#839: Dr. Fei-Fei Li, The Godmother of AI — Asking Audacious Questions, Civilizational Technology, and Finding Your North Star

Transcript from https://podmenti.com/t/523e752086466f89

Hello, boys and girls, ladies and germs. This is Tim Ferris. Welcome to another episode of the Tim Ferris Show, where it's my job to deconstruct world class performers. I interview them to tease out the habits, routines, frameworks, et cetera, that you can apply to your own lives.
My guest today is Dr. Fei Fei Lee. She is the inaugural Sequoia Professor in the Computer Science Department at Stanford University. She's been called the Godmother of AI. She's a founding co-director of Stanford's Human Centered AI Institute.
And the co-founder and CEO of World Labs, a generative AI company focusing on spatial. She's also the author of The World's I See: Curiosity, Exploration, and Discovery at the Dawn of AI. Her memoir.
Her story is incredible. It is one of beating the odds.
On so many different levels.
And uh let's get straight to it.
doctor Favely.
Optimal minimal.
At this altitude, I can run flat out for a half mile before my hands start shaking. Can I ask you a personal question?
No, it is kinda.
I'm a cybernetic organism, living tissue over metal endoskeleton.
Me
So
Doctor 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 that 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. I was Forbes too. Okay. This is for people who don't know what the hell we're talking about.
There are these uh 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 a fast food spot like seven eleven called Wawa.
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 slash sororities 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 are only in the libraries.
Whatever it was, six dollars 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. Hilarious. Okay. Did you change name or something? We did meet. I did didn't change my name.
But here we are. 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 and Beijing.
and went to the what was it Capital University of Business Economics after that. So I was gone for a good period of time and then took a year off.
before graduating with the class of two thousand.
Still, we had a lot of overlap, 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
Their parents were pretty
Atypical for Chinese parents, in my experience, certainly. You know a lot. Could you speak to that, please?
Yeah, 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 Chendu. I was born in Beijing, but most of my childhood was spent in Chendu.
Where it's very famous for panda bears.
And at the age of fifteen
My mom and I join my dad in a tanco Persepiti, New Jersey.
So I went from a
Relatively typical.
Middle class Chinese kid.
To become
A new immigrant.
In a completely different world of all places, New Jersey.
And to learn a new language, to learn a new culture, to embrace a new country.
And then from there on
I went to Princeton as a physics major.
But I did
Take some of the classes you took.
And then went to Caltech as a PhD student to study AI and the rest is history.
I wanna hear about
Both your parents, but I wanna 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 Shao 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 dad. So in the case of Bo's upbringing.
His father is very strict, but 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 appearance a little bit?
Yeah, so first of all clearly you read my book. Thank you for that.
It is true as a child you don't realize that as I was
just going through my own science memory. 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 buy, he finds humor and fun.
Mm.
Un serious things, you know, like he loves bugs, insects, he loves
Taking me as a kid
Growing up in the nineteen eighties in China.
There isn't much abundance in terms of material resources. So
My city Chandu 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 will
And I would just play in the fields where there's still rice fields, there's water buffaloes.
I had a puppy.
And my dad would just
Really all my memory is just like finding bugs, really, and then sometimes
My dad and I will follow some
I don't know, we took a art class, I took a

mountains, neighboring mountains to draw
Uh my entire
childhood memory of my dad is
It's 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 like
Competition awards? Nothing to do with that. Even when I came to New Jersey with my parents
Лайф бік стримли таф іммігрант лиф.
We were in a lot poverty.
And even that my memory is
That he has so much fun in yard sales.
I would just go to yard sales and those are our v
Every weekend.
It was just yay, let's go to York sales and just use that as a as a treasure hunt almost. So
He's a very curious um
Childlike mind in that way.
So I'm asking about your parents in part because I know you're a parent and ultimately I'm gonna 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 AGO politics because there are plenty of people who wanna
get into that and fight over that, which we're not gonna do. But
Why did your parents leave China? 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, right? I mean you're going from Chengdu, 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.
Early teenage Fei Fei.
Would say
I have no idea.
Because
My dad left when I was twelve, and my mom and I joined him when I was fifteen.
