All language subtitles for 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en

Hi, I'm delighted to
have with us here

today my old friend,
professor Fei-Fei Li.

Fei-Fei is a professor
of computer science at

Stanford University and
also co-director of HAI;

the Human-Centered AI Institute.

Previously, she also
was responsible for

AI at Google Cloud as a chief
scientist for the division.

It's great to have you Fei-Fei.

Thank you, Andrew.
Very happy to be here.

How long have we known each
other? I've lost track.

Definitely more than a decade.

I've known your work before
we even met and I came to

Stanford 2009 but we started
talking 2007, so 15 years.

I actually still have
very clear memories of

how stressful it was when
collectively bunch of us;

me, Chris Manning, bunch of us.

We tried to figure
out how to recruit

you to come to Stanford.

It wasn't hard.

I just needed to sort
out my student's life,

but it's hard to resist Stanford

Really great to have you as
a friend and colleague here.

Me too. It's been a long time

and we're very lucky
to be the generation.

Seeing AI is great progress.

There was something
about your background

I always found inspiring,

which is today people
are entering AI from

all walks of life and
sometimes people still wonder,

is AI a right path for me?

So I thought one of the
most interesting parts

of your background was that

you actually started out

not studying computer
science or AI,

but you started out studying
physics and then had

this path to becoming one of

the most globally
recognizable AI scientists.

How did you make that
switch from physics to AI?

Well, that's a great
question, Andrew,

especially both of us
are passionate about

young people's future and they
come into the world of AI.

The truth is, if I could enter

AI back then more than
20 years ago today,

anybody can enter AI
because AI has become

such a prevailing and globally
impactful technology.

But myself, maybe
I was an accident.

I have always been a
physics kid or a STEM kid.

I'm sure you were too,

but physics was my
passion all the

way through middle school,

high school, college and I went

to Princeton and
majored in physics.

One thing physics has
taught me till today

is really the passion for
asking big questions,

the passion for
seeking no stars.

I was really having fun as

a physics student at Princeton.

One thing I did was reading

up stories and just writings of

great physicists of
the 20th century and

just hear about what they
think about the world,

especially people
like Albert Einstein,

Roger Penrose,
Erwin Schrodinger,

and it was really funny to

notice that many of
the writings towards

the later half of the career
of these great physicists

were not about just
the atomic world

or the physical world,

but ponderings about

equally audacious
questions like life,

like intelligence,
like human conditions.

Schrodinger wrote this
book, What is Life?

Roger Penrose wrote this
book, Emperor's New Mind.

That really got me very curious

about the topic of intelligence.

One thing led to another
during college time,

I did intern at the top
of neuroscience labs

and especially vision-related,
and I was like,

wow, this is just as
audacious question to ask as

the beginning of the universe
or what is matter made of?

That got me to switch
from undergraduate degree

in physics to graduate
degree in AI.

Even though I don't know
about you during our time,

AI was a dirty word.

It was AI winter,

so it was more machine
learning and computer

vision, and computational
neuroscience.

Yeah, I know. Honestly, I
think when I was an undergrad,

I was too busy writing code.

I just managed to blithely

ignore the AI winter and
just kept on coding.

Yeah, well, I was too busy
solving PDE equations.

Actually, do you have an
audacious question now?

Yes, my audacious question
is still intelligence.

I think since Alan Turing,

humanity has not fully
understand what is

the fundamental computing
principles behind intelligence.

Today we use the words AI,

we use the word AGI,

but at the end of the day,

I still dream of a set
of simple equations or

simple principles that can

define the process
of intelligence,

whether it's animal intelligence
or machine intelligence.

This is similar to physics.

For example, many people

have drawn the
analogy of flying.

Are we replicating birds

flying or are we
building airplane?

A lot of people ask
the question of

the relationship
between AI and brain.

To me, whether we're

building a bird or

replicating a bird or
building an airplane,

at the end of the day,

aerodynamics and physics that

govern the process of flying,

and I do believe one day
we'll discover that.

I sometimes think about this

one learning
algorithm hypothesis.

Could a lot of intelligence,
maybe not all,

but a lot of it be explained by

one or a very simple
machine learning principle?

It feels like we're still so
far from cracking that nut.

But in the weekends when I have

spare time when I think about

learning algorithms and
where they could go,

this is one of the things I'm

excited about. Just
thinking about that.

I totally agree. I
still feel like we're

pre-Newtonian if we're doing

physics analogy before Newton.

