This is an incredibly exciting
day for MIT and for the world
as we celebrate the launch
of the Stephen A. Schwarzman
College of Computing.
Our world is full of complex
and challenging problems.
And the widespread adoption
of computing technologies
has transformed
virtually every industry.
I believe that with dedication
and the right resources,
there is no problem
that's too hard to solve.
And we are only just
beginning to scratch
the surface of what
high-performance computing can
really do.
From health care to
engineering to the bleeding
edge of scientific research,
the best and brightest minds
across nearly every
field will rely
on high-performance computing
and artificial intelligence
to help make the
impossible possible.
This college places
MIT at the forefront
of AI and high-performance
computing research
and will develop the next
generation of great leaders
whose ideas will
change the world.
Congratulations,
and I look forward
to seeing graduates from the MIT
Schwarzman College of Computing
do incredible things.
[APPLAUSE]
At MIT, we're dedicated
to bringing knowledge
to bear on some of the
world's greatest challenges.
But in order to
scale our impact,
we must accelerate the time
from discovery to market.
Building on a myriad
of efforts at MIT
to spur innovation and
entrepreneurship, in October
of 2016, we launched The Engine
to help founders from MIT
and across the region
create the next generation
of world-changing companies,
or as Katie Rae says,
to bring disruptive
technologies from the lab
and into the light.
Since Katie joined
The Engine as CEO
and managing partner
just two years ago,
The Engine has already
invested in 16 companies,
working with some of the
toughest technologies,
including deep software
and AI, robotics,
and quantum computing.
And now, with the launch of
the MIT Stephen A. Schwarzman
College of Computing, we
have another opportunity
to revolutionize the scale of
impact enabled by computation
and AI across disciplines,
across technologies,
across society, across the
nation, and across the world.
Shortly, Kate is going
to lead a discussion
with our esteemed panelists.
But first, I have the privilege
of introducing a person that
needs no introduction.
Eric Schmidt has had
a distinguished career
at Google and its parent
company, Alphabet,
growing Google from a startup to
a global leader in technology.
Now a technical advisor
to Alphabet, Eric
advises its leaders on
technology, businesses,
and policy.
And he was recently tapped
to lead the National Security
Commission on
Artificial Intelligence
to advise the US government
on the national security
implications of AI
and how to maintain US
competitiveness in the field.
Over the past year, we
have had the great fortune
of having Eric here at MIT as
a visiting Innovation Fellow.
He has inspired MIT
scholars to take innovation
beyond invention, to address
global problems, play
the leadership role in advancing
conversations around human
and machine intelligence, and
the tremendous support that he
has given to launching
this college.
Eric.
[APPLAUSE]
I'd like to acknowledge
three sets of people.
Steve and Christine
Schwarzman, your generosity
is far greater than this
audience understands.
The gift that you have
given to create this college
has now ignited an explosion
of planning and philanthropy
that will be billions of
dollars from other donors
and other universities
to help establish
the age of intelligence.
It's an extraordinary
achievement.
[APPLAUSE]
And I mean it.
It's very, very rare that
you get a founder who
has that kind of leverage.
And we have them in
our audience right now.
Second person I'd
like to acknowledge
is President Rafael Reif, a good
personal friend of all of us.
The president years
ago sat down with me
and started talking
about the problem
that he saw of how the
university was structured
and how this new technology,
which he understood
would be transformative
in the many other parts
of the university
that he cared about.
He couldn't figure out
a way to get there.
So he and Provost Marty
Schmidt figured out
a way to create a college, which
you are hearing about all day
today, which works
within the culture
and norms of a university to
actually achieve something
which no one has
yet been able to do,
and that is to
aggressively diffuse
this new technology, which
I'll mention in a minute,
into fields which need it but
can't get it on their own.
And the final person
I'd like to acknowledge
is my very close friend,
Dan Huttenlocher,
one of the best hiring
decisions made by MIT
in a very, very long time.
