AC: Welcome, everyone. Thank you for tuning
in from around the world to hear a talk by
the world renowned artificial intelligence
expert, Dr. Kai-Fu Lee. My name is Anne Chao,
and I'm the executive director of the Frank
and Cindy Liu Distinguished Visitor Series.
Thanks to the generosity of Frank and Cindy,
the Liu Lecture Series feature leaders who
have been influential in Asia related realms
such as government, business, law, medicine
and the arts. This talk is co-sponsored by
the Chao Center for Asian Studies, the Liu
Idea Lab for Innovation and Entrepreneurship
and the Ken Kennedy Institute at Rice University.
Now, I would like to introduce Dr. Yael Hochberg,
who is the head of Rice Entrepreneurship Initiatives.
YH: Thank you, Anne. On behalf of the Liu
Idea Lab for Innovation and Entrepreneurship
at Rice, the Chao Center for Asian Studies,
and the Ken Kennedy Institute, we are delighted
to welcome all of you to tonight's distinguished
visitor series lecture by Dr. Kai-Fu Lee.
The Liu Idea Lab for Innovation and Entrepreneurship
funded by the generosity of the Liu Family
Foundation is honored to be able to co-sponsor
this fascinating event. Lily which serves
as the Center for Entrepreneurship education
at Rice is proud to hold the ranking of number
one graduate entrepreneurship program in the
US and has been consistently ranked in the
top 10 graduate entrepreneurship programs
for the last decade. The lab also hosts Rice's
new undergraduate entrepreneurship minor,
and an array of programs for Rice students,
faculty, staff and the broader community.
We invite you to find more information about
Lily's programs and offerings at entrepreneurship.rice.edu.
I'll now hand the floor back to Anne Chao
to introduce our distinguished speaker.
AC: Thank you, Yael. I also want to thank
two individuals who generously donated their
time and expertise to enable tonight's lecture
to be streamed on multiple platforms: Mr.
Steven Li— Li Fanghe of JD capital, and
Mr. Davy Wang— Wang Yi of Tencent.
Now, it is my great honor to introduce our
distinguished speaker Dr. Kai-Fu Lee. Dr.
Lee is currently Chairman and CEO of Sinovation
Ventures, a US $2 billion dual currency investment
fund and president of Sinovation Ventures
Artificial Intelligence Institute. His stellar
career includes having been president of Google
China, and having served in senior executive
positions at Microsoft, SGI, and Apple. Dr.
Lee received his bachelor's degree from Columbia
University in computer science and his PhD
from Carnegie Mellon. He holds honorary doctorates
from Carnegie Mellon and the City University
of Hong Kong. He is also the co-chair of Artificial
Intelligence Council, the World Economic Forum
Center for the Fourth Industrial Revolution,
and a Fellow of the Institute of Electrical
and Electronics Engineers. Dr. Lee has 50
million followers on social media. A giant
in the field of artificial intelligence, Dr.
Lee built one of the first game playing programs
of Othello and defeated a human world champion
player of the game in 1988. He also created
the world's first large vocabulary, speaker-independent,
continuous speech recognition system. He founded
Microsoft Research China, later renamed Microsoft
Research Asia, which trained a generation
of AI leaders in China, such as the Chief
Technology Officers in Baidu, Tencent, Alibaba,
Lenovo, Huawei and Haier. During his time
with Apple, Dr. Lee led AI projects in speech
and natural language, which were featured
on Good Morning, America. He has authored
10 US patents, and more than 100 journal,
conference papers. His book, “AI Superpowers,
China, Silicon Valley, and the New World Order”
was on the bestseller list of both the New
York Times and The Wall Street Journal. Now
please join me in welcoming Dr. Kai-Fu Lee.
KFL: Thank you very much. It's an honor to
have a chance to talk to such a large group
of young people, entrepreneurs. So to fit
in with the event, I am giving a talk I've
not given before, “Things that I wish I'd
known when I was 20.” So I will chronicle
my career, things that I learned, and talk
a bit about entrepreneurship, technology,
and family values. And in the closing, I'll
be happy to take questions about any of these
topics.
So starting when I was 20, this– this was
a long time ago. I was at Columbia, a liberal
arts major in the humanities. This was something
I really was glad that I did. Because I didn't
know the difference between a liberal arts
college and an engineering school. And being
an engineer, I ended up in the liberal arts
school. But it was a happy mistake, because
it was there that I got to read really a lot
of the classics and taken a lot of courses
that would not have been possible in engineering,
and absorbing all this human wisdom and literature
made it possible for me to speak and to write;
and I think to the extent that I've been somewhat
successful as an author and a speaker, a lot
of it was due to this decision. At the time
since I was in liberal arts, I thought I would
be a pre-law; because at the time, being a
doctor and a lawyer were the two most desirable
professions. However, after about a year and
a half to two years, I found that pre-law
was just not for me. I was a political science
major, and I was falling asleep in class,
had not- not really doing very well and really
not appreciating all the wonderful things
that I knew my classmates enjoyed. So, at
the time, I was 20. I made a change and became
a computer science major. And sorry, let me
make sure I'm still presenting. [Pause] Okay.
All right. Yeah. So, so when I was 20, I found
that something that I really loved, and it
was computer science and artificial intelligence.