There has been great physics,

great physicists, a
lot of phenomenology,

a lot of studies of how

the astral bodies
move and all that.

But it was Newton who started
to write very simple laws.

I think we are
still going through

that very exciting coming of

age of AI as a basic science.

We're pre-Newton in my opinion.

It's really nice to hear
you talk about how despite

machine learning and
AI having come so far.

It still feels like

there are a lot more
unanswered questions,

a lot more work to be
done by maybe some of

the people joining the field

today than work that's
already been done.

Absolutely. Let's calculate,

it's only 160 years about.

It's a very nascent field.

Modern physics and chemistry

and biology are all
hundreds of years.

I think it is very
exciting to be entering

the field of science of

intelligence and
studying AI today.

Yeah, actually
that's good. I think

I remember chatting with

the late Professor John McCarthy

who had coined the term
artificial intelligence,

and boy, the field has
changed since when he

conceived of it at

a workshop and came
up with the term AI.

But maybe another
ten years from now,

maybe someone watching this
will come with a new set of

ideas and then we'll be saying,

boy, AI show us different than

what you and I
thought it would be.

That's the exciting
future to build towards.

Yeah, I'm sure Newton would have

not dreamed of Einstein.

Our evolution of science
sometimes takes strides,

sometimes takes a while,

and I think we're absolutely

in exciting phase
of AI right now.

It's interesting hearing you

paint this grand vision for AI.

Going back a little
bit, there was one

of the piece of your background

that I found inspiring,

which is when you're
just getting started,

I've heard you speak about
how your physics student,

but not only that, you're also

running a laundromat
to pay for school.

Just tell us more about that.

I came to this country,

to New Jersey actually
when I was 15.

One thing great about
being in New Jersey is

it was close to Princeton so I

often just take a weekend trip

with my parents and to admire

the place where
Einstein spent most of

his career in the latter
half of his life.

But with typical immigrant
life and it was tough.

By the time I enter Princeton,

my parents didn't speak English

and one thing led to another.

It turns out running
a dry cleaner

might be the best
option for my family,

especially for me to lead

that business because
it's a weekend business.

If it's a weekday business,

it would be hard for
me to be a student.

It's actually, believe or not,

running a dry cleaning shop
is very machine-heavy,

which is good for a
STEM student like me.

We decided to open
a dry cleaner shop

in a small town

in New Jersey called
Parsippany in New Jersey.

It turned out we were physically

not too far from Bell Labs

and where lots of

early convolutional neural

network research was happening,

but I had no idea.

It's just summer
intern at the ancient.

That is right, with Rob Shapiro.

With Michael Kearns was my
mentor and Rob Shapiro,

inventor of those things
creator algorithms.

You're encoding AI.

I was trying to [inaudible]

Only much later that
I started interning.

Yeah. Then it was seven years.

I did that for the entire
undergrad and most

of my grad school and I
hire my parents. Yeah.

Yeah. Now, that's
really inspiring.

I know you've been

bred into doing exactly
where all your life.

I think the story of running

a laundromat to globally
prominent computer scientists.

I hope that inspires

some people watching this
that no matter where you are,

there's plenty of for everyone.

I don't even know this.

My high-school job was

office admin and so

to this day I remember doing
a lot of photocopying.

The exciting part was
using this shredder.

That was a glamorous one.

I was doing so much coffee in

high school I
thought, boy, felony,

I could build a robot to
do this for the coffee,

maybe I could do something.

Did you succeed?

Still working on it. When people

think about you and
the work you've done,

one of the huge successes
everyone thinks about

this ImageNet where help

establish early benchmark
for computer vision.

It was really completely
instrumental to

the modern rise of deep
learning and computer vision.

One thing I bet not many
people know about is

how you actually got
started on ImageNet.

Tell us the origin
story of ImageNet.

Yeah. Well Andrew,

that's a good question
because a lot of

people see ImageNet as just
labeling a ton of images.

But where we began was really

going after a Northstar

brings back my
physics background.

When I enter grad school,

when did you enter grad year?

'97.

I was three years
later that year, 2000.

That was a very exciting
period because I was in

computer vision and
computational neural science lab

of Pietro Purana and
Christoph car at Caltech.

Leading up to that,
there has been,

first of all, two things
was very exciting.

One is that the world

of AI at that point
wasn't called AI.

Computer vision or natural
language processing

has founded Lingua Franca.

It's machine
learning, statistical

modeling as a new
tool has emerged.

I mean, it's been around.