As the new dean, Dan will bring
a level of professionalism
and sophistication.
And he knows how to
build organizations.
So you have everything
you need right now.
You have a strategy.
You have the funding.
You have the vision.
And you have a leader
to pull this off.
That's why this is such
an extraordinary moment.
The reason that we're
fundamentally here
is to talk that this new age
is far broader than people
appreciate and that the
technologies that we're
talking about here,
which started out
of computer vision--
computer vision is now
better than human vision--
appear to be able to
solve some very, very
longstanding problems that
have existed in society.
And what I want
you to do is I want
you to imagine a world where
each and every one of us
has an assistant.
As a child, you
have an assistant,
which is your teddy bear,
which helps you learn language.
As an adult, you
have an assistant
that allows you to figure
out what to do during the day
and figure out what your
choices are and help educate
you and make sure that you're
telling the truth and all
the other things that humans
run into in daily life.
And as an elderly person,
you'll have an assistant
who will help you with
your medical needs
and deal with loneliness
and keep you connected
to your friends and achieve
the things that you care about.
This model of an
assistant, which
is both a virtual assistant
as well as a robotic assistant
in different forms, is at the
basis of the vision of how
people--
all of us-- will
see a difference
in our lives every day.
And this story is profound.
So it's now clear, for
example, in medicine
that we can begin
to forecast events
based on your health care data.
And many companies,
including mine,
are working very hard to
take the data that exists
and put it all in a format
where machine learning can
go and take that
data and help solve
horrific diseases and
terrible problems people
find themselves in,
but more importantly,
help predict outcomes,
help you know
that you need to get yourself
to the hospital pretty quick,
that kind of a thing.
In the same way, we're
also seeing results
in distribution networks.
I'll give you an example.
Our company recently showed that
we can predict the wind turbine
electricity use in a clever way
using reinforcement learning.
And we're able to
essentially anticipate
the combination of
where the wind is
and where the demand is.
Now you say, oh, OK, that's OK.
There's lots of wind,
there's lots of demand.
It's a huge number, because
these distribution markets
don't make a lot of money.
So all of a sudden,
this technology
is the difference
between not being
able to fund these
projects with capital
and debt versus the ability
to fund them and grow
them and build real
businesses from them.
So on the margin, even
traditional businesses
benefit from the kind
of scalable analytics
that this technology provides.
So this brings us to
the panel right now.
And what we want to
talk about in this panel
is entrepreneurship.
Now, economists will
explain in great detail
that job growth
and economic growth
is not coming from
really big companies
and not coming from
really small companies.
It's coming from a relatively
young and fast growing
companies of all kinds, not
just tech companies, but others
as well.
We know that entrepreneurs drive
the economy, whether we like it
or not, of at least
the Western nations.
We know this to be true.
And what do you need to
have these companies?
You need entrepreneurs.
And it turns out, you
need some other things,
which I'll talk about.
Now, entrepreneurs come in
many, many different kinds
and phases.
You have the sort of
scientific founder,
the stereotype in my world.
But there are many,
many other people
who are sort of
fulfilled with a vision.
They have an idea.
They care about something.
And they personalize it.
They believe in it.
And they convince
others to follow them.
It's one of the most
fundamental human skills.
And that entrepreneurial
spirit, which
is that the basis of our success
as a country, needs more juice.
One of the things that's
interesting is that while
in the world I live in, it's
incredibly entrepreneurial--
there's all this
funding and so forth--
the total number of firms
that are entrepreneurial
is declining in America.
It's also declining elsewhere.
There's something
about the system that
makes it harder and
harder and so forth
for people to want to do this.
So it seems to me that
a job of the university
is to help create entrepreneurs.
Now, they're born, right?
They have this special skill.
They have a skill that I,
for example, don't have where
they can see it and
they know it and they
want to make it happen.
But the university can help
support them, educate them,
get them connected, get them
connected to their friends--
their friends [? as a set ?]
found the company and so forth.