I was very lucky that at Columbia, I had an
excellent professor in natural language and
in computer vision and in classical artificial
intelligence. And this really fascinated me
because as a young engineer and scientist,
I wanted to understand how human cognition
works. And I thought this pursuit would allow
me to do that. And I also found that I was
quite good at computer science, unlike political
science where I struggled to get B's. At computer
science, I hardly had to study and I was getting
A's. Probably the only small regret moving
on from computer science, was I transferred
out of political science into computer science
in my junior year, and that was the year Barack
Obama transferred to Columbia and became a
computer a— sorry— a political science
major. So just barely missed getting to know
President Obama. But computer science was
wonderful. Artificial Intelligence was wonderful.
It became the cornerstone of my career.
And that leads to the first thing that I want
to share with you, and it's a common saying
in, in the West, “do what you love, and
you'll never have to work another day in your
life.” Many people attributed it to Confucius,
many other people say, “there's no way Confucius
would say something like that.” For those
of you who are Chinese, this is the closest
saying from Confucius that I think matches
it. And I think there's a lot of wisdom in
that. Because if you're doing what you love,
you will be thinking about it when you're
sleeping, showering, eating, and you can't
help but be successful. And if you do something
you love, you'll be good at it. And if you're
good at it, you will love it. This kind of
virtuous cycle continues. And beyond artificial
intelligence and computer science. I found
that to be true for many, many other things
that– that were, are dear to me.
Then moving onto from Columbia, I went to
Carnegie Mellon and studied speech recognition
and artificial intelligence, and there's my
father trying the system that I built. And
I was there at a very critical time: it was
a time when expert systems were about to go
out of favor. But my PhD advisor, Raj Reddy,
a very well known expert, who was very much
strongly in belief that expert systems were
the way to solve speech recognition and AI
problems. He was a wonderful man, tremendous
mentor. But I really did not agree that expert
systems are extensible. I wanted to pursue
machine learning that is teaching the computer
using what it's good at, crunching numbers
to, to exhibit intelligent behavior; rather
than follow step by step, how humans thought.
So for the first year, he gave me directions;
I wrote a paper on expert systems. It got
accepted. It became clear that I could graduate
with my PhD and get it over with, and be done
with it, and move on to do what do what I
love. But I thought deeply about doing things
that I love. And I did not love doing expert
systems. My intuition was that this was not
scalable; this would be fragile. And only
technologies that scale with the power of
computing, that scale with Moore's law, that
scale with more data, can be powerful enough
to change the world. So one day I drummed
up enough courage and went to my professor
and said, "I love you as my advisor. I want
to study with you, you're wise and give me
great directions. But on this expert systems,
I just don't want to do it anymore. Here's
the approach I want to take." And I proceeded
to explain hidden Markov models, which are
a variant of neural networks, and why I think
it was a better approach. And the thing that
he said to me will stay with me forever. He
listened carefully, asked me questions. And
then he said, "I don't agree with you, but
I support you." And that was tremendous feedback
for me that I did not expect I would convince
him; and I was prepared for the worst outcome,
that either he would force me to do what he
wants, or force me to go get a new advisor,
but he actually supported me.
And this was not just an act of generosity,
and it was not just a respect of individual
thoughts, but that he really saw me as a scientific
equal, and that I should pursue what I believe
in; and that he not only verbally supported
me, but he gave me resources. He actually
had millions of dollars of funding from DARPA,
and it was a lot of money back then. And he
let me use all the machines of the people
who were doing expert systems. As for those
of you who are computer scientists, you know,
expert systems don't take any cycles. So I
wrote programs that took over all of their
computers and ran all night, and achieved
great results that persuaded them it could
work. And he proceeded to persuade DARPA to
collect what was then the largest database
for speech, which was also critical to making
my PhD thesis work.
So I dedicated my book to him and I called
him my mentor in AI and life, because of what
he said that “I don't agree with you, but
I support you” is not just an act of generosity.
It is a management style. It is a leadership
style. It's something that I hold dear to
myself. And whenever one of my employees tells
me something I don't agree with, this is my
default answer. And I truly support them to
try what they want. Very likely, they're right;
if they're wrong, if they love it, and it's
still time well-spent; and if they learn they're
not right, then they move on having felt they
had the support. So this is really an amazing
leadership style.
And, and as I was lucky enough to work at
Apple, Microsoft, Google, full of smart people,
this is the way to lead smart people. Not
to be smarter than them, that's never possible;
but to let them know that you understand and
support them. There's nothing more important
than that about leadership. Fortunately, my
PhD thesis did work out, we built the first
working speech recognition system. And I then
went to Apple and the technologies became
a part of Apple Computer. Siri that you use
today is now managed by the team that I then
recruited. I did quite well at Apple, it was
a lot of fun. But then Apple got in trouble.
So I went to a company called SGI, no— probably
no longer that famous nowadays; but then it
was the hottest company to work at in Silicon
Valley. It was the equivalent of Google at
its height. In fact, Google's building was
bought from SGI, that's how cool SGI was.
And I had a group of smartest engineers work
with me on very exciting new technologies.