I remember when the idea of

applying machine learning
to computer vision,

that was a controversial thing.

I was the first generation of

graduate students who were
embracing all the base net,

all the inference
algorithms and all that.

That was one exciting happening.

A certainly exciting
happening that

most people don't know
and don't appreciate is

the couple of decades probably

one of them two or
three decades of

incredible cognitive science

and cognitive neuroscience work

in the field of vision,

in the world of
vision, human vision.

That has really
established a couple

of really critical
Northstar problems.

Just understanding of

human visual processing
and human intelligence.

One of them is the
recognition of

understanding of natural
objects and natural things.

Because a lot of

the psychology and
cognitive science work

is pointing to us.

That is an innately optimized,

whatever that word is.

Functionality and the ability of

human intelligence
is more robust,

faster, and more nuanced
than we had thought.

We even find neural correlates,

brain areas devoted to faces
or places or body parts.

These two things lead to
my PhD study of using

machine-learning methods to work

on real-world
objects recognition.

But it becomes very painful very

quickly that we
are coming banging

against one of the
most continued to be

the most important challenge in

AI machine learning is the
lack of generalizability.

You can design a beautiful model

or you want if you're
overfitting the model.

I remember when it used
be possible to publish

a computer vision and paper

showing it works on one image.

Exactly.

It's just the overfitting.

The models are not
very expressive,

and we lack the data.

We also as a field was betting
on making the variables

very rich by hand
engineered features.

Remember, every
variable carrying

a ton of semantic meaning,

but with hand
engineered features.

Then towards the end
of my PhD, my advisor,

Pietro and I start to look
at each other and say,

well, boy, we need more data.

If we believe in

this North Star problem
of object recognition,

and we look back at
the tools we have,

mathematically speaking,

we're overfitting every
model we're encountering.

We need to take a
fresh look at this.

One thing led to another.

He and I decided we'll
just do at that point.

We think it was a
large-scale data project

called Caltech 101.

I remember the dataset.

I wrote papers using your
Caltech 101 dataset way back.

You did, you and your
early graduate student.

You have benefit a
lot of researchers,

that Caltech 101 dataset.

That was me and my
mom labeling images,

and a couple of undergrads,

but it was the early
days of Internet.

Suddenly the availability
of data was a new thing.

I remember Pietro still have

this super expensive
digital camera.

I think it was Canon
or something like

$6,000 walking around
Caltech taking pictures.

But we're the
Internet generation.

I go to Google image search,

I start to see these thousands
and tens of thousands of

images and I tell Pietro,
"Let's just download."

Of course it's not
that easy to download.

One thing led to another.

We build this
Caltech 101 dataset

of 101 object categories,

and about 30,000 pictures.

I think us really saying

that even though everyone's
heard of ImageNet today,

even you took a couple of

iterations where you did

Caltech 101 and
that was a success.

Lots of people used it for

even the early learnings
from building Caltech 101.

They gave you the basis
to build what turned

out to be an even
bigger success.

Except that by the time I
became an assistant professor,

we started to look
at the problem.

I realized it's way
bigger than we think.

Just mathematically
speaking, Caltech 101 was

not sufficient to
power the algorithms.

We decided to do image there.

That was the time
people start to think

we're doing too much.

It's just too crazy,

the idea of downloading the
entire Internet of images,

mapping out all the English
nouns was a little bit.

I start to get a
lot of push back.

I remember at one of
the CVPR conference

when I presented the
early idea of ImageNet,

a couple of researchers
publicly questioned and said,

"If you cannot recognize
one category of object,

let's say the chair
you're sitting in,

how do you imagine or what's
the use of a dataset of

22,000 classes of
15 million images."

In the end, that giant
dataset unlocked a lot of

value for [inaudible] number

of researchers around the world.

I think it was the
combination of betting on

the right North Star problem
and the data that drives it.

It was a fun process.

To me when I think
about that story,

it seems like one of

those examples where
sometimes people

feel like they should
only work on projects

without the huge thing
at the first outset.

But I feel like for people
working in machine learning,

if your first project is a

bit smaller, it's totally fine.

Have a good win.
Use the learning

to build up to even
bigger things,

and then sometimes you get

the ImageNet size win all of it.

But in the meantime,

I think it's also
important to be

driven by an audacious
goal though.

You can size your problem
or size your project

as local milestones and
so on along this journey,

but I also look at some
of our current students.

They're so pure pressured by

this current climate
of publishing nonstop.

It becomes more
incremental papers to

just get into a publication
for the sake of it.