MIT has been at the center
of this for a long time.
One of the things
that's interesting
about this new
world is that as we
enter the
entrepreneurial age, it's
going to be
important to remember
that if you have multiple
entrepreneurs and multiple sets
of data and multiple
platforms, they all
benefit by sharing data.
Now, you sit there
and anybody who's
been through a
legal review says,
well, we have to own this.
We have to own this.
We can't release
it and so forth.
But in fact, this data
gets better with sharing.
And so the platforms
that I and others
have built over the
entire aspect of my career
will now be augmented by
additional sources of data
that can be used by
entrepreneurs to solve
really hard problems.
So I mentioned all the
language and learning things.
Imagine if we can begin to
build knowledge libraries of how
people think, what they
do, what choices they have.
Imagine the contribution
that that would have
in this age of intelligence.
There are so many other
examples where you
can apply AI to solve problems.
There are people who
are, for example, using
artificial intelligence
to solve problems
that are computationally
intractable by doing
essentially AI estimates
for the hard bits.
And that technique will lead
to fundamental expansions
in material science, which
will again create companies,
fundamental expansions
of knowledge
in climate change
and climate science,
and so forth and so on.
MIT, for example, is a
leader in organic chemistry.
Organic chemistry
can be understood
as a relaxation problem
of getting these compounds
to merge together.
Every one of those is another
startup under the business,
another problem solved.
So we are at the beginning of
an explosion of essentially
innovation and discovery in
each of the key fields that
is needed to create
these companies.
Let me tell you that we
need more entrepreneurs.
MIT is at the forefront
of creating them.
There's a long history
in Cambridge-- you all
know this-- of doing this.
And we need more
and more and more.
The shortage of talent is being
addressed by the university
here.
The shortage of faculty
is being addressed
by the Schwarzmans' gift and
the creation of this college.
So we are on plan.
The problem is we're not
doing it fast enough.
So I want the vision
where medical care
is far more effective, far
cheaper, and far better.
I want the vision of material
science and new businesses
and services around that.
I want the ones in biology that
solve all sorts of diseases,
but more importantly
are new compounds,
new organic approaches to life.
I want the sustainability that's
implied by the climate change
work.
I want all of those
businesses to happen.
And what's beautiful
about it is that MIT
is at the forefront in
every single example
that I just gave you.
Thank you very much.
[APPLAUSE]
[INAUDIBLE]
I am.
Thank you.
[INAUDIBLE]
Thank you, Eric.
All right.
Well, welcome, everyone.
This is a panel on AI
in the marketplace.
So how does AI play
with entrepreneurship?
And we have the most incredible
people here with us today.
So I'm going to do a very
brief introduction of each one
and then jump right
in to the discussion
that we want to have.
So next to me is Helen Greiner.
And she is the
co-founder of iRobot.
She's a roboticist,
incredible thinker
about how robots will
affect the future,
and a multiple
time entrepreneur.
Next to her is Jim Breyer.
He is a renowned
venture capitalist.
And in Boston we
always lament that he
was the one that backed
Facebook, but has
done an incredible
amount of investing,
both in China and the US.
So we'll have very
interesting perspective.
Next to him is Bob Langer,
who's one of our gems here.
Entrepreneurship has
founded many, many companies
and made a huge impact in
different areas of health care.
So thanks for being here.
And then Jocelyn Goldfein,
who is at Zetta Ventures
and is a partner there and
works with entrepreneurs in AI.
And then certainly
last but not least
is Professor Tang, who is the
co-founder of SenseTime, which
I believe was Hong Kong's
first unicorn and a true leader
worldwide in AI.
If you didn't see him for
the Quest for Intelligence,
he's also absolutely hilarious.
So welcome.
But no pressure.
Yeah, no pressure.
So we're going to start
off with US versus China,
because how could we not?
It seems like we're kind of
in the middle of a space race
right now with China.
And I thought I'd
start with Jim,
who has invested on both
sides of this ocean into AI.