And those years, you're a teenager, right? Like there's so many strange things in your head and uh
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 of freedom. 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. I can tell you all the fighter jets I love from F one seventeen to F sixteen to you know to all the
So from
Things that I loved. So that's all I knew.
In hindsight, as a grown up Fay
I appreciated
My parents
They're very brave people.
Cause I don't know this age myself will just pick up
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 too. And mind you, that's
pre internet, pre AI age. So when you are
Going to a different country, you might as well go to a different planet.
Yeah.
So I think they're very brave. The grown up Fay Fei realized that they wanted me to have
An opportunity that
They think will be
Unprecedented for my education.
And it turned out
That's kind of true.
Well, certainly.
Looking at your bio, I mean it's mind boggling.
Two
Imagine all the different sliding door.
events and different
So we're gonna hop
Pretty closely.
Along chronologically, but we're gonna
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. It's 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.
Yeah.
Yeah, there are two questions.
Mostly is my mom the one who puts in the drive and the technical passion and what role did Bob play in my life?
So first one
First of all, my mom has zero technicalities. She really has no
I sometimes still laugh at her. She cannot
Do math. Let's put it this way. So I think the technical passion is just
I was born with it. My dad is more technical, but he's
You know, he he loves bugs more than insects more than equations for sure. So
I think that's you know, as a 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 package.
But my mom is much more disciplined person.
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. Both my parents never ever cared about me bringing any awards home.
Maybe I did, maybe I didn't, but I can tell you in our house
There's zero
Wall hangings of
Anything.
Which actually carry to to date.
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 the
A focused person if I wanna do something. She doesn't want me to
play while doing homework and th th that kind of thing would bother her. She would say
Just finish your palm work.
Say by six PM.
If you don't finish your homework
you're not allowed to do more homework. You have to deal with the consequences. So 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
Kid herself and cultural really crushed.
Or her dreams.
So she became a more rebellious person in that sense that
I think I did observe and experience as a
Daughter.
So
Maybe part of immigration is even part of that. She has this
Many years later she would say
I had no plan coming to New Jersey.
But I think I'm gonna survive.
I just believe I'm gonna survive, and I'm gonna make sure Фей Фей са в'їзд.
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 Persephone High School. He was my own math teacher as well as many, many students.
He entered my life.
So it's kind of bordering sophomore to junior year in person high school when I started taking AP calculus.
But she
quickly became
the most influential person in my formative years as a new American
immigrant as a teenager because
He became my mentor, my friend, and h eventually his h entire family.

American family.
And he
Became my friend.
When I was a very lonely
Yes, English a 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 listen to me as a very
I wouldn't say confused, but uh a teenager undergoing
A lot of
Лайф Термой і май уніксимстанці, а на
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 personally high school couldn't
offer a full calculus B class because it just didn't have that.
He just sacrificed his lunch hour, his only lunch hour.
To teach me calculus B C.
So it was a one to one class.
And I'm sure that contributed

me an immigrant kid getting to Princeton eventually.
But later.
А з I became teacher myself, it's exhausting to t.
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 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're 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 uh'cause personty is a heavily
Immigrant time
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. That's part of the reason I wrote the book was to
Celebrate a teacher like that.
Yeah.
I
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I have so much I wanna cover and I know we're gonna run out of time before we run out of topics. So I wanna spend more time on Bob and at the same time I wanna keep the
Conversation moving. So we're going to do that and I'll just
Perhaps hit on a few things and then uh 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 seven years.
So through that, I mean you gain perspective on
It feels like you've gained perspective on many different levels that have then helped inform what you've done professionally, right? So you
Learn to think about
Not just people who are protected in Ivery Tower, but people all the way down and 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 to international literature. So now you have this global perspective, presumably at the time in Chinese.
And
You end up at Princeton and I know we're gonna be hopping around.
Quite a bit, but I'm curious to know.
how Image Net came about. 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 such a an important chapter.
Yeah, so let me just explain what ImageNet is. ImageNet on the surface
was built in between two tausend seven and two tausend nine when I was a system professor
at Princeton and then 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 twenty years of image that was
It was the inflection point of big data.
Before you mention that 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, the it it wasn't showing breakthroughs that the public needs.
But
Imagine that together with
Two other modern
computing ingredients. One is called neural network algorithm.
The other one is modern chips.
Call GPU graphic.
