learn a new culture, to to embrace a new country. Mhm. >> And then from there on I went to Princeton as a physics major, but I did take some of the classes you took and and then went to Caltech as a PhD student to study AI and and the rest is history. I want to hear about both
your parents, but I want to hear a little bit about your dad because he seems like based on my reading a very whimsical sort of creative soul which is a sharp contrast in some ways to for instance I had Bo Sha on the podcast amazing entrepreneur and his father was I suppose what some folks might think of when they imagine not a tiger mom but like a tiger ad. So in the case of B's upbringing, his father was very strict, but if he if he, meaning Bo, won a math competition, then he would get extra
love and he would be allowed to have certain treats and things like that. >> Could you just describe your parents a little bit? >> First of all, clearly you read my book. Thank you for that. It is true. As a child, you don't realize as I was just going through my my own science memo, I was writing it, the more I wrote about it, the more I realized, oh my god, I really did not have a typical dad. >> My dad loved and still loves nature. He's just a curious. He finds humor and fun in
unserious things, you know, like he loves bugs, insects. Mh. >> Growing up in the 1980s in China, there isn't much abundance in terms of material resources. >> But my city Chundu was expanding. So we lived in apartment complexes at the edge of the city. Even though my dad and my mom worked in the middle of the city. So on the weekends, my dad and I would just play in the fields where there's still rice fields. There's water buffaloos. I had a puppy. really all my memory is just like fighting bugs really and then sometimes my
dad and I will follow some I don't know we took a art class I took a kids art class and we'll go to the mountains neighboring mountains to draw >> but my entire childhood memory of my dad is is just a very unserious parent who had no interest in my grades or what I'm doing in class. Did I achieve anything? Did I bring back any competition awards? Nothing to do with that. Even when I came to New Jersey with my parents, life became extremely tough, right? It was immigrant life. We were in a lot of
poverty. And even that my memory is that he had so much fun in yard sales. Like I would just go to yard sales and and those are our every weekend it was just yay let's go to yard sales and just use that as a treasure hunt almost. So so it's just he's a very curious and childlike mind in that way. >> I'm asking about your parents in part because I know you're a parent and ultimately I'm going to want to ask how you think about parenting. that will come up at some point. But since listeners
will certainly be asking themselves this question and we're not going to get into any geopolitics because there are plenty of people who want to get into that and fight over that which we're not going to do. But why did your parents leave China? like what was the catalyst or what were the reasons behind leaving what you knew or leaving what they knew and coming to a very different foreign country where I you're going from Chungdu which is a city to suburban New Jersey which is as I think you've described it felt very empty right and
then you have the language barriers and the financial barriers there's so many things why the move >> I'll give you two answers in the early teenage fay would say I I have no idea >> because my dad left when I was 12 and my mom and I joined him when I was 15 and those years you're you're a teenager, right? Like there's so many strange things in your head and all I knew is that you know they said let's go to America and I had no idea. I really did not know what happened. There was
this vague sense of there's opportunities and freedom. there's this that education is very different >> and I had a hunch that I was not a typical kid >> in the sense that you know I was a girl and >> I loved physics I loved fighter jets of all things you know I can tell you all the fighter jets I love from F-17 to F16 to you know to all the different things that I I loved. So that's all I knew. In hindsight, as a grownup fa >> I appreciated my parents. They're very brave people because
I don't know this age myself would just pick up and and leave a country I'm familiar with and go to I don't know a completely different country that I speak zero language and I have zero connectivity to >> and mind you that's pre- internet pre- AI age so when you are >> going to a different country you're comp you're just you might as well go to a different planet >> you're cut of >> Yeah. >> Yeah. >> I think they're very brave. The grown-up Fay realized that they wanted me to have an opportunity that that
they think will be unprecedented for for my education. >> Mhm. >> And it turned out that's that's kind of true. >> Yeah. Well, certainly looking at your bio, I mean, it's mind-boggling to imagine all the different sliding door events and different paths you could have taken. So, we're going to hop pretty closely along chronologically, but we're going to ultimately get to a lot of the meat and potatoes of the conversation, but I want to touch on maybe some other formative figures. And I would like to hear about your mother as well, because just with the
context of your dad, it's like, okay, that seems fascinating and very unusual, particularly if you've spent any time in China, especially during that period of time. >> He is very unusual that way. Yeah, very unusual. So then people might wonder, well, where does the drive come from? Where does the technical focus come from? And I'd love to hear your answer to that and also hear you explain who Bob Sabella was, if I'm pronouncing that correctly. >> Yes. There are two questions mostly, you know, is my mom the one who put in the drive and the
technical passion and and what role did Bob play in my life? So first of all, my mom has zero technical jeans here. I sometimes still laugh at her. She cannot do math. Let's put it this way. So I think the the technical passion is just I was born with it >> inate. >> My dad is more technical, but he loves bugs more than insects, more than equations for sure. So I think that's you know as an educator for so many decades now myself and also as a parent you have to respect the wonders of nature
