>> With the rise of technology often
comes greater concentration of
power in smaller numbers of people's hands,
and I think that this creates
greater risk of ever-growing wealth
inequality as well.
To be really candid, I think that with
the rise of the last few ways of technology,
we actually did a great job
creating wealth in the East and the West Coast,
but we actually did leave large parts
of the country behind,
and I would love for this next one
to bring everyone along with us.
>> Hi everyone. Welcome to Behind the Tech.
I'm your host, Kevin Scott,
Chief Technology Officer for Microsoft.
In this podcast, we're going to get behind the tech.
We'll talk with some of
the people who made our modern tech world
possible and understand what
motivated them to create what they did.
So, join me to
maybe learn a little bit about the history of
computing and get a few behind the scenes insights
into what's happening today.
Stick around.
Today I'm joined by my colleague Christina Warren.
Christina is a Senior Cloud Developer Advocate
at Microsoft. Welcome back Christina.
>> Happy to be here Kevin,
and super excited about
who you're going to be talking to today.
>> Yeah. Today's guest is Andrew Ng.
>> Andrew is, I don't think this is too much to say,
he's one of the preeminent minds
in artificial intelligence and machine learning.
I've been following his work since
the Google Brain Project,
and he co-founded Coursera,
and he's done so many important things and
so much important research on AI and that's
a topic that I'm really obsessed with right now.
So, I can't wait to hear what you guys talk about.
>> Yeah. In addition to his track record as
an entrepreneur, so Landing.AI, Coursera,
being one of the co-leads of
the Google Brain Project in its very earliest days,
he also has this incredible track record
as academic researcher.
He has a hundred plus really fantastically good papers
on a whole variety of topics in artificial intelligence,
which I'm guessing are on
the many a PHD student's reading list
for the folks who are trying to get
degrees in this area now.
>> I can't wait. I'm really
looking forward to the conversation.
>> Great. Christina, we'll
check back with you after the interview.
Coming up next, Andrew Ng.
Andrew is founder and CEO of Landing.AI.
Founding lead of the Google Brain Project
and co-founder of Coursera.
Andrew is one of the most influential leaders
in AI and deep learning.
He's also a Stanford University
Computer Science adjunct professor.
Andrew, thanks for being here.
>> Thanks a lot for having me Kevin.
>> So, let's go all the way back to the beginning.
So, you grew up in Asia?
And I'm just sort of curious when was it that you
realized you were really
interested in math and computer science?
>> I was born in London,
but grew up mostly in Hong Kong and Singapore.
I think I started coding when I was six-years-old.
And my father had a few very old computers.
The one I used the most was some old Atari,
where I remember there were these books
where you would read the code in a book and
just type in a computer and then you had
these computer games you could play
that you just implemented yourself.
So, I thought that was wonderful.
>> Yeah, and so that was probably the Atari 400 or 800?
>> Yeah. Atari 800 sounds right.
It was definitely some Atari.
>> That's awesome. And what sorts of
games were you most interested in?
>> You know, the one that fascinated me
the most was a number guessing game.
Where you, the human, would think
of a number from 1 to 100,
then the computer would basically do
binary search but chooses: Is it higher or lower than 50?
Is it higher or lower than 75 and so on,
until it guesses the right number.
>> Well, in a weird way,
that's like early statistical Machine Learning, right?
>> Yeah, and then, so at six-years-old
it was just fascinating that the computer could guess.
>> Yeah. So, from
six years- did you go to
a science and technology high school?
Did you take computer science classes
when you were a kid or...?
>> I went to good schools: St. Paul's in
Hong Kong and then ACPS in the Raffles in Singapore.
I was lucky to go to good schools.
I was fortunate to have grown up in
countries with great educational systems.
Great teachers, they made us work really hard but also
gave us lots of opportunities to explore.
And I feel like, computer science is not magic.
You and I do this, we know this.