Processing unit.
These three things converged.
In the
seminal work milestone worк ін твої твов
call image net classification.
Deep convolutional neural network approach.
That was a paper.
that a group of scientists did.
To show
That the combination of large data by Image Net
Fast parallel computing by GPUs.
And a neural 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.
Um
My work image that was
One third of that if you count the elements.
And I think that was the significance I feel very 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 me image that started in Princeton when I was an undergrad. You were in uh
The East Asian study department. I was hiding in Jadwin Hall, which is our physics
department. I loved physics since I was a young kid.
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 the universe. So I loved
You know, looking to the stars I loved
the speed of fighter jets and then and the intricate engineering of that eventually it translated into
The love of
the discipline that that asks the most audacious question of our civilization, such as
What is the smallest matter for
What is the definition of space time? How big is the universe? What is the beginning of the universe?
In that early teenagehood love
I love Einstein. I love his work and I wanted to go to Princeton for that.
But it turned out what
Physics taught me
Was not just the math 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 realised my passion was not the
Physical matters?
It was more about intelligence.
I was really, really enamored by the question.
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, two thousand.
And I think I consider that moment I became
Yeah.
Budding AI scientist. That was my formal training.
as a computer scientist in AI.
Then
My physics training continued in the sense that
Physics taught me to ask
Audacious question and turn them into a North Star.
In scientific terms, that nor star became a hypothesis.
And it was very important for me to define my North Star.
And my first North Star.
For the following years to come.
was solving the problem of
Visual інteligens
Is how we can make machines see the world, and it's not just by seeing the
R to be colours 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 pink team behind you.
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 North Star question.
Um 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 are many questions.

Mi 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
а за факульте і двазен сев
It was one of my happiest moment of my life, I feel
So validated my alma mater would uh 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 a epiphany.
Is the
I realize there was a hypothesis that everybody missed.
And that hypothesis was big data.
This is the point.
That I'm so so curious about.
And uh 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 entire campus. And it's fun. In that respect, it's very fun. But I'm gonna read something from a wired piece.
That disgust you at length.
And as you mentioned, big data before and after in terms of its integration into the type of research that you're describing. And
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.
And
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's too much focus on single genius.
Yeah.
Newton discovered the modern laws of physics, but yes, he is a genius not to take away any of that from Newton. But science is a lineage and science is actually a nonlinear lineage. For example,
Why did I see 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 Erf Bieterman.
Who was a psychologist who was
not interested in 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.
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
Hell the history
Of A I
In a way that so many
Ans hero so many generations of scientists.
So many
Крос дисплінарі адія
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 happen across so many people's
work throughout their life.
time devotion to science.
And we got to the point of image net.
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 and
The
Simplicity of the single hero's journey is appealing and it's simplicity, but it's almost never true. It it probably is never true. Even my biggest hero Istead, right? He th anybody who knows me, anybody who read my books knows
how much I revere him and I just I I love everything he's done.
The special relativity equation is a continuation of Lawrence transform.
So even I understand
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 a AI hype. I'm 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.
Yeah, for sure. I mean, everyone knows Watson and Crick, for instance, but without Roslyn Franklin and her X ray crystallography, it doesn't happen. Doesn't happen. It just doesn't happen point blank. We're gonna hop to modern day in a second, but with ImageNet.
I would love for you to speak to some of the
Decisions.
Let's say decisions or moments that were just formative in making that successful. Because for instance,
If you're going to
try to allow a machine to and I'm using very simple terms because I'm not technical enough to do otherwise.
to learn to identify objects.
closer to the the path that a child would take.
You have to label a lot of images, right?
So I was reading about how Mechanical Turk is
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 he mentioned that many, many people have attempted to make
Data sets.
But still
Only very few are successful. So what
made the 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 uh
Part of the success.
But it's also
As you were asking
The hypothesis of big data
It's not just size. A lot of people actually misunderstand image that's
significance as well as other data set's significance coming
With the data set.
Is
A scientific hypothesis.
Of
What is the question to ask? For example, in visual recognition
You can make a data set of discerning R G B.
And that would not be
as impactful of a data set.
That is organized around objects.
We can go down a rabbit hole of why.
Not because R to B is easier per se.
is because you have to ask the scientific question the right way.