there is this inner love and fire and and passion and curiosity that comes with the with the package right so >> but my mom is much more disciplined person she she's still not a tiger mom in the sense I don't remember my mom ever going after me on grades or she really did not. My both my parents never ever cared about me bringing any awards home. >> Mhm. >> Maybe I did, maybe I didn't. But I can tell you in our house there's zero wall hangings of anything which actually carried to today. Even for myself,
my own house, my own office have zero of those decorations of achievements or awards. It's just uh my mom did not care about that. But she did care about me being a focused person. If I want to do something, she doesn't want me to play while doing homework. And that kind of thing would bother her. She would say, "Just finish your homework. Say by 6:00 p.m. If you don't finish your homework, you're not allowed to do more homework. You have to deal with the consequences." So she she instilled some discipline, but that's about it. She's
tougher than my dad. She is very rebellious. She had a unfinished dream herself. She was very academic when she was a a kid herself and cultural revolution really crushed all her dreams. >> She became a more rebellious person in that sense that I think I did observe and and experience as a daughter. >> Maybe part of immigration is even part of that. Many years later, she would say, "I had no plan coming to New Jersey, but I think I'm going to survive. I just believe I'm going to survive and I'm going to make sure Fay
survives." >> I think that is her strength, her stubbornness, and her rebelliousness. >> When does Bob enter the picture and who is Bob? >> Bob Sabella was a high school math teacher in Pipony High School. He he was my own math teacher as well as many many students. He entered my life in my second year in per so it's kind of bordering sophomore to junior year in Persip high school when I started taking AP calculus but he quickly became the most influential person in my formative years as a new American kid immigrant as a teenager
because he became my mentor my friend and eventually his entire family became my American family >> and he became my friend when I was a very lonely ESL English as second language student. >> I was excelling in math but I think it's more because I was lonely and he was very friendly. He treated me more like a friend who talks about books we love, talk about the culture, talks about science fiction, >> and also listened to me as a very, you know, I wouldn't say confused, but teenager undergoing a lot of life's turmoil in in
my unique circumstance. And that unconditional support made me very close to him and his family. And one thing he did to me that I did not appreciate till later is that when Pimity High School couldn't offer a full calculus BC class because it just didn't have that, he just sacrificed his lunch hour, his only lunch hour to teach me calculus BC. So it was a one-to-one class. And I'm sure that contributed to me a immigrant kid getting into Princeton eventually. But later as I became teacher myself, it's exhausting to teach all day long. And the
fact that on top of that, he would use his lunch hours to do that extra class for me is just such a gift that I now appreciate more than I was as a as a teenager. >> Yeah. Thank God for the teachers who go the extra mile. It's just incredible, especially when you >> get a bit older and you have more context and you can look back and realize. >> I really think these public teachers in America are the unsung heroes of our society because they are dealing with kids of all backgrounds. They're dealing with
the changing times. the kind of stories Bob would share with me in terms of how he went extra miles not just with me but with many students in because Puberty is is a heavily immigrant town. >> Mhm. >> So his students are from all over the world and how he helped them and their family. It's just those are the stories that people don't write about and that's part of the reason I wrote the book was to celebrate a teacher like that. >> Yeah. I have so much I want to cover and I know we're going
to run out of time before we run out of topics. So, I want to spend more time on Bob and at the same time I want to keep the conversation moving. So, we're we're going to do that and I'll just perhaps hit on a few things and then dig into a number of questions. But certainly at Princeton you but also your entire family had to survive. So, you were involved with operating a dry cleaning shop in New Jersey as one option, right? you ran that for 7 years. So through that, it feels like you've gained
perspective on many different levels that have then helped inform what you've done professionally, right? So you you learn to think about not just people who are protected in an ivory tower, but people all the way down in across in society. So from every swath of society your mother also although she was not technical she imbued in you this discipline and also seems to have had a very broad appreciation and knowledge of literature and international literature. So now you have this global perspective presumably at the time in Chinese and then you end up you end up
at Princeton and I know we're going to be hopping around quite a bit but I'm curious to know how Imagenet came about and you can introduce this any way you like. You can tell people what it is and what it became and why it's important and then talk about how it started or you can just talk about how it started. But it's it's such a an important chapter. >> So let me just explain what ImageNet is. Imagenet on the surface was built between 2007 and 2009 when I was an assistant professor at Princeton and then
I moved to Stanford. So during this transitional time my student and I built this at that time the field of AI's largest training and benchmarking data set for computer vision or visual intelligence. The significance today after almost 20 years of imageet was it was the inflection point of big data. Before imageet AI as a field was not working on big data and because of that and couple of other reasons which I'll get into AI was stagnating. The public thinks that was the AI winter. Even though as a researcher, young researcher at that time, it was