The technology we work on was called, “3D
browser.” Hmm, you're probably thinking
I've never heard of that. You talked about
“Siri,” I heard of that; “Speech recognition,”
I know that, you know; “Neural networks,”
okay; but what is a 3D browser? Well, it was
an idea that a bunch of engineers had, that
wouldn't it be great if we had the virtual
reality inside the browser, and we were in-
inside virtual 3D worlds, we can navigate,
we can move objects, we can build wonderful
ads. And we proceeded to build it. It was
amazing how, how well the system worked, given
how slow the computers were at the time. And
we believe strongly that if we build it, they
will come— they being the customers. And
what actually happened was we built it and
nobody came. We hardly sold the products.
We couldn't— we got a bundle with Netscape
browser, but people didn't use it. There was
not much content.
And ultimately, a new CEO came into SGI, and
he was very upset at me for starting this
project and spending so much of the company's
resources on it. And I told him, "Look, this
is the world's best technology in virtual
reality. You don't– you don't want to keep
it. We should figure out how to– how to
create value out of it." So he gave me a deadline
to sell the company. And we originally thought
we sell it for a decent amount of money. We
eventually settled for a much less amount;
we did get it sold. But unfortunately, after
it was sold, the company that bought it got
into financial trouble. So they laid off all
of the hundred engineers who worked on this
project with me. And I felt extremely guilty
and also extremely stupid that why would I
have started the project like this without
thinking about utility, only thinking about
coolness? This is a mistake that I think a
lot of young people make when they want to
go into entrepreneurship.
So I would urge all of you listening to be
thinking about innovation. Not– not what
matters about innovation is not innovation
itself, but useful innovation. That, something
looks really cool like this picture is actually
not only useless but counterproductive. And
that has been my model in developing both
my research and technology. Now, of course,
researchers in universities should not be
bound to thinking about utility at the time
of doing innovative work. But if you're thinking
about a company, a technology, a product,
then the utility must come ahead of innovation.
In fact, when you start meeting with venture
capitalists, you will realize that venture
capitalists are taking huge risks by investing–
by investing in you. They're taking people
risk, market risk, competition risk, and execution
risk. They don't want to take a technology
risk. So you want to go to them with a proven
technology that already works, and– and
you want their money to make it useful and
valuable. And that's something that's very
obvious today.
I'm sure most of you already know, but I did
not. And I think someone with a PhD, coming
from academia, may fall into the same mistake.
So I thought I'd list that as my third lesson
that I would share. After SGI, I moved to
China and started Microsoft Research China,
which later became Microsoft Research Asia.
It was a huge success. MIT Technology Review
called it “the world's hottest computer
lab.” Many of the founders— by the way,
I'm having a reunion with them this weekend—
became CEOs and CTOs of some of the biggest
and hottest and most exciting technology companies
in China. I was very lucky that I had a big
brand like Microsoft, and the funding to attract
great people like that.
Year 2000, I'd be repatriated back to Microsoft
Headquarters, had the fortunate opportunity
to work with Bill Gates on a number of projects.
And it became boring for me after a while,
as I saw internet emerge, and Microsoft at
the time was slow to catch on internet. The
hottest company everybody wanted to work for,
at that time was, of course Google. That people
would joke that if they didn't get invited
to an interview, they must not be very smart.
So I thought I had to get myself an interview.
So I emailed Eric Schmidt. Many of you may
think, "Wow, you're, we're already doing pretty
well! Shouldn't that– shouldn't they have
poached you or called you or something? Wouldn't
it be beneath you to, to contact them and
promote yourself?" But actually, that's an
interesting small lesson I want to share,
also. As it turns out, Google and Microsoft
at the time, were in negotiating a non-solicit,
non-hire— that agreement never came to fruition.
And that agreement would have been illegal
had it come to fruition, but they were nevertheless
negotiating for months. So they were forbidden
from contacting Microsoft people in hopes
that maybe a– an interesting agreement could
be reached.
So as it turns out, had I not written Eric
Schmidt indicating my fascination, respect,
and interest in working for Google, I would
never have gotten the job. So don't let your
own so-called self-esteem get ahead of you.
If you want something, go for it, be proactive.
That's what I learned. But what I also learned
was a very rude surprise, which was that Microsoft
decided to sue me over trying to go work for
Google; it was over something called “non-compete”.
I won't go into the details in here. I would
just say that many of the accusations made
at the time, sometimes by Microsoft, sometimes
by various people, and sometimes just by speculative
press, were incredibly inaccurate and damaging.
There were many, many headlines that talked
about, “Is it possible that I might have
taken intellectual property?” Of course,
I did not. “Is it possible that I may have
been compensated to not leave Microsoft?”
Of course, I was not. There were talks about,
“Did I try to bring people into Google from
Microsoft before I left Microsoft?” Of course,
I did not. But those speculations persisted.
And it became really the darkest moment of
my life during the two months that we were
pending for the judge to make a decision.
It became so stressful that I could not sleep
anymore, that I was frustrated and unhappy,
and I could not eat, lost a lot of weight
for the first few weeks. And one time, I was
so sick and tired of hearing, seeing myself
on TV and on newspapers, that I thought I
would, on the– on the trip from Seattle
to– to New York, I thought I would finally
get a rest by going to my– my seats, opening
up a magazine, and there was— I was again
on the cover, because this was the lawsuit
of the century. Probably the corporate lawsuit
of the century, because it's two giant—
it's David versus Goliath. And it's a very
something that news people love to write about.