So another example is instead of making a a data set of
Objects.
Why don't you make a data set of cities?
You know, that's even more complicated, the objects.
But then that's dialing too complicated. So
Every scientific quest
You have to have the right hypothesis 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?
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 great 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
Arrangement has higher resolution.
Is it
It's photorealistic.
Is it because it's
Everyday image that looked very cluttered.
Is it
All product shots?
That look cling.
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 this hypothesis, our conclusion is.
We nід а ліст.
Tens of millions
of high quality image
окрас
Every possible diverse dimension, whether it's
User photos or is it
Product shots, or is it
Stark photographer like
А не вині осу хай кваліті лабels.
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 gonna do that? You know, I did
try to hire Princeton undergrads. And as you know, Princeton undergrads are very smart.
But uh very high opinion of the value of the time. Yes, and they're expensive. But um 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. We want to train machines that are
measured against human capability, so we cannot shortcut that at that time.
So we had to go to what we eventually found out is called crowd engineering.
Crowd sourcing.
And that was a very new technology.
Was
Barely a year old or so.
by Amazon. They created a lot on line
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 check the first task I checked out to do just to try.
Was
labeling wine bottles or or transcribing wine bottle labels. So you the you know the task will give you a picture of a wine bottle and you have to say this is nineteen ninety nine Birdot and and all that. So 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.
А ви реаліз да вас
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 fifteen million.
high quality image that images.
It's just so wild when you look at these stories. I've just finished a book on
Genentech and there were all these little technical inflection points that also allowed things to happen. So if it had been five years earlier.
Or maybe three years earlier, right, without Mechanical Turk. Boy, like it presents a challenge. But also as you pointed out.
I mean 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 mechanics of
employing that, you can get yourself into trouble because
If humans are incentivized 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? So you have to follow the in incentives as well. How did you solve for that?
Yeah, I know. 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
Up front quizzes?
So that they they understand what a panda is.
They read the question.
And then once they get into
They qualify for that?
We asked them to label Panas, but there are some images we have pre
We know the correct answer. Some of are true penas, some of them are not true penas.
So 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. Yeah. Just incredible.
All right. And I'll 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 converging
Technological, 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 ImageNet. Pattern breakers is a really good read for folks. So let's op 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 you've had such a
Начніко,
viewpoint, meaning you've over a broad timeline, you've been able 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 facades or dimensions to this statement.
Із за а із абсолютні сивілізаціонал технології.
I define civilizational technology in the sense that
Because of the power of this technology
It'll have
We're already having a profound impact in the
Economic
social, cultural, політику.
Downstream.
effects of our society, so
I just heard this is unverified, but I just heard that
Fifty percent
of the US GDP growth last year.
is attributed to AI growth.
So apparently this number is four percent for US DP
I've grown four person.
If you take away a I
It's only two percent.
That's what means.
So
That's civilizational from an economic point of view.
It's obviously
redefining our culture, right? Think about
You're talking about the word uh sucking oxygen out of the room.
Everywhere from Hollywood.
Two.
Wall Street.
To Silicon Valley.
to political campaign.
To TikTok.
To YouTube, to Insta. Taxis in Japan. I was just there, and the videos playing on the back of the headset and the taxi were all talking about AI. It's everywhere. It's culturally
Impactful. Not only impactful, it's shifting our culture. It's gonna shift
Education.
Every parent today.
Is
Wondering 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 the grandchildren's future. So
Yeah, it's 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 moat, I guess.
But really at the end of the day.
People are 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 self dignity as individuals, as community, as society
Should not be taken away.
And that's what I worry about, because 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.
Yeah.
Just a quick thanks to our sponsors and we'll be right back to the show.
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No.
Back to the episode.
I
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 or create blind spots. And I'm curious who 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
the 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 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, sort of beyond all possibilities techno optimist view of the future where AI solves everything.
And
It's hard to believe there's a free lunch there. And then you have 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 the reality's probably in between those two.
So do you think people are
worrying about the right things or are they 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 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, 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.
That
Say people in Middle East.
In Asia.
We don't have to litigate uh why they're more worried.
But just to come closer to home, just
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 soваyza.
We have
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
What
And optimism and positivity.
Towards
The future of using AI.
The 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
still more sense of
Hope.