While I'm very excited about
the work I get to do in computer science and AI,
I actually feel like anyone could do what I'd do if they
put in a bit of time to learn to do these things as well.
Having good teachers helps a lot.
>> We chatted in our last episode with Alice Steinglass,
who's the president of Code.org,
and they are spending
the sum total of their energy trying to
get K-12 students interested in
computer science and pursuing careers in STEM.
You're also an educator.
You are a tenured professor at Stanford and
spent a good chunk of your life in academia.
What things would you encourage students to think
about if they are considering a career in computing?
>> I'm a huge admirer of Code.org.
I think what they're doing is great.
Once upon a time, society used to
wonder if everyone needed to be literate.
Maybe all we needed was for
a few monks to read the Bible to us and we didn't
need to learn to read and write ourselves because
we'd just go and listen to the priest or the monks.
But we found that when a lot of us learned to read and
write that really improved human-to-human communication.
I think that in the future,
every person needs to be computer
literate at the level of being able to
write these simple programs.
Because computers are becoming so
important in our world and coding
is the deepest way for
people and machines to communicate.
There's such a scarcity of
computer programmers today that
most computer programmers end up writing
software for thousands of millions of people.
But in the future if everyone knows how to code,
I would love for the proprietors of
a small mom and pop store at a corner to
go program an LCD display
to better advertise their weekly sales.
So, I think just as literacy,
we found it having everyone being able to
read and right, improved human-to-human communication.
I actually think everyone in the future
should learn to code because that's
how we get people and
the computers to communicate at the deepest levels.
>> I think that's a really great segue
into the main topic
that I wanted to chat about today, AI,
because I think even you have used
this anecdote that AI is going to be like electricity.
>> I think I came up with that.
>> Yeah. I know this is your brilliant quote
and it's spot on.
The push to literacy in many ways is
a byproduct of
the second and third industrial revolution.
We had this transformed society
where you actually had to be literate in
order to function in this quickly industrializing world.
So, I wonder how many analogues you
see between the last industrial revolution
and what's happening with AI right now.
>> Yeah.
The last industrial revolution
changed so much human labor.
I think one of the biggest differences
between the last one and this one
is that this one will happen faster,
because the world is so much more connected today.
So, wherever you are in the world, listening to this,
there's a good chance that there's a AI algorithm
that's not yet even been invented as of today,
but that will probably
affect your life five years from now.
A research university in
Singapore could come up with something next week,
and then it will make its way to
the United States in a month.
And another year after that,
it'll in be in products that affect our lives.
So, the world is connected in a way that
just wasn't true at the last industrial revolution.
And I think the pace and speed will bring challenges
to individuals and companies and corporations.
But our ability to drive
tremendous value for AI, for the new ideas,
the tremendous driver for global GDP growth
I think is also maybe
even faster and greater than before.
>> Yeah. So, let's dig in to that a little bit more.
So, you've been doing
AI Machine Learning for a really long time now.
When did you decide that that's
the thing you were going to specialize
on as a computer scientist?
>> So, when I was in high school in Singapore,
my father who is
a doctor was trying to implement AI systems.
Back then, he was actually using XP systems,
which turned out not to be that good a technology.
He was implementing AI systems of
his day to try to diagnose, I think lymphoma.
>> This is in the late 80's.
>> I think I was 15 years old at that time.
So, yeah, late 80's.
So, I was very fortunate to learn from
my father about XP Systems
and also about neural networks,
because they had day in the sun back then.
That later became an internship at
the National University of Singapore
where I wrote my first research paper actually,
and I found a copy of it recently.
When I read it back now,
I think it was a very embarrassing research paper.
But we didn't know any better back then.
And I've actually been doing AI,
computer science and AI pretty much since then.
>> Well, I look at your CV and
the papers that you've
written over the course of your career.
It's like you really had your hands
in a little bit of everything.
There was this inverse
reinforcement learning work that you
did and published the first paper in 2000.
Then, you were doing some work
on what looks like information retrieval,
document representations, and what not.