But it resulted in incredible frustration
for me. But what got me out of it was that
about two weeks into it, my wife told me,
"Look, you gotta calm down and don't be this
frustrated. Think about what makes you peaceful
and serene." And what occurred to me was when
I went to Catholic Middle School in Tennessee,
I learned the Serenity Prayer. And the Serenity
Prayer is such a three sentences of wisdom.
It says, “God, grant me the serenity, to
accept the things that I cannot change, the
courage to change the things that I can, and
the wisdom to know the difference.” And
when applied to my lawsuits, it became very
clear that the fact that reporters will write
what they write is something I cannot change.
And what I needed was to have the courage
to change the things I can and the wisdom
to pick the right ones.
So with that, I went into the lawsuit, thinking
about what are some ways to fight back? What–
what are some things that I can change? I
can change the way the judge sees things.
There are all these terrible things in the
press. Why don't I get him to see the truth?
I, of course, cannot talk to him. And, and
my lawyer and my company won't let me talk
to the press. But I thought I'd take a chance.
There was this reporter I knew from Seattle
Times. I know that everyone, Seattle, in Seattle,
including the judge, reads the Seattle Times.
So I called her and asked if she could write
my side of the story. And, and she did, and
I think that was a very influential piece.
And then I was energized by small things like
that, and still faced huge challenges. For
example, during the discovery process, I asked
for some emails that would prove my innocence.
Of course, Microsoft was obliged to send me
the emails, but they were not prevented from
sending more than I asked for. So they sent
me 300,000 emails that I had to sift through.
And they not only sent 300,000 emails, but
they were not in text format. There were pictures,
because that's what legal law allowed them
to do. Fortunately, I was at Google. So we
had very good OCR technology. We scanned everything,
and we use Google Search, and we found all
the things I needed to find. So things really
changed around when I realized that our time
is limited, and we can only focus on things
where we can make a difference. If we allow
things that we cannot change, to frustrate
us, to slow us down, even to stop us, then
we're destined for a bad outcome. So this
was something that was very helpful to get
me out of the, a big, big difficult situation.
I spent four good years at Google; I learned
a lot. We build a lot of great products. And
then Google asked me to stay, stay four more
years beyond my original commitment. And at
the time I saw that mobile internet was, was
blossoming in China. And many of my smartest
people have already left from Google. They
went to start their own companies, some started
VCs, and some of them started very successful
companies. Some of the companies you hear
about today, were started by my, my employees
back then. For example, “Kuaishou” is
a company valued at about $20 billion. It's
kind of a Tik Tok competitor in China. That
was by one of our engineers. And “Pinduoduo”
over 100 billion dollars, also by one of our
engineers. So I saw people like these left,
one by one, and– and very successful in
raising money. So what became clear to me
was, this is going to be the Silicon Valley
of China, back in the days of early Silicon
Valley, back in the days where Apple, Microsoft
and many— Intel and many great companies
were founded. This is the time to be in entrepreneurial
space. But it was risky. And I didn't have
quite the right experience. And of course,
Google made me a nice offer to stay. So how
would I choose between the two? And that comes
down to another very wise advice. You've probably
heard Steve Jobs commencement speech at Stanford.
If you have not, I would suggest that you
listen to it. In it, he talks about, “follow
your heart.” That is, your heart knows the
answer. You just need to introspect- introspectively
look in and get that answer; and not over
rationalize and explain, and, and debate;
and believe that if you know your heart and
follow your heart. What happens in your life
will be like connecting the dots. But he also
warns not to plan your life by connecting
the dots forward. Because you cannot know
what will happen in the future. If you plan
your career by doing job A, job B, job C;
and then starting company X, company Y, company
Z. That's just fantasy. You cannot connect
them going forward. Just make the one decision
you need to make, based on what your heart
tells you. Years later, you will look back
knowing that you, your heart made the right
decision because these dots connected. So
as it relates to my decision about whether
to leave Google and start Sinovation, which
is a tech VC, I can show you the thoughts
that I drew at the time.
So at the time, I was at Google, at crossroads.
I realized that in my life, because I love
computer science, I picked it. It wasn't like
it was very good for getting a job back in
1980. There were almost no computer science
jobs. Doctor and lawyer was the way to go;
to if you're a computer science major, you
got to go work for IBM or Bell Labs. But I
chose it anyway because my heart tells me,
that's what I wanted to do. Then I went to
Microsoft, and Microsoft China, above all
things, something that many people would not
do. But I thought this was the company that
knew how to build software. And I wanted to
learn that. Then I tried. Then I, I tried
to go to SGI, and we tried to sell the company
and raise money. And I failed at the feet
of Silicon Valley venture capitalists. So
I learned my lessons, and how they think and
what they do. Then I went to Google, which
taught me the hardest things about internet,
mobile, ecommerce and so on. So I then realized,
“Well, I now have just what it takes to
become a tech investor.” Not any investor.
I wasn't ready to build a SoftBank or a Sequoia.
But as a tech investor, investing in early
stage companies, I know technology, I can
relate to them. I understand what VCs are
looking for. And my thoughts were being connected.