Um
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 Mahattan, but I want people
in rural communities in the
traditional industries in in everywhere, fifty states to be able to embrace 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.
I built world apps.
There are two levels of this answer from a technology point of view. World apps is building the next generation AI focusing on spatial.
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 linchpin technology.
But one level up.
Why am I still a technologist?
It's because
I believe
Humanity is the only species that builds civilize.
Animals build colonies or herds.
But we build civilizations.
We build civilizations.
Because we wanna be better and better.
We wanna do good.
Even though along the way we do a lot of bad things, but there is a desire.
Of having better lives.
Have a better community, have a better society.
Live more healthly.
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

Она в демофо тус.
in building civilizations
And I wanna contribute to that. That's why I'm
Стіл а сайтест та на технологіст.
And I'm building war 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 one
You pack a sandwich in a bag.
When you
Take a run or a hike.
In a mountain.
When you
Hanged.
Your bedroom.
Everything that has to do with
Сім
And turning that scene into
Understanding of the three D 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
Nope.
Between seeing and doing
Is
Support it 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 lattice leaf.
On the bread. Uh you know how to like
Put the sandwich into a ziploc bag. Every part of this.
is spatial intelligence.
Um
Does today's AI have that?
It's getting better, but compared to language intelligence, AI is still very early in that ability.
to si
To reason.
And also to do.
in world in both virtual three D world as well as real three D world. 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 robots to interact with the world.
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 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 talking to someone a couple of weeks ago and it was really inspiring is that
High school theaters
A 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.
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 worlapse model, we call it marble.
And the you
Create.
A scent.
In
I don't know, in medieval
French Town.
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
В Фрейте. Само там дезай кретор.
Само там геймін кретир.
Some of them are educators who wanna
build some worlds that transfer their students into different experiences.
are already starting to use our model.
Because they find it
very powerful to
at their fingertips to be able to
create three D worlds that they can use to immerse
either their characters or themselves into.
And just to process wise, if someone's wondering how this works, let's just say it's a
Public school teacher.
Let's just say is 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 work if someone's non technical?
Yes. So they don't need to be technical at all. They open our
Page um
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 they can actually
Go to
Anywhere they can use Mid Journey or Nano Banana to create a photo of a French medieval town, or they could get a actual photo about that.
And then they upload it, we call it prompt.
And then after a few minutes, our model gives you a three D world.
That is
Say
a part of the town. It does have a limit in its range.
And then that three D world is
Generally three D,'cause you can just use the mouse to drag and turn around.
And walk around and see that world, and then downstream.
If you wanna use it.
You have many
Ways to use it.
You can actually create a movie out of it by like using one of our
tools on the website to just put cameras and you can make a particular movie out of it. If you're a game developer. Yes. You c you can put a lot of characters in it.
If you're VFX uh 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 means 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 similar to her is not complete.
Yeah.
But that's part of the journey.
You mentioned psychiatric studies. I think that's what you just mentioned. What might that look like?
Yeah, so we actually got this researcher who call us and they're studying
People who have
psychological disorders like obsessive compulsive 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 uh strawberry field. I'm just making it up.
I mean you you can take them to a strawberry field, but but what about you wanna know if it's strawberry field
In the summer?
Or strawberry field at night?
Or is strawberry?
Or it's meaning 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 in terms of now that you're describing it, I could see how it could be.
Added into the
I mean pretty much everything, right? I mean if you think about how humans operate in the real world. Yes. Yeah. And the boundary between real world and digital world is less and less, uh 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, we'll create machines.
that can do things in real world and virtual world. So there's a lot we do.
Um
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 marky lights that are already
Very public in the world. Does there anybody who stands out where you're like, you know, there's some really tremendous people doing good work.
That's part of the reason I wrote the book, especially in the middle chapters where I wrote about the journey of doing image that that combines cognitive science with computer science and
I actually talk about psychologists and neuroscientists and developmental psychologists.
No, some of them are still with us, some of them are not. For example the
Relate Anne Trießmann, Erv Petermann, they 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.
Or
thinkers in developmental psychology in AI, I follow their work.
I think that the world of science
just to name some names, right? Liz Belke in Harvard, Alison Gobnick in Berkeley.