By 2007, you were doing
this interesting stuff on self-taught learning.
So, transfer learning from unlabeled data.
Then, you wrote the paper in 2009 on
this large scale unsupervised learning
using graphical processing.
So, just in this 10-year period in your own research,
you covered so many things.
In 2009, we hadn't even really
hit the curve yet on deep learning,
the ImageNet result from Hinton hadn't happened yet.
How do you, as one of the principles,
you help create the feel,
what does the rate of progress feel like to you?
Because I think this is one of the things that people
get perhaps a little bit over excited about sometimes.
>> One of the things I've learned in my career
is that you
have to do things before they're obvious to everyone,
if you want to make a difference
and get the best results.
So, I think I was fortunate back in maybe 2007 or so,
to see the early signs
that deep learning was going to take off.
So, with that conviction,
decided to go on and do it,
and that turned out to work well.
Even when I went to Google to
start the Google Brain project, at that time,
neural networks was a bad word to
many people and there was a lot of initial skepticism.
But, fortunately,
Larry Page was supportive and then started Google Brain.
And I think when we started Coursera,
online education was not an obvious thing to do.
There were other previous efforts,
massive efforts that failed.
But because we saw signs that we could make it
work with the conviction to go in.
When I took on the role at Baidu at that time,
a lot people in the US were asking me, "Hey,
Andrew, why on earth would you want to do AI in China.
What AI is there in China?"
I think, again, I was fortunate
that I was part of something big.
Even today, I think landing.ai
where I'm spending a lot of my time,
people initially ask me, "AI for
manufacturing? Or AI for
agriculture? Or try to transfer calls using AI?
that's a weird thing to do."
I do find people actually catch on faster.
So, I find that as I get older,
the speed at which people go from being really
skeptical about what I do
versus to saying, "Oh, maybe that's a good idea."
That window is becoming much shorter.
>> Is that because the community is maturing or
because you've got such an incredible track record that...
>> I don't know. I think everyone's getting
smarter all around the world. So, yeah.
>> As you look at how machine learning has
changed over the past just 20 years,
what's the most remarkable thing from your perspective?
>> I think a lot of recent progress
was driven by computational scale,
scale of data, and then also by algorithmic innovation.
But, I think it's really interesting when something
grows exponentially, people, the insiders,
every year you say, "Oh yeah,
it works 50 percent better
than the year before." And every year it's like,
"Hey, another 50 percent year-on-year progress."
So, to a lot of machine learning insiders,
it doesn't feel that magical.
It's, "Yeah, you just get up and
you work on it, and it works better."
To people that didn't grow up in machine learning,
exponential growth often feels
like it came out of nowhere.
So, I've seen this in
multiple industries with the rise of the movement,
with the rise of machine learning and deep learning.
I feel like a lot of the insiders feel like, "Yeah,
we're at 50 percent or some percent better than last
year," but it's really
the people that weren't insiders that feel like,
"Wow, this came out of nowhere.
Where did this come from?"
So, that's been interesting to observe.
But one thing you and I have chatted about before,
there's a lot of hype about AI.
And I think that what happened with the earlier AI winters is
that there was a lot of hype about AI that
turned out not to be that useful or valuable.
But one thing that's really different today is
that large companies like Microsoft,
Baidu, Google, Facebook, and so on,
are driving tremendous amounts of revenue as well as
user value through modern machine learning tools.
And that very strong economic support,
I think machine learning is making a difference to GDP.
That strong economic support
means we're not in for another AI winter.
Having said that, there is a lot of hype about
AGI, Artificial General Intelligence.
This really over hyped fear of evil killer robots,
AI can do everything a human can do.
I would actually welcome a reset
of expectations around that.
Hopefully we can reset
expectations around AGI to be more realistic,
without throwing out baby with the bath water.
If you look at today's world,
there are a lot more people working on
valuable deep learning projects
today than six months ago,
and six months ago, there were a lot more people
doing this than six months before that.