So this would be my final thoughts relating
to Steve Jobs' speech.
I also realized something else I did over
the past 20 years. At the time I did this,
maybe the past five or six years, I had written
seven letters to the Chinese students, because
I felt at the time— this was year 2000s—
that the Chinese students were not as exposed
to a lot of the mentorship and thinking that
American students were more privileged to.
So I felt an obligation that I should do that.
It was something that my heart told me to
do. My corporate PR said not to. My corporate
PR said, "Who are you to write letters? You
didn't even grow up in mainland China." But
I thought this would be helpful to people
so I did it. And then I gave many, many speeches
to a total of maybe 500,000 students. This
is face-to-face before the days of Zoom. I
wrote about nine books in Chinese; four of
which were dedicated for Chinese students
about their similar content to this talk.
So you can imagine this, these are efforts
to help them. And I had 50 million followers
on social media. So what better basis and
background could one ask for, to be to, to
declare myself now, “the mentor to young
entrepreneurs,” and because I have been
sincere in helping young people. So this is
just the natural next step to close the loop
on connecting the dots.
So in fact, the first year, we helped about
forty people, forty the young entrepreneurs.
And many of them came in as engineers, and
I taught them entrepreneurship. Not all forty
became CEOs, but the numbers are staggering.
Here you see the recruiting poster that we
created after one year. And the recruiting
we had for campus hires was, “join your
company.” Meaning: join as an engineer,
become a CEO, get our funding and become the
next great company. And these eight people—
who are, who were more or less randomly picked;
maybe just picked under good looks or something—
out of these eight, there are now five CEOs
of companies valued over $200 million. In
particular, the lower left is someone who
actually runs Taobao and Tmall today, so about
a $40 billion revenue business. And he runs
it inside– inside Alibaba. His name is Jiang
Fan, and he was one of the engineers we brought
in. So I'm incredibly proud of the work we
have done in nurturing these young entrepreneurs.
Then finally, what about connecting the dots
for AI? As I told you, I started AI early
at Columbia. I applied to CMU to learn AI,
lower here you see my— a clip from my PhD
application letter. I decided AI would be
my life fairly early, while most people at
the time laughed at AI. AI was called the
thing, that things that never work. Whenever
something in AI worked, it would become engineer-
engineering, products, technology; if it doesn't
work, it's called AI. So we were laughed at
for decades. And nevertheless, I joined it
because I thought this pursuing how people's
cognition works was something that was really
dear to me. And I wanted to follow my heart.
So I went on to lead the AI team at Apple,
at Microsoft. And also I went to learn modern
AI at Google. This was where distributed computing,
deep learning and using massive technology
and TPUs, it really elevated AI to a next
level. And reflecting on all the things I've
seen invested, I wrote a best selling book,
“AI Superpowers.” And we invested in at
Sinovation, 50 AI companies, including five—
soon to be six, Unicorns, the largest in the
world. And we also built an AI Institute of
our own AI people. So these thoughts also
connected for the age of AI. And, and reflecting
back my own AI journey, I pursued it with
total dedication. I really loved my work as
it relates to technology, AI, whether it was
with Apple, Microsoft, or Google, I felt like
the luckiest man on earth. And luck— the
work was the only thing that mattered to me.
The picture you see on the left, you're probably
wondering what it is. That was my bedroom
after one of my surgeries. I had a surgery
in late 2000s. And I was bedridden— not
allowed to leave my bed. At the time, there
was no mobile phone to play with. I only had
a Blackberry. So I had my company, my team,
my IT team, construct this device that allowed
me to lie on my back, put the keyboard and
mouse, on my stomach and continue to work
with a monitor over my head. So I was that
much of a workaholic. And I worked alongside—
and after Google, I worked alongside the Chinese
“996” entrepreneurs— “996” means
9am to 9pm, six days a week, and that's a
standard workstyle. Some Chinese entrepreneurs
do “007”; that's noon to midnight, seven
days a week. I wasn't that crazy, but I was
working incredibly hard. And you see all these
New York Times headline talking about the
crazy work, work hours, that exemplify the–
exemplified China. And that's a large extent,
I think the reason that– that China has
been successful. So I believe the work ethic
that was born out of the Industrial Revolution,
really brainwashed all of us that working
hard was important; and that work define the
meaning of our lives. And I became a willing
victim to this workaholic brainwashing; and,
and that's why I worked so hard, starting
in corporate, and then also into Sinovation.
And the work has led to many great results.
I'm quite proud of the accomplishments of
my team and the work that I have done; and
also raised some concerns. I was talking to
Frank earlier, he asked about, “What about
AI is getting so mature, so good that it can
take routine jobs and blue collar job, white
collar jobs? Should we all be worried about
our jobs being taken taken away?” So hold
that thought I'll come back to this point.
But I want to get back to my life, my obsession
with– with work.
That obsession ended about six years ago when
I was diagnosed with four stage lymphoma.
What you see here was my PET scan, and it
shows about 20 malignant tumors jumping out
in my intestines area, melting away my ambition
and any desire to work further. I was faced
with the real likelihood that my life may
end in a just– just a matter of a few months.