I love Rodney Brooke, who was a former p MIT professor in uh robotics and
And there's just a lot of them. I I don't mean to just single them out, 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 s in terms of developments in the near intermediate future. And I'll give you an example of what I mean. In two thousand eight, two thousand nine, I became involved with
Shopify the company back when they had like ten employees.
And there were a few things happening.
around that time. And you could ask questions, you know, in the next ten years or twenty 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 over appreciated, but they're definitely appreciated. The need for power is appreciated.
The trend of more AI, not less AI is appreciated.
The long term trend of robots coming is appreciated.
So these are
Appreciate it.
What's under appreciated is
Spatial intelligence is underappreciated in the sense that everybody's still now talking about large language models, but really world modeling.
Dump.
pixels of three D worlds is underappreciated because like you were saying
It powers so many things from
storytelling to entertainment to experiences to robotic simulation. I think
AI and education is underappreciated.
Because well we
are going to see is that
AI can accelerate.
The learning.
For those who wanna 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.
you know, used to be which school you graduate from, with with which degree.
That will be changing.
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.
Or like everybody's job will be
Gung 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.
Бар полісіворки, барскар.
But just
Overall society.
W what are some of the nuances?
From the job perspective, maybe this ties into what I promised earlier I was gonna ask you, which is what you
Are telling or will tell at on other ages.
Your children.
Or 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 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
training vocationally
And that's kind of a path.
And with that is
A set of structured
Credentials
from degrees and and all that. But 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 me at this point
In um
twenty twenty five hiring at World Apps.
I would not hire any software engineer who does not embrace
AI collaborative software tools.
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
Four kids?
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 strikes me as we're talking that
It's only going to get increasingly easier for
The ambitious to
act as superpowered autodidacts. 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-growth 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.
On so many levels, it's getting to the point. There are some exceptions, but of near impossibility. 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 gonna change a lot.
It's very, very interesting.
I actually think to
If the
Sku 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.
So 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.
Someone told me the story of their kids' school.
On the first day of school.
The teacher.
Ашлисет клас.
I wanna show you how I would score AI.
So the teacher given essay topic.
Show the students this is what
The best.
AI
Gave me.
And I'm gonna 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 her essay?
This is what you're gonna get.
You can use AI. That's totally fine. But you if you can
Do the work, learn.
Think.
Be the best human creator you can.
A work on top of that.
You can get to A. You can get to A plus
And that would be, in my opinion, the right way to structure the evaluation.
Is not to
hit 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 gonna 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.
I'm gonna chew on that. I know we only have a few minutes left.
Fifi, 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, mantra. Doesn't matter. Could be almost anything. What would you or what might you put
On that billboard.
What is your North Star?
Mm.
What is your North Star? 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 uh that leading to a North Star 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 too.
be so fully alive is that
Ви а за спіші.
can live beyond the chasing of just
Basic needs, right? But dreams and missions and goals and passion.
And everybody's North Star is different.
And and that's fine. Not everybody has to have AI as their North Star. 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

how to formulate your North Star and how to
Chase after that.
Last question.
Did your parents ever explain to you why they named you Fei Fei?
Yes, it's 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, the city of Beijing, my dad was bicycling to my mom's hospital.
And that inspired him to call me Fei Fei. Fay Fei. Fay Fei Oh wait, sorry.
For those who don't speak Chinese, I forgot you do speak Chinese, but for those who don't speak Chinese, Fey means flying.
So yeah, so be inspired by a bird.
You know, really quick, I'll just say because it's kind of funny. My first Chinese name that I had was Fei Ting Cheng, which is because I was very blunt and honest, so Ting Cheng, but Fei.
Fate, but when I've got to
Was first starting my tones
in China were not polished and people thought I was saying that my name was Fei Jichong, which is a air airport. So I changed uh we I petitioned my teachers and we changed my name to something less less confusing. What's your new name? Fei Yu Chang.
Oh okay. You should you sure.
It's it's uh like it's like the Shu, but it's without the at the bottom.
Yeah. Oh wow. Fake. That's way more sophisticated than mine. 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 slash podcast. They'll be able to find you easily.
And everybody should check out worldlabs dot AI and we'll put every other link here social and so on in the show links. But thank you for the time.
Thank you, too. I enjoyed our conversation. Yeah, likewise. Bye.
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