So, if you look at it in terms of the number
of people, number of projects,
amount of value being created,
it's all going up.
It's just that some of the hype and
unrealistic expectations about, "Hey,
maybe we'll have evil killer robots
in two years or 10 years,
and we should defend against it."
I think that expectation should be reset.
>> Yeah. I think you're spot on
about the inside versus outside perspective.
The first machine learning stuff that I did was
15 years-ish ago when
I was building classifiers for
content for Google's Ad systems.
Eventually, my teams worked on some of
the CTR predictions stuff for the ads auction.
It was always amazing to me how simple an algorithm you
could get by with if you had
lots of compute and lots of data.
You had these trends that were driving things.
So, Moore's Law and things that we were
doing in cloud computing was making
exponentially more compute available
for solving machine learning problems
like the stuff that you did,
leveraging the embarrassingly parallelism
in some of these problems and solving them on GPUs,
which are really great at
doing the idiosyncratic type of compute.
So, that computer is one exponential trend,
and then the amount of available data for
training is this other thing,
where it's just coming in at this crushing rate.
You were at the Microsoft
CEO Summit this year and you gave
this beautiful explanation where you said,
"Supervised Machine Learning is
basically learning from data,
a black box that takes one set
of inputs and produces another set of outputs.
And the inputs might be an image and the outputs
might be text labels for the objects in the image.
It might be a waveform coming in that has
human speech in it and the output might be the speech."
But really, that's sort of at the core of
this gigantic explosion of
work and energy that we've got right now,
and AGI is a little bit different from that.
>>Yes, in fact to give credit where it's due.
You know actually many years ago,
I did an internship at
Microsoft Research back when I was still in school.
Even back then, I think it was
Eric Brill and Michele Vanko
at Microsoft way back had already published a paper
using simple algorithms, that basically
it wasn't who has the best algorithm that wins,
it was who has the most data for
the application they were looking at at NLP.
And so I think that the continuation of that trend,
that people like Eric and Michelle had
spotted a long time ago,
that's driving a lot of the progress
in modern machine learning still.
>> Yeah. Sometimes, with AI Research
you get these really unexpected results.
One of those I remember it was
the famous Google CAT result from the Google Brain Team.
>> Yes, actually, those are interesting projects,
while still a full time at Stanford,
my students at the time Adam Coates and others,
started to spot trends that,
basically the bigger you build in
your neural networks, the better they work.
So that was a rough conclusion.
So I started to look around Silicon Valley to see
where can I get a lot of
computers to train really really big neural networks.
And I think in hindsight,
back then a lot of us leaders of
deep learning had
a much stronger emphasis on unsupervised learning,
so learning without label data, such
as getting computers to look a lot of pictures,
or watch a lot YouTube videos without telling
it what every frame or what every object is.
So I had friends at Google so I wound up pitching to Google
to start a project which
we later called the Google Brain Project,
to really scale up neural networks.
We started off using Google's Cloud,
the CPU's and in hindsight,
I wish we had tried to build up
GPU capabilities like Google sooner,
but for complicated reasons,
that took a long time to do which is why I wound
up doing that at Stanford rather than at Google first.
And I was really fortunate to have
recruited a great team to work
with me on the Google Brain Project.
I think one of the best things I did was
convince Jeff Dean to come and work.
And in fact, I remember the early days,
we were actually nervous about whether
Jeff Dean would remain interested in the project.
So a bunch of us actually
had conversations to strategize,
"Boy, can we make sure to keep Jeff Dean engaged
so that he doesn't lose interest and go do something else?"
So thankfully he stayed.
The Google CAT thing was led by my,
at the time PhD student Quoc Le
put together with Jiquan Ngiam,
were the first two sort of
machine learning interns that
I brought into the Google Brain Team.
And I still remember when
Quoc had trained us on unsupervised learning algorithms,
it was almost a joke, you know I was like, "Hey!
there are a lot of cats on YouTube,
let's see this learning cat detector."