And during that time of ultimate uncertainty,
I did a lot of thinking. And I sought a lot
of wisdom. I read a lot of books. And I talked
to in particular, one very wise person, Master
Hsing Yun. He's one of the oldest and wisest
Buddhist monks and priests. And he was very
approachable. And he took the time to see
me. I spent a weekend before my surgery with
him. And he had a— we had a great conversation,
about one hour. And he– he basically asked
me what my wi- life was about. And all I could
say was, “work, work, work.” And he said,
"Why do you work so hard?" And I said, "Because
I wanted to maximize my impact. Make a difference
in the world." And then he said something
that really surprised me. He said, "I don't
believe you." He said, “Whenever someone
says they want to maximize the impact, change
the world, I always doubt— are they really
doing something altruistically for the world?
Or are they just trying to make themselves
more famous, trying to get more wealth and
fame?” And I could not tell him that I was
fully altruistic. And it's the kind of thing
that sounds grandiose, that we really tried
to fool ourselves. But when in reality, it
feeds our greed and feeds our desire. And
he left me with the phrase that, “Remember
Kai-Fu,” he said, "The richest man is not
he who has the most, but he who wants the
least." And whenever I feel overcome, again,
by desire, I rethink about this; whenever
I want to have the urge to change the world
again, which I do, I want to separate, “Am
I doing it for the world or am I doing it
for me?” This was one big lesson that once
took too long for me to realize. And through
his wisdom, I saw how foolish it was to base
my entire self worth completely on my accomplishments
and my work. And I also realized that my life
was quite out of order. I neglect— had neglected
my family in my hard work. My father had passed
away, and I never had the chance to tell him
that I loved him. My mother had dementia and
no longer recognized me. And my kids had grown
up, and I really didn't get to know them.
And one of the books that I read during my
chemotherapy was Bronnie Ware's book about
the regrets of people on their deathbeds.
And she found that no one wished that they
had worked harder, but the single thing they
wanted the most— and there are five here,
but the number one thing was— Spend more
time with their loved ones.
So fortunately, with some sense coming back
to me, having learned from Master Hsing Yun
and Bronnie Ware, I now am in remission. And
that's why I can come here and give you this
talk. I've changed my work style, so that
I'm spending much more time with my loved
ones; I was able to move back near my mother
for a period of time, before she passed away,
to finally spend with her. And I, whenever
my children want my time, I put that as a
priority; work must take a backseat. And I
travel with my wife whenever she would go
with me. So this near life experience really
changed how I thought. And, and another thing
that Master Hsing Yun told me was that, changing
the world was too presumptuous, and maybe
a cloak of magnanimity for my own greed. And
that he suggests, the thing to do, is to care
for others from your heart, and lead your
life by giving unconditional love. And it
was then that I realized that while I did
not give conditional— unconditional love
to others; but my family and my friends have
done that for me. So I'm really— I've recommitted
myself, this is something I would change.
And I think, the last few years, I've done
a much better job.
And that also leads us back, to the AI taking
jobs away, because it gave me a new epiphany,
epiphany about how to look at AI and humanity,
that AI will beat us at doing all kinds of
repetitive tasks. But the type of thing that
separates us from AI is love. Also creativity,
as shown here. AI is good at doing routine
things. AI is good at computing, optimizing;
but AI cannot create, has no self awareness,
and no love. And love is what differentiates
us from AI. Despite what science fiction may
portray, I can tell you responsibly, that
AI cannot love. AlphaGo does not love to play
Go, it does not feel good that it beat Ke
Jie; it does not feel sadness that it lost
the game. In fact, it doesn't know why people
play Go at all. So with this idea that the
two dimensions that people can do that AI
cannot do, our creativity and love, we can
rethink about a blueprint of human-AI coexistence.
So if we put creativity on the x axis, jobs
that require lots of creativity on the right
side, jobs that are more routine and optimizing
on the left side; and then love not needed
on the bottom, love needed on top; we will
see that the lower left jobs are destined
to be taken by AI; but all other three jobs
are human AI symbiosis. On the lower right,
we will have tools, AI tools, helping people
to become more creative, helping scientists
invent new drugs; on low— on the upper left,
we have jobs like doctors and teachers where
AI can do the analysis, the diagnosis, the
statistics, the root— the work about patient
statistics, whereas the doctor is the human
interface, making the patient feel better,
in confidence that he or she would recover.
And then on the upper right, jobs that require
creativity and love, only human can do that.
So the, what's incumbent us in— upon us
in this society is realizing that the largest
number of jobs are on the left side. After
all, we all wish everyone in the world can
be creative, but only a small percentage are.
But– but on the left side, we can see that
the many people who are on the lower left
who might be faced with AI displacement, there
is a path that they should move up to jobs
that require greater empathy. To give more
precise examples, talking about people who
work in factories, assembly line, truckers,
people who do repetitive jobs, BPO, paper
pushing, those jobs will be replaced by AI.
But jobs that require human touch and love,
jobs that involve a great tourist guide, a
great concierge, a great nurse, great teacher.
These are jobs of love that only people can
do. So migration from lower left to upper
left is the path to go.