the most exciting field for me, but I get it. It wasn't showing breakthroughs that the public needs. >> But imaget together with two other modern computing ingredients. One is called neuronet network algorithm. The other one is modern chips called GPU graphic processing unit. These three things converged in a seinal work, milestone work in 2012 called image net classification deep convolutional neuronet network approach. That was a paper that a group of scientists did to show that the combination of large data by imageet, fast parallel computing by GPUs and a neuronet network algorithm could achieve AI performances
in the field of image recognition in a way that's historically unprecedented. And that particular milestone is many people call it the birth of modern AI. and my work image that was onethird of that if you count the elements and I think that was the significance I feel really very lucky and privileged that my own work was pivotal in bringing modern AI to life >> but the journey to image that was longer than that the journey to image that started in Princeton when I was an undergrad you were in the East Asian study department I was
hiding in Jadwin Hall which is our physics department. >> Yeah, >> I loved physics since I was a young kid. I I don't know how somehow my dad's love of bugs and insects and nature translated in my head into just the curiosity for for the universe. So, I loved, you know, looking to the stars. I loved the speed of fighter jets and the intricate engineering of that eventually translated into the love of the discipline that that asks the most audacious question of our civilization such as what is the smallest matter? What is the definition of
spacetime? How big is the universe? What is the beginning of the universe? And in that in that early teenagehood love I loved Einstein. I love his work. >> I wanted to go to Princeton for that. But it turned out what physics taught me was not just the math and and physics. It was really this passion to ask audacious question. So by the end of my undergrad years, I wanted my own audacious question. You know, I wasn't satisfied with just pursuing somebody else's audacious question. And through reading books and all that I realized my passion was
not the physical matters. It was more about intelligence >> I was really really enamored by the question is of what is intelligence and how do we make intelligent machines. So at that time I swear I did not know it was called AI. I just knew that I wanted to pursue the the study of intelligence and intelligent machines. And then I applied to grad school and I went to Caltech. Caltech was my PhD. I started in the turn of the century 2000. And I think I consider that moment I became a budding AI scientist. You know
that was my formal training as a computer scientist in AI. Then my physics training continued in a sense that physics taught me to ask audacious question and turn them into a northstar. >> Mhm. >> And in scientific terms that northstar became a hypothesis. >> Mh. >> And it was very important for me to define my northstar. And my first northstar for the following years to come was solving the problem of visual intelligence is how how we can make machines see the world. And it's not just by seeing the RGB colors or the shades of light
is about making sense of what's seen which is you know I'm looking at you Tim. I see you. I see a beautiful painting behind you. I don't know it was real. I see you're sitting on a chair like that is seeing. Seeing is making sense of what this world is. So that became my northstar question. And that hypothesis that I had is I have to solve object recognition. >> And then that was in my entire PhD was the battle with object recognition. There were many many mathematical models we have done and there were many questions
but me and my field was struggling. We can write papers no problem but we did not have a breakthrough and then luckily for me Princeton called me back as a faculty in 2007. It was one of my happiest moment of my life. I feel so validated my alma m would consider giving me a faculty job. So I happily moved back to Princeton as a faculty this time and I continue to be a Forbes member actually. So at Princeton there was an epiphany is that I realized there was a hypothesis that everybody missed and that hypothesis
was big data. Could I pause you there for a second because this is the this is the point >> that I'm so so curious about and I just want to pause for a second also for people who are interested in some of the history of Princeton. It's pretty crazy. They should look up the history of the Princeton Institute for Advanced Study and I remember taking some of those East Asian Studies classes that you referred to in classrooms where Einstein taught and it's just the aura, the veneer. You want to believe that you can feel it
just permeating the uh the entire campus and it's fun in that respect. It's very fun. But I'm going to read something from a Wired piece that discussed you at length and as you mentioned big data before and after in terms of its integration into the type of research they were describing as it was written and please feel free to fact check this or push back on it but in wire they said the problem was a researcher might write one algorithm to identify dogs and another to identify cats and then you it says you know Lee
began to wonder if the problem wasn't the model but the data she thought that if a child learns to see by experiencing the visual world by observing countless objects and scenes in her early years, maybe a computer can learn in a similar way. I want you to expand on that for sure. And the question for me is like why did you see it, right? Why didn't it happen sooner? >> We're all students of history. One thing I actually don't like about the telling of scientific history is there is too much focus on single genius. >>
Yes, agreed. We know Newton discovered the modern laws of physics but yes he is a genius not to take away any of that from Newton but but science is a lineage and science is actually a nonlinear lineage for example why was I inspired by this hypothesis of big data because many other scientists inspire me in my book I talked about this particular lineage of work by professor Beerman who was a psychologist who was he was not interested AI, but he was interested in understanding minds. And I was reading his paper and he particularly was talking