And I still remember when Quoc
told me to walk over and say,
"Hey Andrew, look at this." And I said, "Oh wow!
You had unsupervised learning algorithm
watch YouTube videos and learn
the concept of 'cat.' That's amazing."
So that winds up being an influential piece of work,
because it was unsupervised learning,
learning from tons of data for
an algorithm to discover concepts by itself.
I think a lot of us actually
overestimated the early impact of unsupervised learning.
But again, when I was leading Google Brain Team,
one of our first partners was
the speech team working
with Vincent Vanhoucke, a great guy,
and I was really working with Vincent and his team,
and seeing some of the other things
happening at Google and outside that caused a lot
of us to realize that there was
much greater short term impact to
be had with supervised learning.
And then for better or worse,
when lot of deep learning communities saw this,
so many of us shifted so much
of our efforts to supervised learning,
that maybe we're under resourcing
the basic research we still
need unsupervised learning these days
which maybe, you know,
I think unsupervised learning is
super important that there's
so much value to be made with supervised learning.
So much of the attention is there right now. And I think,
really what happened with
the Google Brain Project
was- were the first couple of successes,
one being the Speech Project
that we worked with the speech team on.
What happened was other teams saw
the great results that
the speech team was getting with deep learning with our help.
And so, more and more
of the speech team's peers ranging from
Google Maps to other teams
started to become friends and
allies of the Google Brain Team.
We started doing more and more projects.
And then the other story is after,
you know, the team had tons of momentum,
thank god, we managed to
convince Jeff Dean to stick with the project,
because one of the things that gave
me a lot of comfort when I wanted
to step away from a day-to-day
role to spend more time in Coursera was,
I was able to hand over
leadership of the team to Jeff Dean.
And that gave me a lot of comfort that I
was leaving the team in great hands.
>> I sort of wonder, if there's
a sort of a message or a takeaway
for AI researchers in
both academia and industry about the Jeff Dean example.
So for those who don't know,
Jeff Dean might be the best engineer in the world.
>> It might be true. Yes.
>> But I've certainly never worked
with anyone quite as good as him.
I mean, I remember there was this-
>> He's in a league of his own. Jeff Dean is definitely-
>> I remember back in long,
long ago at Google.
This must have been 2004 or 2005,
right after we'd gone public,
Alan Eustace who was running all of
the engineering team at the time would,
once a year, send a note out to everyone in engineering at
performance review time to get your Google resume
polished up so that you
could nominate yourself for a promotion.
First thing that you were suppose to do
was get your Google resume,
which is sort of this internal version of
a resume that showed all of your Google specific work.
And the example resume that he would send out was Jeff's,
and even in 2004,
like he'd been there long enough
where he'd just done everything.
And, you know I was an engineer at the time.
I would look at this and I'm like,
"Oh my god, my resume looks nothing like this."
And so I remember sending a note Alan Eustace saying,
"You have got to find someone else's resume.
You're depressing a thousand engineers
everytime you send this out."
Because Jeff is so great.
>> We're just huge fans really of Jeff.
So me, you know, fans of Jeff among them and just,
not just a great scientist but
also just an incredibly nice guy.
>> Yeah. But this whole notion of coupling
world-class engineering and
world class-systems engineering with AI problem solving,
I think that is something that we don't
really fully understand enough.
You can be the smartest AI guy
in the world and you know just have this sort of
incredible theoretical breakthrough, but
if you can't get that idea implemented,
not that it has no impact it just sort of
diminishes the potential impact that the idea can have.
That partnership I think you have with
Jeff is something really special.
>> I think I was really fortunate that
even when I started the Google Brain Team
I feel I brought a lot of
machine learning expertise and Jeff,
and other Google engineers
early team members like Rajat Monga,
Greg Corrado, just thought a 20 percent project for
him. But there are other Google engineers--
really first and foremost Jeff--they brought a lot of
systems abilities to the team.