I personally always push my
students to ask the question,

what is the North Star
that's driving you?

Yeah, that's true.

Myself when I do
research over the years,

I've always pretty much done
what I'm excited about,

where I want to try to
push the view forward.

Doesn't have to
listen to people.

Have to listen to people let
them shape your opinion.

But in the end, I think

the best researchers let the
world shape their opinion,

but in the end, drive things
for using their own opinion.

Totally agree, yeah.

It's your own fire.

As a research program developed,

you've wound up taking
your, let's say,

foundations in computer vision
and neuroscience and apply

it to all sorts of topics

including your very
visibly health care.

Looking at neuroscience
applications.

We'd love to hear a
bit more about that.

Yeah, happy to. I think

the evolution of my
research in computer vision

also follows the evolution

of visual intelligence
in animals.

There are two topics
that truly excites me.

One is what is

a truly impactful
application area

that would help human lives?

That's my health care work.

The other one is what is

vision at the end
of the day about?

That brings me to

trying to close the loop between

perception and robotic learning.

On the healthcare side,
one thing, Andrew,

there was a number
that shocked me about

10 years ago when I met my
long term collaborator,

Dr. Arnie Milstein at
Stanford Medical School,

and that number is
about a quarter of

a million Americans die of
medical errors every year.

I had never imagined

a number being that high
due to medical errors.

There are many reasons,

but we can rest

assure most of the reasons
are not intentional.

These are errors of

unintended mistakes and
solar, for example.

That's a mind boggling number.

It is.

It's been about 40,000 deaths

a year from
automotive accidents.

It's just completely tragic
and this is even [inaudible]

I was going to say that.
I'm glad you brought it up.

Just one example.

One number within that
mind-boggling number

is the number of
hospital acquired

infection resulted fatality
is more than 95,000.

That's 2.5 times than the
death of car accidents.

In this particular case,

hospital acquired infection
as a result of many things.

But in enlarge, lack of
good hand hygiene practice.

If you look at WHO,

there has been a
lot of protocols

about clinicians hand
hygiene practice.

But in real health
care delivery,

when things get busy
and when the process

is tedious and when there's
a lack of feedback system,

you still make a
lot of mistakes.

Another tragic
medical fact is that

more than $70 billion
every year are spent

in full resulted
injuries and fatalities.

Most of this happen
to elderlies at home,

but also in the hospital rooms.

These are huge issues.

When Arnie and I got
together back in 2012,

it was the height of
self-driving car,

let's say not hype.

But what's the right word?

Excitement in Silicon Valley.

Then we look at the technology
of smart sensing cameras,

lidars, whatever, smart sensors,

machine-learning algorithm,
and holistic understanding

of a complex environment

with high-stakes
for human lives.

I was looking at all that
for self-driving car and

realized in healthcare delivery,

we have the same situation.

Much of the process,

the human behavior process of
health care is in the dark.

If we could have smart sensors
be it in patient rooms or

senior homes to help

our clinicians and
patients to stay safer,

that will be amazing.

Arnie and I embarked on this,

what we call ambient
intelligence research agenda.

But one thing I learned
which probably will

lead to our other topics,

is as soon as you're applying

AI to real human conditions,

there's a lot of human issues

in addition to machine
learning issues,

for example, privacy.

I remember reading some of

your papers with Arnie
and found it really

interesting how you could
build and deploy systems that

were relatively
privacy preserving.

Yeah, well, thank you. Well,

the first iteration
of that technology

is we use cameras that do
not capture RGB information.

You've used a lot of that
in self-driving car,

that depth cameras, for example.

There you preserve a lot of

privacy information just by

not seeing the faces and
the identity of the people.

But what's really
interesting over

the past decade
is the changes of

technology is actually giving

us a bigger toolset for privacy,

preserved, a computing
in this condition,

for example,
on-device inference.

As the chips getting
more and more powerful,

if you don't have to
transmit any data

through the network and
to the central server,

you help people better.

Federated learning, we know
it's still early stage,

but that's another
potential tool

for privacy,
preserved computing.

Then differential privacy

and also encryption
technologies.

We're starting to see
that human demand,

privacy and other issues is
driving actually a new wave

of machine learning technology

in ambient intelligence
in health care.

Yeah, I've been
encouraged to see

your real practical applications

of differential privacy
that are actually real.

Federated Learning as you said,

Pray the PRRs little bit

ahead of the reality, but
I think we'll get there.