about the massive number of visual objects that young children was able to learn in early ages. Right? So that piece of work itself is not image that. But without reading that piece of work, I would not have formulated my hypothesis. So while I'm proud of what I have done, my book especially wanted to tell the history of AI in a way that so many unsung heroes, so many generations of scientists, so many crossdisiplinary ideas pollinate each other. So I was lucky at that time as someone who is passionate about the problem but also someone who
benefited from all these research. So yes something happened in my brain but I would really attribute to many things happened across so many people's work throughout their lifetime devotion to science that we got to the point of imageet. I'm so glad that you're underscoring this because if you really dig as a I don't consider myself a scientist, but I I love reading about the history of science. There's so many inputs, so many influences, so many interdependencies. >> Yes. >> And the simplicity of the single hero's journey is appealing and its simplicity, but it's almost never
true. >> It probably is never true. Even my biggest hero, Einstein, right? He anybody who knows me, anybody who read my book knows how much I rever him and I just love everything he's done. The special relativity equation is a continuation of Lawrence transform. Even Einstein, he builds upon so many other people's work. So I think it's really important especially I'm sure we'll talk about it. I'm here calling you in the middle of Silicon Valley and we're in the middle of an AI hype and obviously I'm very proud of my field but I think that
when the media or whatever tells the story of AI it almost always just talk about a few geniuses and it's just not true. It's generations of computer scientists, cognitive scientists and engineers who who made this field happen >> for sure. I mean, everyone knows Watson and Crick for for instance, but without Rosalyn Franklin and her X-ray crystalallography, it doesn't happen. Doesn't happen. It just doesn't happen. Point blank. We're going to hop to modern day in a second, but with ImageNet, I would love for you to speak to some of the decisions or let's say decisions
or moments that were just formative in making that successful, right? Because for instance, if you're going to try to allow a machine to, and I'm using very simple terms cuz I'm not technical enough to do otherwise, to learn to identify objects closer to the path that a child would take, you have to label a lot of images, right? And I was reading about how Mechanical Turk came into play and then there's a competitive aspect that seems to have driven some of the watershed moments. Could you just speak to some of the elements or decisions that
made it successful? >> A lot of people ask me this question because after image that many many people have attempted to make data sets but still only very few are successful. So what made image less successful? I think one of the success was timing is that we truly were the first people who see the impact of big data. So that very categorical or qualitative change itself is part of the success but it's also as you were asking the hypothesis of big data is not just size. A lot of people actually misunderstands image nets significance as
well as other data sets significance coming with the data set is a scientific hypothesis of what is the question to ask. For example, in visual recognition you could talk about you could make a data set of discerning RGB and that would not be as impactful of a data set that is organized around objects. Mhm. >> We can go down a rabbit hole of why not because RGB is easier per se. It's because you have to ask the scientific question in the right way. Another example is instead of making a data set of objects, why don't
you make a data set of cities, >> you know, that's even more complicated that objects. But then that's dialing too complicated. So every scientific quest, you have to have the right hypothesis and and asking the right question. So that's one part of the success is we defined visual object categorization as the right hypothesis. >> That was one rightness I guess. Another rightness is that people just think oh it's easy you just collect a lot of data. Well first of all it's laborious. But even aside from being laborious how do you define the quality? >> Mhm.
You could say well if quality is big enough we don't care about quality. But how do you dial between what is big, what is good and how do you trade off that is a deeply scientific question that we have to do a lot of research on. And then another decision that is a set of decision that is really hard is what defines quality in terms of image. Is it every image has higher resolution? Is it it's photorealistic? Is it because it's everyday image that look very cluttered? Is it all product shots that look clean? These
are questions that if you're too far away, you wouldn't even think about asking. But as a scientist, as we were formulating the deep question of object recognition, we have to ask this in so many dimensions. And then you mentioned Amazon Mechanical Turk. That is actually a consequence of desperation because when we formulated these this hypothesis, our conclusion is we need at least tens of millions of high quality images across every possible diverse dimension. Whether it's user photos or is it product shots or is it stock photography like and then we need also high quality labels.
Once we make that decision we realize this has to be human filtered from billions of images. >> So with that we became very desperate. We're like how are we going to do that? You know, I did try to hire Princeton undergrads and as you know, Princeton undergrads are very smart. But >> they have very high opinion of the value of their time. >> Yes. And they're expensive. But even if I had all the money in the world, which we didn't, it would have taken so long. So, we were very very stuck for very, very long.