And my question to
you is, how do we
not make this something
that destroys one another,
but amplifies it?
And kind of what do you see
coming from both sides, the US
and China?
And how does this play out?
What are you excited about?
What are you scared of?
Well first, it's just
wonderful to be here at MIT.
And Steve and
Christine Schwarzman,
what a gift to students,
faculty for decades to come.
And Rafael is just
a great leader.
So I want to start
with that around MIT
as a local Bostonian.
China/US in two minutes.
I just returned.
Very proud to be a series
A investor in SenseTime.
Very proud to have done so much
around artificial intelligence
in the last two years.
If I had to categorize AI
and the state of AI today,
here's where I think the
US is still unparalleled,
as I speak from one of the
great universities of the world.
Our top universities
are turning out
the very best, brightest,
most creative technical
and philosophical leaders.
And many of the
best AI companies,
particularly in health care
that Breyer capital is backing
is an interdisciplinary
group of people,
whether it's biology, physics,
electrical engineering,
computing, ethics.
They're all coming together in
these very interesting startups
that are working with hospitals,
academic centers in very
interdisciplinary ways.
That is happening in the US in
profound areas of health care,
in particular cancer,
AI, cardiology,
that is not occurring at
that level at Tsinghua
or the very best
Chinese universities.
So in a nutshell, my view is
for deep domain-specific AI,
particularly in medical and
health care; that diversity,
female leaders,
scientists, biologists,
machine learning come
together in the US
in more profound ways than
anywhere else in the world.
And I don't expect
that to discontinue.
[SPEAKING CHINESE]
OK, excellent.
So I'm going to move
to Bob Langer next,
because he is steeped in
how technology impacts
many areas of health care.
So I thought we'd
just jump to Bob
and say, what are you
excited about in terms
of what the impact of AI
can have very specifically
in the medical and
health care field?
Well, I think there is
a tremendous opportunity
to do an awful lot of things
in medicine and health care.
I mean, just basically
because you can hopefully
get so much more information.
I mean, if you just look at
diseases now, most of what
we do, usually
there's a single test,
like say you could
go into the doctor
and get your cholesterol tested.
And that gives
you an indication,
say, for heart disease.
It's not perfect, but
it's an indicator.
But let's say you'd want to
take a disease like cancer.
I mean, you could--
which I think certainly
we don't really
know how to do early diagnosis.
But could you, for example, take
blood samples, urine samples,
and you can get something
like transcriptomes.
That's just one of
many things, or you
could get a protein
profile, in which case
you'd have enormous
amounts of information.
You could sort of get what I
call a fingerprint, in a sense.
And then you could actually
try to use AI to, for example,
analyze those fingerprints,
decide what type of fingerprint
gives somebody is at risk for
cancer, what person is not.
And then you could also do
drug testing that way, too.
You could see maybe you'll have
a fingerprint, for example,
that as you look at
it, that shows you
that that drug's going to treat
that cancer for that person.
Similarly, I think there's a
lot of other opportunities.
I mean, we do a lot of
chemistry in the lab.
And one of the big challenges
today in nanotechnology
is we've developed
nanoparticles that can deliver,
I think, some of the
drugs of the future,
like messenger RNA
and DNA, siRNA.
Right now, there's ways to
target them to certain places
like the liver, but
very difficult to do it
for other places.
But what we've been
able to do is literally
design thousands and thousands
of chemical structures.
And we're talking to
some of the people at MIT
and the AI area about how you
could find out which structures
are going to be most effective.
Let's say you do round one.
You do thousands.
And you see that
some work pretty well
at targeting certain
types of cell types
that then hopefully
will allow you
to start predicting what ones
to synthesize for round two
and so forth.
So I think there's
just an enorm--
and of course,
the upshot of that
is that you could someday have
much, much better therapies
for almost anything.
So I think there's just enormous
opportunities for the future.
OK, I'm going to come back
to health care in a minute.