But it's interesting
how consumers

in the last several years have,

fortunately, gotten much more

knowledgeable about
privacy and increasingly.

I think the public is

also making us to be
better scientist.

Yeah, I think

ultimately people understand
the AI hosts everyone,

including us, but holds everyone

accountable for really
doing the right thing.

Yeah.

On that note, one of

the really interesting piece

of work you've been doing has

been leading several efforts
to help educate legislators.

I hope governments,
especially US government,

work through better laws
and better regulation,

especially as it relates to AI.

That sounds a very important

and I suspect some
days they'll be,

I would guess, somewhat
frustrating work,

but we'd love to hear
more about that.

Yeah, first of all,

I have to credit
many, many people.

About four years ago,

I was actually finishing my
sabbatical from Google time.

I was very privileged to work
with so many businesses.

Enterprise developers,
just a large

number and variety of

vertical industries are
realizing AI's human impact.

That was one, meaning
faculty leaders at

Stanford and also just
our president, provost,

former President and former

provost all get together
and realize there is

a historical role
that Stanford needs

to play in advances of AI.

We were part of the
birth place of AI.

A lot of work, our previous
generation have done and

all of work you've done
and some of the work

I've done lead to today's AI,

what is our historical
opportunity and responsibility?

With that, we believe that

the next generation
of AI education

and research and policy
needs to be human centered.

Having established the
humans center AI institute,

what we call HAI,

one of the work that really took

me outside of my comfort
zone were aiming

expertise is really
a deeper engagement

with policy thinkers and makers.

Because we're here in

Silicon Valley and
there is a culture in

Silicon Valley is
we just keep making

things and the law will
catch up by itself.

But AI is impacting human
lives and sometimes negatively

so rapidly that it is
not good for any of us.

If we, the experts,

are not at the table with
a policy thinkers and

makers to really try to make

this technology better
for the people.

We're talking about fairness,

we're talking about privacy,

we also are talking about

the brain drain of
AI to industry and

the concentration
of data and compute

in a small number of
technology companies.

All these are really part
of the changes of our time.

Some are really
exciting changes,

some have profoundly
impact that we

cannot necessarily
predicting yet.

One of the policy work that

Stanford AI has very
proudly engaged in,

is we were one of the leading
universities that lobbied

a bill called the

National AI Research
Cloud Task Force Bill.

It changed the name from

research Cloud to
research resource.

Now the bill's acronym is NAIR,

National AI Research Resource.

This bill is calling
for a task force to put

together a road-map for
America's public sector,

especially higher education,

and research sector to

increase their access to
resource for AI compute and

AI data and really is
aimed to rejuvenate

America's ecosystem in AI
innovation and research.

I'm on the 12-person task-force

under Biden administration
for this bill,

and we hope that's
a piece of policy

that is not a regulatory policy,

it's more an incentive policy

to build and
rejuvenate ecosystems.

I'm glad that you're doing this,

The Help Shape US Policy,

and making sure
enough resources are

allocated to ensure
healthy development of AI.

I feel like this
is something that

every country needs
at this point.

Yeah.

Just from the things that
you are doing by yourself,

not to speak of the things that

the Global AI
Community is doing,

there's just so much
going on in AI right

now so many opportunities,
so much excitement.

I found that for someone

getting started in machine
learning for the first time,

sometimes there's so much
going on and they can

almost feel a little
bit overwhelming.

Totally.

What advice do you have for

someone getting started
in machine learning?

Good question, Andrew, I'm
sure you have great advice.

You're one of the
world-known advocate

for AI machine
learning education.

I do get this question
a lot as well,

and one thing you're
totally right

is AI really today feels
different from our time.

Just for the record, you
all are still on time.

That's true when we
were starting in AI,

I love that exactly we're
still part of this.

When we first started
the entrance to

AI and machine learning
was relatively narrow.

You almost have to start from
computer science and go.

As a physics major,

I still had to wedge myself into

the computer science track or

electrical engineering
track to get to AI.

But today, I actually think
that there is many aspect of

AI that creates entry points

for people from
all walks of life.

On the technical side,

I think it's obvious
that there's

just incredible plethora of

resources out there
on the Internet

from Coursera to YouTube,

to TikTok there's just
so much that students

worldwide can learn about AI and

machine learning compared to

the time we began learning
machine learning.

Also any campuses,

we're not talking about
just college campuses

we're talking about
high school campuses,

or even sometimes earlier,

we're starting to see

more available classes
and resources.