We thought we had other shortcuts, but the truth is human labeling is a gold standard. M >> we want to train machines that are measured against human capability. So we cannot shortcut that at that time. >> Right? >> So we had to go to what we eventually found out is called crowd engineering >> crowdsourcing and that was a very new technology was barely a year old or so by Amazon. They they created a lot online marketplace for people to do small tasks to earn money. when these tasks can be uploaded on the internet. I remembered
when I heard about Amazon Mechanical Turk, I logged into my Amazon account. I checked the first task I checked out to do just to try was labeling wine bottles or transcribing wine bottle labels. The task will give you a picture of a wine bottle and you have to say this is 1999 Berdo and and all that. Yeah, >> people upload these kind of micro tasks and then online workers like someone in their leisure time like me if I had leisure time I would just go sign up and get paid to do that. And we realized
that was again out of desperation that was a massive parallel processing with online global population to do this for us and that's how we labeled billions of images and distilled it down to 15 million high quality image that images. >> It's just so wild when you look at these stories. is I just finished a book on Janentech and there were all these little technical inflection points that also allowed things to happen right so if it had been 5 years earlier or maybe 3 years earlier right without mechanical turk boy like it presents a challenge >>
y >> but also as you pointed out in science it's one thing to get answers but you need the input on the front end with a proper hypothesis or a good question and even with mechanical turk if you're only focused on the the mechanics of employing that, you can get yourself into trouble. Because if humans are incentivized, right, to let's just say, I think this was the example I read about, identify pandas in photographs and they're paid for identifying pandas, well, what's to stop them from identifying a panda in every photo, whether they exist in
the photos or not? Yes. >> Right. So, you have to follow the incentives as well. How did you solve for that? >> This is where you know my student and I had I cannot tell you how many hours and hours of conversation we have about controlling the quality. We have to solve for that in multiple steps. We need to first filter out online workers who are serious about doing the work. So for example, we have to have some upfront quizzes >> so that they understand what a panda is. They read the question and then once
they get into they qualify for that we ask them to label pandas but there are some images we know the correct answers some are true pandas some are some are not true pandas >> but the labelers don't know so in a way we implicitly monitor the quality of the work by knowing where the gold standard answers are >> so these are the kind of computational tactics we have to use to ensure the quality of labeling. >> Amazing. Just incredible. I'll actually just put a recommendation out there for a book, Pattern Breakers, by a friend of
mine, Mike Maples Jr. He taught me the ropes initially of angel investing. But in terms of identifying inflection points and in some cases converging technological trends that for the first time make something possible which then opens an opportunity right for something with the right prepared mind in your case and those of your collaborators and the people you built upon for something like imagageet pattern breakers is a really good read for folks. So let's let's hop to modern day then for a moment and I would love to ask you right because you've been called the godmother
of AI in our alumni magazine in fact and elsewhere but you've had such a not just technical but historical viewpoint meaning you've over a broad timeline well broad by AI standards been able to watch the development and forking and perils and promise of this technology. What are people missing? What do you think is eating up all the oxygen in the room? What are people missing? Whether it's things they should know or things they should be skeptical of or otherwise >> especially I'm here calling you from the heart of Silicon Valley and I think people are
missing the importance of people in AI >> and there's multiple facads or dimensions to to this statement is that AI is absolutely a civilizational technology. It's I define civilizational technology in the sense that because of the power of this technology it'll have or already having a profound impact in the economic, social, cultural, political downstream effects of our society. So >> I just heard this is unverified but I just heard that 50% of the US GDP growth last year is attributed to AI growth. >> Apparently this number is 4% for US GDP have grown 4%. If
you take away AI it's only 2%. That's what means >> that's civilizational from an economic point of view. It's obviously redefining our culture, right? Think about you're talking about the word sucking oxygen out of the room everywhere from Hollywood to Wall Street to Silicon Valley to political campaign to Tik Tok to YouTube to Insta. >> Taxis in Japan. I was just there and the videos playing on the back of the headset and the taxi. We're all talking about AI. It's everywhere. >> It's culturally impactful. Not only impactful, it's shifting our culture and it's going to
shift education. Every parent today is wondering what what should their kids study to have a better future. Every grandparent is say, "I'm so glad I'm born early. I don't have to deal with AI." but still worry about their grandchildren's future. So AI is a civilizational technology, but what I think it's missing right now is that Silicon Valley is very eager to talk about tech and the growth that comes with the tech. Politicians are just eager to talk about whatever gets the vote, I guess. But really, at the end of the day, people at the heart
of everything. People made AI, people will be using AI, people will be impacted by AI, and people should have a say in AI. And no matter how AI advances, people's selfdignity as individuals, as community, as society should not be taken away. And that's what I worry about because I think I think there's so much more anxiety that because the sense of dignity and sense of agency, sense of being part of the future is slipping in some people and I think we need to change that. I've heard you say that you're an optimist because you're a
mother. And both optimism and pessimism to an extreme can bias us in ways that are unhelpful, right? Or create blind spots. And I'm curious if you try to put your most objective hat on, which is difficult for any human, but if you try to do that, do you think people are too worried, not worried enough, or worrying about the wrong things? for people who are not CEOs and builders and engineers behind AI because you're right of course I mean everybody will agree with this that a lot of people are very worried and I'm just wondering
if it's if it's ill-placed because I don't really if you talk to some of the VCs who are the biggest investors of course they have this sort of in my view beyond all possibilities techno optimist view of the future where AI solves everything right and it's hard to believe there's a free lunch And then you have the the doomers, the doom and gloom where suddenly it's Skynet next year and we're all slaves to robots or eliminated, turned into paper clips and reality is probably in between those two. Do you think people are worrying about the
right things or have they lost the plot in some way? >> First of all, I call myself a pragmatic optimist. I'm not a utopian. So I'm actually the boring kind. I don't believe in the extreme on both sides. I travel around the world. Just last month I was in Middle East, I was in Europe, I was in UK and I I was in Canada, I came back home in America. I think people in America and people in Western Europe are more worried about AI than say people in Middle East, in Asia. And I think we