I'm going to skip all the
way down to Professor Tang
and then back to
Jocelyn, back to Helen.
Professor Tang, you both
teach and start companies.
Part of your education was here.
But you are deeply steeped
in the China market.
If you were giving advice
to the future entrepreneurs
both in the US and China
about how to create a winning
company, what
advantages would you
tell them to draw upon
here versus China?
Well, thank you.
Well, since the tool
I have in my hand
is really like a hammer,
so I approach everything
as if it's a nail, right?
So my advice to
the company, if you
start here, I think the
first thing I want to advise
is you start with the
collaboration with SenseTime.
[LAUGHTER]
That was not a joke.
[LAUGHTER]
So just think about it.
In the US, you almost
have everything.
You have the technology,
the university,
and the rule of law
and the venture capital
and all the big company who
want to buy you when you grow.
So really everything's set up.
You have a pipeline.
But at the same time, everybody
here who start a company
have the same access
to that pipeline.
So you are just
one of everybody.
So to have some
distinct advantage,
I think it makes sense
to actually collaborate
with a company in China,
Hong Kong or anywhere
in other part of the
world, because China
can offer something different.
They have market.
They have people who
are willing to work 24
hours for much less the salary.
So anyway--
Today, today.
Anyway, if you
combine the advantage,
then it put you in a
different position.
So I think for this topic,
I have some comments.
I think it should not be
really China versus the US.
It really should be
China and the US.
We should really
collaborate, work together.
AI is really a perfect tool to
break boundary-- break boundary
between countries, between
academic and the industry,
between different industry.
So we should really
take advantage of that.
And also every country
should approach
this in a different way.
If we all approach this from
the same way like in China,
is probably the government
played more roles
to work with a company.
In the US, it's
more market driven.
I think it's good.
In China, it's like you
are raising a farmland.
So you have higher production,
but it's just [INAUDIBLE]..
But in the US, it's
like a rain forest.
So everything can happen.
So you have something very
unique come out of it.
So they both have an
advantage and a disadvantage.
So the key is really
to collaborate.
Thank you.
Thank you.
Jocelyn, you invest
in Silicon Valley.
I do.
And I'm sure more
broadly than that.
But when you think about
what is coming out of there
and how you coach your
entrepreneurs to succeed,
how do you think about AI and
succeeding as a business coming
out of the valley?
Well, at Zetta we were
defined by three things that
were the first stage of
institutional capital
that goes into a company.
So I'm working with
very early companies.
We're, I would say, the
invention capital, not
the growth capital.
We're defined by
seeking companies
that invest in
AI, that are built
around AI and around competitive
advantages from data.
Zetta stands for zettabyte,
which this crowd would know
is a trillion gigabytes.
And thirdly, we invest only
in startups with B2B business
models.
So I am very focused on
finding entrepreneurs
who want to transform the
world of business with AI.
And I think it's no surprise
that a technology that
is novel, that is cutting edge,
and that is potentially risky
really got its
start in consumer.
My introduction to AI
was working at Facebook.
And my first big project there
was adopting machine learning
for the newsfeed's rancor.
And it makes sense that
the first places where
AI could prove itself
would be things like movie
recommendations or deciding
whether to show you
the cat photo or the baby photo,
decisions where if we got it
right everybody was better
off, and if we got it wrong--
well, at the time,
we didn't think
that the order of
your news feed had
civilization-shaking
consequences.
Now that we move
into business, we
are trying to
solve problems that
are more mission critical
where companies are betting
their bottom line on it.
And initially we've talked about
this concept of the AI risk
curve, which dictates that
AI will be adopted not just
as the technology is ready
to solve the problem,
but also as human beings and
companies are ready to accept
the consequences, the social and
business consequences of using
technology for this problem.
So we think that
it's natural then
that in business
at first companies
were willing to adopt AI as
an assistant to a human being,
to make recommendations
to help human beings,
maybe to help
sales reps optimize
how they were calling on leads.