I do encourage those

of the young people with
a technical interest

and resource and opportunity to

embrace these resources
because it's a lot of fun.

But having said that,

for those of you who are not

coming from a technical angle,

who still are
passionate about AI,

whether it's the
downstream application

or the creativity it engenders,

or the policy and social angle,

or important social problems,

whether it's digital
economics or

the governance or history,

ethics, political sciences,

I do invite you to join us

because there is a lot
of work to be done.

There's a lot of unknown
questions, for example,

my colleague at HAI are
trying to find answers

on how do you define our
economy in the digital age?

What does it mean when robots,

or software, are

participating in the
workflow more and more?

How do you measure our economy?

That's not AI coding question
that is AI impact question.

We're looking at the
incredible advances

of generative AI and
there will be more.

What does that mean
for creativity,

and to the creators from
music to art, to writing?

I think there is a
lot of concerns,

and I think it's rightfully so,

but in the meantime,

it takes people
together to figure this

out and also to
use this new tool.

In short, I just think
it's a very exciting time,

and anybody with
any walks of life,

as long as you are
passionate about this,

there's a role to play.

That's really exciting when
you talk about economics,

think about my conversations

with Professor
Erik Brynjolfsson.

Impact of AI on the economy,

but from what you're
saying and I agree,

it seems like no matter what
your current interests are,

AI is such a general-purpose
technology that

the combination of your
current interest in

AI is often promising.

I find that even for learners

that may not yet have
a specific interest,

if you find your way into AI,

start learning things,

often the interest will
evolve and then you can

start to craft your own path.

Given way AI is today,

there's still so much
room and so much need for

a lot more people to
craft their own path,

to do this exciting work that I

think the world still
needs a lot more of.

Totally agree.

One piece of work that you did

I thought was very
cool was starting

a program initially called

SAILORS and then later AI4ALL,

which was really reaching out

to high school and
even younger students,

to try to give them more
opportunities in AI,

including people of
all walks of life.

I'd love to hear
more about that.

This is in the spirit
of this conversation

is that was back in 2015.

There was starting to be a
lot of excitement of AI,

but there was also
starting to be this talk

about killer robot coming next
door, terminators coming.

At that time Andrew,

I was the director of

Stanford AI Lab,
and I was thinking,

we know how far we are from
terminators coming and

that seemed to be a little
bit far-fetched concern.

But I was living my work life

with a real concern I felt
no one was talking about,

which was the lack of
representation in AI.

At that time, I guess
after Daphne has left,

I was the only woman
faculty at Stanford AI Lab.

We're having very small,

around 15% of women
graduate students,

and we really don't
see anybody from

the underrepresented
minority groups

in Stanford AI program.

This is a national or
even worldwide issue,

so it wasn't just Stanford.

Frankly, it still needs
a lot of work today.

Exactly. How do we do this?

Well, I got together with

my former student
Ugawa Sakofski,

and also a long-term educator of

STEM topics Doctor Rick Summer

from Stanford Pre-Collegiate
Study Program,

and thought about inviting

high schoolers at
that time women,

high-school young
women to participate

in a summer program to
inspire them to learn AI,

and that was how it
started in 2015,

and 2017 we got a
lot of encouragement

and support from people like

Jensen and Lori Hung
and Melinda Gates,

and we formed a national
non-profit AI4ALL,

which is really
committed to training or

shaping tomorrow's
leaders for AI.

From students of
all walks of life,

especially the traditionally

under-served and
underrepresented communities.

Till today, we've had

many summer camps
and summer programs

across the country

more than 15 universities
are involved,

and we have online curriculum

to encourage students
as well as college

pathway programs to
continue support

these students' career by

matching them with
internships and mentors.

It's a continuous effort of

encouraging students
of all walks of life.

I remember back then,

I think your group was printing

these really cool t-shirts
that asked the question.

AI will change the world,

who will change AI?

I thought the answer
of making sure

everyone can come
in and participate,

that was a great answer.

Still an important
question today.

That's a great thought
and I think that

takes us toward the
end of the interview.

Any final thoughts for
the people watching this?

Still, that this is a
very nascent field.

As you said, Andrew,

we're still in the
middle of this.

I still feel there's
just so many questions

that I wake up

excited to work on with
my students in the lab,

and I think there's

a lot more opportunities
for the young people

out there who want to learn and

contribute and shape
tomorrow's AI.

Well said [inaudible]
that's very inspiring,

really great to chat with you,

and thank you for
attending this video.

Thank you. It's fun to
have these conversations.