don't have to litigate why they're more worried >> but just to come closer to home just in talk about us. I wish I have a megaphone to tell people in the US that you're known to be one of the most innovative people our country have innovated so many great things for humanity for civilization. We have a society that is free and vibrant and we have a political system that we still have so much say in how we want to build our country. I do wish that our country has more optimism and positivity towards the future
of using AI than what is being heard now. I think people like me technologists living in Silicon Valley has a lot of responsibility in the right kind of public communication. So there's a lot of things that was not communicated in the effective way. But I do hope that we can instill more sense of hope and self agency into everybody in our country because I think there's so much upside of using AI in the right way. And I want not just people in Silicon Valley or in Manhattan, but I want people in rural communities in traditional
industries in everywhere 50 states to be able to embrace and and benefit from AI. >> Why are you building what you're building? What is World Labs? Why decide to do this? >> I actually answer this question very often to every member of my team. Mhm. >> I built World Labs. There are two levels of this answer. From a technology point of view, World Labs is building the next generation AI focusing on spatial intelligence >> because spatial intelligence just like language intelligence is fundamental in unlocking incredible capabilities in machines so that it can help humans to
create better, to manufacture better, to design better, to build better robots. So spatial intelligence is a lynchpin technology. Mhm. >> But one level up, why am I still a technologist? Is because I believe humanity is the only species that builds civilizations. Animals builds colonies or herds, but we build civilizations. And we build civilizations because we want to be better and better. We want to do good. Even though along the way we do a lot of bad things but there is a desire of having better lives, having better community, having better society, live more healthily, have
more prosperity. >> That desire is where civilization is built upon. And because I believe that humanity can do that, I believe science and technology is the most powerful tool, one of the most powerful tools in building civilizations and I want to contribute to that. That's why I'm still a scientist and a technologist and I'm building world labs for that. Can you explain to people what spatial intelligence is and what the product is so to speak at least as it stands right now that you're building? >> Spatial intelligence is a capability that humans have which goes
beyond language is when you pack a sandwich in a bag when you take a run or a hike in a mountain. When you paint your your bedroom, everything that has to do with seeing and turning that scene into understanding of the 3D world, understanding of the environment and then in turn you can interact with it, you can change it, you can enjoy it, you can make things out of it. That whole loop between seeing and doing is supported by the capability of spatial intelligence. Right? The fact that you can pack a sandwich means you know
what the bread looks like. You know how to put the knife in between. You know how to put the lettuce leaf on the bread. You know how to like put the bread or sandwich into a Ziploc bag. Every part of this is spatial intelligence. M and does today's AI have that? It's getting better, but compared to language intelligence, AI is still very early in that ability to see, to reason, >> and also to do in world in both virtual 3D world as well as real 3D world. So, so that's what world labs is doing. We
are creating a frontier model that can have intelligent capability in the model to create world to reason around the world and to enable for example creators or designers or or robots to interact with the world. So that's spatial intelligence. >> Could you expand on the you know designers or creatives or robots interacting with the world? So does that mean that you could and my team has been playing with with some of the tools. So thank you for that. What does that mean? If you could paint a picture for let's say a year from now, two
years from now, how might someone use this or how might a robot use this? >> I was just talking to someone a couple of weeks ago and it was really inspiring is that high school theaters are very low budget, right? like, okay, sometimes I go to San Francisco opera or musicals and the sets that's built for theater are just so beautiful. >> Mhm. >> But it's very hard for high school or middle school to have that budget to do that. Imagine >> that you can take today's worldlapse model, we call it marble. >> Mhm. >>
And then you create a set in medieval French town. >> Mhm. And then you put that in the background and use that digital form to help transport the actors and action into that world. And of course, depending on the auxiliary technology, whether you're on a computer or eventually people can use a headset or whatever, you can have that immersive feeling of being in a medieval French town. That would be an amazing creative tool for a lot of creators. >> That was the example. Someone and I was talking about it a couple of weeks ago. But
we already see creators all over the world. Some of them are VFX creators. Some of them are interior design creators. Some of them are gaming creators. Some of them are educators who want to build some worlds that transport their students into different experiences are already starting to use our model >> because they find it very powerful at their fingertip to be able to create 3D worlds that they can use to to immerse either their characters or themselves into. And just a process-wise, if if someone's wondering how this works, let's just say it's a a public
school teacher, let's just say, who's hoping to inspire and teach their students going the extra mile. What does it look like for someone to use this? Are they typing in text, describing the world they'd like to create, uploading assets or photos, almost like an image board? How does it how does it work? If someone's nontechnical, >> they don't need to be technical at all. They open our page on desktop or in their phone, but desktop is more fun because it has more features. >> And then they can type, you know, a French medieval town or
or they can actually go to anywhere. They can use midjourney or nano banana to create a photo of a French medieval town or they can get an actual photo about that and then they upload it. We call it prompt. And then after a few minutes, our model gives you a 3D world that is say a part of the tab. It does have a limit in its range. And then that 3D world is generally 3D because you can just use the mouse to drag and turn around and walk around and see that world. And then downstream
if you want to use it, you have many ways to use it. You can actually create a movie out of it by using one of our tools on the website to just put cameras and you can make a particular movie out of it. >> You could if you're a game developer. >> I was just going to say it sounds a lot like a gaming engine. >> Yes, you you can put a lot of characters in it. If you're VFX professional, we have a lot of VFX professional. they can actually take this and put it in
the workflow of their movie shooting and have real actors shooting movies. We've also have psychology researchers using that immersive world in particular psychiatric studies. >> We could also use that as the simulation for robotic training >> because a lot of robotic training needs a lot of data and then use that for generating a lot of different data. So is it almost like a flight simulator for robots before they go into the real world? >> That's part of the goal. We are still early. So the flight simulator is not complete yet, >> right? >> But that's