The next stage is
to trust AI so much
that it can automate
fully a task
and without human oversight.
And that, I believe, is
the area that we're in now.
But what is coming and
what really has me excited
is a world in which we can start
turning AI loose on problems
that are too hard for
human beings, problems
like human health, problems
like the health of the planet.
And I want to chime in and
sort of echo something Xiao'ou
said about
collaboration, which is,
I don't know if space race
is the right analogy here.
I don't want the US to have the
best health care in the world.
I want the whole world
to have the best health
care in the world.
I want the whole world to have
access to the best technology
to solve our climate problems,
because that's only a problem
that can be solved globally.
I want the whole world to
have access to the best ways
to design for smart cities.
And I think reasonable
people can disagree on
whether the whole
world ought to have,
for example, the best
military applications of AI.
So there's places maybe
where nation states
need to guard or
protect their secrets.
But I would say that I
am excited about startups
that have a global aim and
intend to have global impact.
Nice.
OK, so now you
brought up the issue
of potentially negative
impacts of AI and robotics.
I mean, I think there's a
general feeling in the public
that these things are scary.
They're going to take our jobs.
They're going to
change the world
in ways that are going
to drive more inequality.
So Helen, not to put you on
the spot, but as the roboticist
here, I thought this would
be a fun one for you--
[INAUDIBLE]
--which is if we're driving
more and more inequality because
of AI, because the assistant
becomes the worker,
what should we do?
Are we going to tax robots?
Are we going to--
how should we fix this?
And what's the role of
the entrepreneur in that?
We can talk about the negative.
But let's just touch
on the positive.
Having a little robot do
your vacuuming actually
save you time so you
can do other things.
And people have plenty
of other things to do.
Having a cell phone in
a third world country
allows people to communicate
or know market data.
People would not give up these
technological inventions.
And as they get
more, I think it's
hard to predict exactly
what the ramifications are.
We're at pretty low
unemployment right now,
in this country at least.
And I just see so
many wonderful areas
that are going to be pushed.
I mean, electric
cars are coming.
Like, huge industries
are forming--
automatic cars, drone delivery,
roboticizing their house,
making it care for you.
There's so many
areas that are really
changing the infrastructure
of everything.
And those will provide
jobs to people.
And then as a society, we can
choose what else we want to do.
Maybe there's more teachers.
Maybe there's more
exploration of space.
I think we have to make
those positive decisions.
The environment was mentioned.
We have to make those decisions.
I'm for the four-day
work week myself,
not for people who work
for me, but everyone else,
because people
couldn't have imagined
earlier on that people would
only work 40 hours a week.
It's like, you work
from sunup to sundown.
You maybe have Sundays off.
Then you have to
go and socialize
and go to church and stuff.
But I think we will
morph as society.
And that's where policy
and AI and robotics
have to get together,
because I think
we can do things that makes
everybody's lives improve.
Anybody else want to--
Oh, and we shouldn't
tax the robots.
Oh, don't tax the robots?
[INAUDIBLE] Taxing productivity
is a really, really bad idea,
because you have to have your
company be more successful
and create jobs.
If you don't do that, someone
else potentially from China
is going to eat your lunch.
Jim, so I do think these policy
questions around privacy--
and I'm going to ask all of
you to weigh in on this one.
These questions around
convenience versus privacy
are really tricky questions.
And I think if we go
back to China versus US,
we've made really different
decisions about that
and partly because China can
make decisions with a very
centralized government.
And the US will have a
very hard time legislating
almost anything, as we've
seen over the last few years.
And so my question
for you is, how do you
think about the evolution
of that tricky problem?
In two minutes or less.
Yeah.
I mean--
I would just offer,
there are certain areas
where the privacy is so
important that, again, I'm
a very firm believer, if we just
focus on health care and AI,
I think we're one or two years
into a 10-year wondrous journey
on how AI, biology,
computational science, many
of the professors and postdocs
and students represented
in this room are going to create
huge, beneficial, wondrous
outcomes to improve
cancer diagnosis,
help doctors come
to better decisions.