Give you an example, World Health Organization
estimates that about 18 million more healthcare
services jobs will be needed in the next 15
years to reach sustainability requirements
throughout the world. So a lot of jobs are
available. Just because AI is taking the routine
jobs, it does not mean it does not mean that
there will be no jobs for people. In fact,
if we look at the longer term, I would tend
to believe that in 30 years, looking back,
we would feel that this has been the good,
good thing that AI came about. Because it
did not, we would not look at it as AI stealing
our jobs, but AI liberating us from ever having
to do routine job again, allowing us to do
things that we love and things we're good
at.
So in conclusion, these are the seven things
that I learned. I would add an eighth thing
now, in having heard my story, what should
become obvious to all of you is that I learned
the most when I face the greatest obstacle,
when I have the greatest failure. It was when
my SGI company fell apart that I learned the
most important thing then. And it was when
I was facing the lawsuit of the century that
I learned a tremendous deal. And it was when
I was facing death that I learned absolutely
the two greatest lessons. So please take with
you the importance of failure and learning
from failure. Failure is not a punishment
on what you didn't do right, but the chance
to learn a valuable life lesson. Thank you.
AC: Thank you so much, Dr. Lee, for this wonderful,
heartfelt talk, and I think I can speak for
the entire audience that we have learned a
great deal. And as you were talking, I received
a text message that there were about 100,000
people listening to you, I think all over
the world and the number was still climbing.
We have some wonderful questions in the next
few minutes. What AI area can US and China
collaborate on now, despite the seasonal political
tensions between the two countries?
KFL: I believe academically, US, China, and
Europe, the three biggest groups in the world
working on AI are still collaborating a great
deal. And that has what that is what has propelled
the world forward in AI applications. So I
think that will continue and I hope that can
continue. Academia is without national boundaries,
and people are able to make progress only
by standing on the shoulders of giants.
And then specific to industries. I think healthcare
would be an excellent one, certainly facing
COVID-19 and the issues in dealing with that,
whether it's social distancing, contact tracing,
or coming up with AI assisted vaccine discovery,
that would make another area. And also climate
change, and anything I think that's good for
the future of humanity would be a good topic
for that discussion.
AC: Thank you. The next question, do you think
AI will ever be able to articulate why they
make certain decisions, aka verbalizing the
black box?
KFL: Yes, I think they will. But there's a
problem because, you know, we think of our
reasons for making decisions as simple if-then-else.
And that in some sense is a blessing because
of the simplicity. But it's also an inaccurate
way to make decisions. Whereas we think of
three or five or 10 factors in making a decision,
AI thinks of 3000. So when it has 3000 things
intermingle in the mathematical equation,
it's impossible to articulate it with fidelity.
I think in some sense, AI is just too smart
and too complex. And even if it could express
the 3000 dimensional math equation, we can't
understand it. So I think first we have to
accept that, rather than criticize the blackbox.
Now, now it is completely reasonable in certain
domains, such as medical, autonomous vehicles
to demand an answer. Of course, we as users,
we need an answer. And I think the kind of
work that needs to happen are working on explainability
and interpretability for AI. The former approach
is, take a large neural network that reached
a decision, going back and finding the most
concise way to approximate why it made the
decision in a way that makes sense to people.
That's one field of research. The other one
is, are there learning methodologies that
by definition is explainable when decisions
are made? And I do think there's valid reason
to work on both. Right now, the AI systems
are still largely a black box; but I think
for AI to become truly pervasive, we have
to overcome this problem.
AC: Thank you. So the next question, I think
there are two we can combine into one. What
advice do you have for dealing with professional
or personal rejection? How does one know rejection
the simple roadblock and not a sign of something
greater; and in the currents, how do you advise
Asian Americans to break the glass ceiling?
KFL: Right um, I think rejection means that
you are pushing the limit. It does not mean
that you're unqualified. If I've been rejected,
I'm sure many of you have been rejected. When
you're rejected, find out why. Understand
the true reason. Sometimes people if you're
applying for a job, sometimes they just give
you a form letter. Sometimes they only give
you some nice words. Or sometimes they even
tell white lies like the job has been filled,
we're no longer hiring, when that may or may
not be the truth. So be personable, connect
to people, whether it's a headhunter HR specialist,
or your hiring manager that rejected you.
See if you could sit down with them; have
coffee with them, understand the true reasons
that you were rejected, and only then will
you learn what are areas that you need to
improve on.
Otherwise, it's like the black box we were
talking about. If you're just trying a lot
of black box applications, some say yes, some
say no, you will never know why. What it is
that made you special and what is that made
you unacceptable. And when you know the reasons
that they rejected you, you can decide if
that's something you can improve upon, or
is that the company culture that you don't
want to be in. So be inquisitive, be proactive,
find out the true reasons. And then that becomes
the potential basis for your self improvement
plan.
And about the glass ceiling, I think many
of the people in this call, if you're living
in America, were probably born in the US or
moved at an earlier age. I really think there
is not that strong glass ceiling. Sometimes
when you believe in something, it becomes
a self-fulfilling prophecy. You know, we're
seeing many Asian Americans do quite well
in American companies. In particular, I think
Indians have done exceptionally well. And
if you think about why is there less so-called—
um, or let me not use the word I don't believe
in— why there's– there’re more success
by the Indian Americans in– in their careers.
I think I find that the Indian Americans I
work with in the US, they are very personable.
They're able to connect with people, they
try to melt into the society, and they're
articulate. I think on the average, some of
the Chinese– Chinese engineers focus too
much on work, are not as good as at self promotion.