part of the journey. >> You mentioned psychiatric studies. I I think that's what you just mentioned. Yes. What might that look like? We actually got this researcher who called us and they're studying people who have psychological disorders like obsessivecompulsive disorder >> where they're triggered by certain environments and they want to study the trigger and also just study how the treatment but how do you trigger someone who let's say is particularly have issue with let's say a strawberry field I'm is making it up. >> I mean, you can take them to a strawberry field, but what
about you want to know if it's strawberry field in the summer or strawberry field at night or it's strawberry or it's mating strawberry like how do you do this? Suddenly this researcher realized we give them the cheapest possible way of varying all kinds of dimensions and they can test this out and do their studies. >> That's really interesting. Yeah, I could see it being applied to it might be called exposure therapy, but now that you're describing it, I could see how it could be >> added into I mean pretty much everything, right? I mean, if
you think about how humans operate in the real world. >> Yes. And the boundary between real world and digital world is less and less, right? Thinner and thinner because we live in many screens. We live in the real world. We do things in virtual world. We do things in real world. will create machines that can do things in real world and virtual world. >> So there's a lot we do in digital and physical spaces. >> Who are some scientists or researchers who you pay attention to who are not necessarily kind of the big brand names
and marquee lights that are already very public in the world? Is there anybody who stands out where you're like, you know, there's some really tremendous people doing good work? Well, that's part of the reason I wrote the book is especially in the middle chapters where I wrote about the journey of doing image that combines cognitive science with computer science and I actually talk about psychologists and neuroscientists and developmental psychologists in you know some of them are still with us some of them are not for example the the late anman beerman they all passed away in
the last few years But they were giants in cognitive science whose work has informed computer science and eventually AI. You know there are still lots of scientists around the world. Many of them are in the US who are thinkers in developmental psychology in AI. I follow their work. Mhm. >> I think the world of science, just to name some names, right, Liz Beli in >> in Harvard, Allison Gobnik in Berkeley, I love Rodney Brookke, who was a former MIT professor in robotics, >> and there's just a lot of them. I I don't mean to just
single them out. >> Sure. >> But you're asking me for names that are not in in the news of AI. >> Yeah, that's perfect. Thank you. I would also love to get your perspective on what might be this is a very strong word but seemingly inevitable in in terms of developments in the near intermediate future. And I'll give you an example of what I mean. In 2009 2008 2009 I became involved with Shopify the company back when they had like 10 employees. And there were a few things happening around that time and you could ask
questions, you know, in the next 10 years or 20 years, will there be more broadband access or less? More. Okay. Will there be more e-commerce or less? There'll be more. Okay. And when you have four or five of those that seem over a long enough time horizon, absolutely yeses, it begins to paint a picture of where things are going. Are there any things that in the next handful of years you think are perhaps underappreciated as near inevitabilities? >> You want me to talk about underappreciated? I mean, I don't know if they're overappreciated, but definitely appreciated.
The need for for power is appreciated. >> Mhm. >> The trend of more AI, not less AI is appreciated. >> The long-term trend of robots coming is appreciated. So, these are appreciated. What's underappreciated is spatial intelligence is underappreciated in the sense that everybody's still now talking about language large language models but really world modeling of pixels of 3D worlds is underappreciated because like you were saying it powers so many things from storytelling to entertainment to to experiences to robotic simulation. I think AI and education is underappreciated because what we are going to see is that
AI can accelerate the learning for those who want to learn >> which will have downstream implication in our school system >> as well as in just human capital landscape like how do we assess qualified workers? >> Mhm. used to be which school you graduate from with with which degree but that will be changing. Yeah. With AI being at the fingertip of so many people that's underappreciated. I think AI's impact in our economic structure including labor market is underappreciated. The nuance is underappreciated. I think this whole rhetoric of either total utopia post scarcity is hyperbolic. >>
Yeah. >> Or like everybody's job will be gone is hyperbolic. >> But the messy middle is how from knowledge worker to blue collar to hospitality to all these changes that's happening. It's underappreciated by our policy workers, by our scholars, by just overall society. >> Well, what are some of the nuances from the job perspective? Maybe this ties into what I promised earlier I was going to ask you, which is what you are telling or will tell I don't know other ages your children are recommending. Let's just say I don't know how old they are, but
if we assume that they just for the sake of discussion of the age where they're trying to decide what they should study, where they should focus, things of that nature, how how would you think about answering that even provisionally? >> I think the ability to learn is even more important because when there was less tools, fewer tools to learn, it's easier to just follow tracks. You go through elementary school, middle school, high school, college and then get some, you know, get some training vocationally and that's kind of a path and with that is a set
of structured credentials from degrees and all that but AI has really changed it. For example, my my startup when we interview a software engineer honestly how much I personally feel the degree they have matters less to us now is more about what have you learned what tools do you use how quickly can you superpower yourself in using these tools and a lot of these are AI tools what's your mindset towards using these tools matter more to Mhm. >> At this point in 2025, hiring at World Labs, I would not hire any software engineer who does
not embrace AI collaborative software tools. >> Mhm. >> It's not because I believe AI software tools are perfect. is because I believe that shows first of all the ability of the person to grow with the fast growing toolkits the open-mindedness and also the end result is if you're able to use these tools you're able to learn you can superpower yourself better >> so that is definitely shifting so coming back to your question what do you tell young people tell children I think the timeless value of learning to learn, the ability to learn is even more
important now. >> Yeah, it it strikes me as we're talking that it's only going to get increasingly easier for the ambitious to act as superpowered autodidacts, right? We've already seen this >> with certainly YouTube has a nice track record. Now you can either entertain yourself to death and avoid doing things that help with self-rowth and development or you can supercharge it. And similarly with AI, right, you flash forward. We don't even need to flash forward, but it's how does a teacher audit that their students are doing the work they're supposed to be doing. >> Yeah.