The challenge is so
often in the press
or in discussions or in events
in Cambridge or Palo Alto,
it's an either/or.
Will AI replace the doctors?
Absolutely not.
The doctors' decision making,
if it's computational pathology,
rather than wait
10 days for slides
to come back or
breast cancer imaging,
improving the efficiency and
then improving the accuracy
so doctors can do better
work for patients.
That is what the promise, if
we take AI in health care,
is so powerful.
Now with that, we just
have to be really stringent
around privacy laws, security.
And I think the US will
continue to have an advantage
relative to that, because for
the breakthrough outcomes led
by the women and men in this
audience, you need the privacy.
You need the security.
But it is wondrous when
I see the great hospitals
and universities working
together across departments
on interdisciplinary, wondrous
outcomes in cancer, cardiology,
and other areas where AI
serves as a foundation.
So Bob, you are a
professor at MIT.
And you've trained so
many future entrepreneurs
and will continue to.
When you think about how you--
and academia is open.
Academics collaborate worldwide.
When you think about
teaching this next generation
both entrepreneurship
and collaboration,
how do you think
that works with AI?
Well, actually I
think you want to--
I mean, we publish
everything we do.
I mean, we patent it, too.
But we publish everything we do.
And I think that we want to
get knowledge out to the world.
And I think that my experience
in most areas of technology,
or at least biotechnology, is
that publications are good,
that getting it out is actually
an additional validation.
But I would say that I think
that actually both of what
you said I think are right.
I think you want to do things
that are good for the world.
And I think what you said
about medicine is exactly--
that's the kind of thing
I was trying to get across
when Katie asked me earlier.
So I think that you
want to do those things.
You want to get technology out.
You want to learn things.
But what I see sometimes--
and I think this is an important
thing about entrepreneurism.
So many times that when
you come up with an idea,
let's say you came
up with an idea
in the AI area or some other
area, in my experience,
a lot of times people are
going to tell you it's
a terrible idea.
They won't give you any money.
And so I think the most
important thing to me
when I talk to
people in the lab is
we try to come up
with ideas that we
think will change the world.
You should recognize
that you're going
to get a lot of criticism.
And a lot of times
people are going
to tell you it won't work.
And I say keep trying.
Keep going after it.
And so if you don't do it, a
lot of times nobody else will.
And I think that's
the opportunity
for a great entrepreneur.
Awesome.
OK, so now we are going
to go down the line.
And we'll start at this
end with Professor Tang.
So AI is going to be
applied, I believe,
to almost every industry.
When you think of the
next big things coming,
what is the most
exciting space for you
that AI will be applied
to besides SenseTime,
besides [INAUDIBLE]?
[LAUGHTER]
I think AI is pretty much
going to be applied to anywhere
you have data.
And in this digital
world, it's really hard
to find somewhere that
doesn't have data.
So it's probably going
to be everywhere.
At the same pace?
Or do you think
something's going to lead?
It's certainly a different pace.
And also I think it's not really
to find a new area to apply it.
We have so many areas.
We just only touched the
surface at this point.
You may hear a lot about
medical applications,
autonomous driving,
fintech, all this thing.
But if you go down to
it, it's really hard
to find any company who
has made a lot of money
from those areas.
Even for Google, they haven't
started making money on that,
because it's really difficult,
because you need to really work
with the industry.
And the industry is
the people who have
worked on it for 100 years.
So they have a lot of
[? domain ?] knowledge.
And people from AI,
from computer science,
they just know algorithms.
So it's really the two
sides need to work together
to get things done.
And just give you an example.
We are working with
a casino company.
So I'm very confident.
We have these cameras and
all these things with that.
We can track all the
people on the blacklist
will come in to try to cheat
on you and all those things.
And they said, we don't care.
Nobody can cheat on us.
The house always wins.
The movies-- Ocean
Eleven, Ocean Twelve--
those just story.