I don't mean self promotion in exaggerating,
I just mean, letting your boss know you did
the work, or letting your boss's boss know
that you're the person behind the work; making
sure you get fair credit, not excessive credit;
and make sure that you realize communication
is very important. I think a very famous Greek
philosopher once said that, “A person who
has great ideas but cannot articulate is absolutely
not different from someone who has no ideas.”
And I find that some Asian-American engineers
are very quiet. And they expect their boss
to learn about, find out how great they are;
but it's really your job to let people know
the successes in the work and the contributions
that you have made.
AC: That's great. Thank you. Another question,
how do you see AI reshape the real estate
industry in the next decade?
KFL: I think real estate is probably one of
the not so early industries to be disrupted
by AI. I think certainly AI can be used as
a tool to estimate prices and to find the
right piece of land, and things like that.
It's not so fundamentally a part of AI. When
you think about an industry or on whether
it's a fit for AI, think about— do you have
a large amount of data? And does that data
connect to some very important objective function?
Something that you can learn and get better
over time. So, you know, when you think about
real estate data, you know, having a couple
of apartment complexes or a couple of malls,
that doesn't help anything. The only data
that's really large enough is data that's
available, you know, Zillow, or companies
like that. So using that to make projection,
I know one company that is basically has an
order for— let's say, they build a software
to build a modular building. And what they
would do is not only find that use AI to find
the most suitable and low price materials
to build a modular building; but also identify
a site that fits the module, considering the
price of real estate. So those are the kinds
of things where this can fit. But I'm not
sure anyone thinking about disrupting real
estate with AI will, will find good entrepreneurial
ideas at this time.
AC: Great. I have two more questions. I know
you have to leave sharply at your time, 9:30.
And the question is a scholar who's interested
in literary accounts of AI. He said right
now, there's a lot of writing about AI feeling
emotion. He recount— this person recalls
accounts about AI that can simulate love,
empathy, and he was wondering if this kind
of performance of love-empathy is a technical
technology that you see will continue to advance
in AI?
KFL: Yes, well, we should separate empathy,
emotion, love into three different buckets.
I think one, is being able to perceive it;
second is able to fake it; and the third is
to be able to feel it. Okay. So today, AI
has actually made leaps and bounds of progress
on perceiving it. In fact, I think I would
not be surprised if the use of facial expression
can— recognition and conversational agents
and recordings of videos becomes an important
part of forensic evidence. I think it can
be a more powerful tool than a lie detector.
Because the micro expressions that we– we
show our face when we feel emotion, whether
it is sincerity, or lying, or happiness, or
love, or sadness, or anger, are really captured
very, very well by AI, and better than most
people. Obviously, there are people who are
great at it, who are better than AI, but AI
can do that quite well. And that's kind of
one aspect. Obviously, some people will–
will be spooked by these kinds of applications,
but I'm just describing the capabilities of
reading people's faces and expressions and
feelings. That's quite advanced.
So obviously, if you can read it, you can
execute it. Right. So there will be good progress
in avatars that look like they have feeling.
Mo— when you see today animation that looks
like they have feeling— most of it is still
from motion capture, that is, putting sensors
on my face and having me say something with
emotion and having the avatar do the same.
But there is now beginning to be true synthesis
of emotion that appears relatively real. But
the problem is, you know, telling a 3D avatar,
say this with happiness or say this and then
cry, is not feeling. It's not true emotion,
not true feeling. It is really acting. Think
of AI as becoming better and better watchers
of emotion and actors of emotion. But there
is truly no feeling inside AI; it is simply
following the instructions of a person to
do something. And, and I have seen no evidence
that software code can have emotion at this
time. And I personally would like to think
that there's still something special about
our humanity and our souls. And that lies
in the way we feel. And that it may very well
be the case that AI will never capture that.
In fact, if AI someday truly captured that,
then I think we also need to question, what
is it that makes us special as humans? So
I think progress is being made but have, do
not be overly worried that robots with feelings
are coming out anytime soon.
AC: Thank you. So time is up. There's one
question from a gentleman of South Africa,
it's 3am there, and I think I just got the
number you now have 350,000 people watching
this particular talk. His question is a little
more involved, but perhaps we can send it
to you later. He said he's been writing to
you for years, and he's in Africa, hoping
you get advice for him to start a relationship
with VCs such as yours in China to partner
with the startups in the AI ecosystem in Africa;
and he would like some advice. Perhaps I should
just send you an email because we probably
can't explain all that right now?
KFL: Okay. I would be happy to respond to
his email. Thank you.
AC: Well, thank you for your talk. And we
are so appreciative, learn so much from you
today, from the heart. And so in appreciation,
the Liu Series has a little plaque, the marble
plaque with your name on it. We will mail
it to you after the talk. Thank you so much
again, we've learned so much, and thank you
for your time.
KFL: Thank you. Thanks, everybody.
AC: And before everyone else logs off, we
have a talk by Mr. Davy Wang of Tencent. He
is the Chief Solution Architect of Tencent,
and he will be talking at Lilie on November
4. And we will have the information online
for you.
Well, thank you, everybody, for watching.
I think we all learned a great deal. And,
and this concludes our talk. Thank you.