>> On so many levels, it's getting to the point. There are some exceptions, but of near impossibility. >> Yeah. and students can either avoid all work or they can supercharge their own work but the output might look very similar at least for a period of time. So schooling is going to change a lot. It's very very interesting. >> I actually think Tim if the school evaluation is structured in a way that whatever AI gives and whatever the student gives is the same there's something wrong with the structure of the evaluation. >> Okay. Can you say
more about that? That's interesting. >> For example, English essay. >> This is not me. This is me hearing a story that I so agree with. I'll retell the story. Is that as a high school freshman English class teacher? I heard that someone told me the story of their kids school. On the first day of school, the teacher actually said to the class, I want to show you how I would score AI. So the teacher give an essay topic. Show the students this is what the best AI gave me and I'm going to show you how
I think this is good, this is bad, how this is suboptimal and I'll give it a B minus. Now I will tell you this is my bar. If you're so lazy that you ask AI to write your essay, this is what you're going to get. But you can use AI, that's totally fine. But if you can do the work, learn, think, be the best human creator you can and work on top of that, >> you can get to a you can get to A+es. And that would be in my opinion the right way to structure
the evaluation is not to pit humans against the AI and then try to police the use or not use of AI. is that to show where the tools the bar of the tools are and where the bar of the human learner should be. >> I'm going to sit with that example and try to think of more examples. It's very interesting and boy oh boy I've been shocked by how quickly the models improve. But yes, that's like as a thought experiment. >> Yeah, >> I'm going to chew on that. I know we only have a few
minutes left. Fifth, I wanted to ask you a question I ask a lot, which is if you could put a quote or a message, something on a billboard, something to get in front of millions, billions of people. Just assume they all understand it. Could be an image, could be a question, could be a quote, anything at all. A saying, a mantra, doesn't matter. Could be almost anything. What would you or what might you put on that billboard? >> What is your northstar? >> What is your northstar? This is of course critically important and coming back
to how you define that or find that for yourself. I mean you were talking about audacious questions and then that leading to a north star or hypothesis. Is there another way that you would encourage people on top of that to think about finding their north star? I believe that's how that makes us so human and makes us to be so fully alive >> is that we as as a species can live beyond the chasing of just basic needs right but dreams and missions and goals and passion and everybody's northstar is different >> and that's fine
not everybody has to have AI as their northstar but finding That goes to the heart of education again and I don't mean formal classroom education. It's just the journey of education. A lot of that is the ability to learn who you are and to learn how to formulate your northstar and how to chase after that. >> Last question. Did your parents ever explain to you why they named you Fay? >> Yes. is because when my mom was going through labor, my dad was characteristically late to the hospital and along the way he caught a bird.
He let it go, but he did catch a bird. I don't know, he was just distracted and it was in Beijing in the city of Beijing. My dad was bicycling to my mom's hospital >> that inspired him to call me Fay. >> Feet. >> Oh, wait. Sorry for those who don't speak Chinese. I forgot you do speak Chinese, but for those who don't speak Chinese, Fay means flying. >> Means flying. >> Yeah. So, be inspired by a bird. >> Really quick, I'll just say it's kind of funny. My first Chinese name that I had was
Fay Ting Chong, which is because I was very blunt and honest. So, Ting, but Fing Chong, but when I was first starting, my tones in China were not polished and people thought I was saying that my name was Fiji Chang, which is airport. So, I petitioned my teachers and we changed my name to something less less confusing. >> What's your new name? >> Oh, okay. >> It's like but it's without the at the bottom. >> Oh, wow. >> Fancy name. That's way more sophisticated than my >> Well, I get to script it with my Chinese
teachers, so I have an unfair advantage. Dr. Lee, thank you so much for the time. We will link to the show notes for everybody at tim.blog/mpodcast. They'll be able to find you easily and everybody should check out worldlabs.ai and we'll put every other link, your social and so on in the show links. But thank you for the time. I really appreciate it. >> Thank you, Tim. I enjoyed our conversation. >> Yeah, likewise. >> Okay, bye >> bye.