What we care is
our own employee.
We have 100,000 of employees.
And when they are
dealing the cards,
they can really cheat us.
So those are our biggest loss.
Now, how can you
monitor those things?
So you don't know the
need of the industry.
So for automatic
driving, it's also
you need to work
with a car company.
They can teach you a lot.
But this is really
difficult. It's kind of hard.
So I think for the
next step is not
to have all this big
stories of new breakthrough.
It's just how we get
down to the business
to actually make money.
If we cannot make money,
we cannot survive.
I think every technology will
have its 15 minutes of fame.
And for AI, we have
to think about how
to get through the 15 years of
fame to really get it working.
And I really think MIT
did the right thing.
They said that they wanted to
start a college focusing on AI.
But it turns out it's
a college of computing.
But in China, we have a lot
of college [INAUDIBLE] AI.
I think someday AI
[INAUDIBLE] bubble.
But really it's computing, it's
innovation, the technology.
That will survive.
Great.
Thank you.
Jocelyn, what are you excited
about that's going to pop next?
I think we're so much
in the early innings.
I think we're in the command
line days of the internet.
And someday 10, 20
years from now, people
will look back and think
how primitive we were.
I think what I am most
excited about is building
the structures and systems
that enable us to do this well
and to do it across the board.
I think the Schwarzman
College of Computing
is an excellent first
start, this idea
of educating AI bilinguals.
I am excited about,
and I think there's
a huge role for academic
institutions for MIT, Tsinghua,
Stanford and other places
like that to step forward
in terms of creating a code
of ethics, a code of standards
for the types of work that
we're doing around data and data
privacy.
I think those will be
fundamental building
blocks actually to enable
the kinds of innovation
we want to see.
And I look forward, frankly,
to more MIT grads and more
scientists, engineers,
and mathematicians
actually to go
into policy making
and to go into
politics so that we
can have policies
and regulations that
create structure
for what we're doing
that are informed by people
who deeply understand.
And I think those are all
sort of necessary precursors
to enable the world
we all want to live in
and that we want our kids
and grandkids to live in.
And I guess I
would ask you, Bob,
just slightly
differently, when you
look at where your students
are applying AI, like what do
you think is going to pop next?
Well, I think it's
what I mentioned and I
think Jim mentioned, too.
I think therapeutics and
diagnostics, I think,
are giant opportunities.
Jim, anything besides
health care that--
There's a lot, of course.
So much of what we all
need to do as investors
is go very deep
in certain areas.
And I think the big challenge
is the interdisciplinary nature
of bringing chemists,
biologists, computation--
I could go on and on-- together
in small entrepreneurial teams
that are functioning
extraordinarily well
and having diversity
of women, men,
underrepresented
minorities all at the table
as we're building these
next generation companies.
But my goodness, to be an
entrepreneur or postdoc as part
of an entrepreneurial
venture with affiliations
at MIT and elsewhere--
what a phenomenal
time, I believe,
to be a technology investor
as well as an entrepreneur.
And Helen, you get
the last word here.
You already know how
I'm going to answer.
Well, I wonder
how you'll answer.
Combining the computation with
the physical, things that think
or robots, because--
Maybe more specifically, though.
What are you excited
that you're seeing
where robots will be applied?
Even where they're at
today with autonomous cars,
unthinkable in the
[? '90s. ?] In fact,
[INAUDIBLE] we chose not to do.
We said, oh, they'll
never let us on the road.
They'll never let us get
insurance and stuff like that.
Package delivery, logistics
in general-- the whole train
is being automated with
robots doing warehousing
and fulfillment, et cetera.
It will be trucks.
And getting the robots
into the physical world,
getting that
computation from just
being on desktops
and in the cloud
to in the physical world--
I find that the most exciting,
because at the end of the day,
they kind of come to life.
That's amazing.
Well, I want to thank the entire
panel for their comments today
and for being here.
Appreciate it.
[APPLAUSE]