And the other convenient thing was that,
we were able to get a thousand computers to run this.
And having Larry Page's backing and Jeff's ability to
marshal those types of computational
resources turns out to be really helpful.
>> Well, let's switch gears just a little bit.
I think it was really apt that you
pointed out that AI and
machine learning in particular are starting to
have GDP scale impact on the world.
Certainly, if you look at the products
that we're all using everyday,
there's many levels of machine learning involved
in everything from search to social networks to- I mean,
basically everything you use has got
just a little kiss of machine learning in it.
So, with that impact and
given how pervasive these technologies are,
there's a huge amount of
responsibility that comes along with it.
I know that you've been thinking a lot
about ethical development of AI
and what our responsibilities are
as scientists and engineers
as we build these technologies.
I'd loved to chat about that for a few minutes.
Yeah. There's potential to promulgate
things like discrimination and bias.
I think that with the rise of technology often
comes greater concentration of
power in smaller numbers of people's hands.
And I think that this creates greater risk
of ever-growing wealth inequality as well.
So, we're recording this here in California,
and to be really candid,
I think that with the rise
of the last few waves to technology,
we actually did a great job
creating wealth in the East and the West Coast,
but we actually did leave large parts
of the country behind,
and I would love for this next one
to bring everyone along with us.
>> Yeah. One of the things that I've spent a bunch
of time thinking about
is, from Microsoft's perspective,
when we think about how we build our AI technology,
we're thinking about platforms that we
can put in the hands of developers.
It's just sort of our wiring as a company.
So, the example you gave
earlier and the talk where you want someone in a mom
and pop shop to be able to program
their own LCD sign
to do whatever and everybody becomes a programmer,
we actually think that AI can play a big role in
delivering this future. And we want
everybody to be an AI developer.
I've been spending much of my time lately talking with
folks in agriculture and in healthcare,
which again you're thinking about
the problems that society has
to solve. In the United States.
the cost of healthcare is growing
faster than GDP which is
not sustainable over long periods of time.
Basically, the only way that I see
that you break that curve is with technology.
Now, it might not be AI. I think it is.
But something is going to have to sort of
intercede that pulls cost out
of the system while still giving
people very high quality healthcare outcomes.
And I just see a lot of companies almost every week,
there's some new result where AI can read and
EKG chart with cardiologists' level of accuracy,
which isn't about taking all of the cardiology jobs away.
It's about making this diagnostic capability
available to everyone because the cost is free
and then letting the cardiologist do
what's difficult and unique that humans should be doing.
I don't know if you see that pattern
in other domains as well.
>> I think there'll be a lot of
partnerships with the AI teams and
doctors that will be very valuable.
You know, one thing that excites me these days with
the theme of things like healthcare, agriculture,
and manufacturing is helping
great companies become great AI companies.
I was fortunate really, to have led the Google Brain team
which became I would say probably the leading force
in turning Google from
what was already a great company
into today great AI company.
Then, at Baidu, I was responsible
for the company's AI technology and strategy and team,
and I think that helped transform Baidu from
what was already a great company into a great AI company.
I think it really Satya
did a great job also transforming
Microsoft from a great company to a great AI company.
But for AI to reach its full potential,
we can't just transform tech companies,
we need to pull other industries
along for it to create this GDP growth,
for it to help people in healthcare deliver
a safer and more accessible food to people.
So, one thing I'm excited about,
building on my experience, helping with
really Google and Baidu's transformation
is to look at other industries as well to see
if either by providing AI solutions or
by engaging deeply in AI transmission programs,
whether my team at Landing.AI,
whether Landing.AI can help
other industries also become great at AI.
>> Well talk a little bit more about
what Landing.AI's mission is.
>> We want to empower businesses with AI.
There is so much need for
AI to enter other industries than technology,
everything ranging from manufacturing to
agriculture to healthcare, and so many more.
For example, in manufacturing,
there are today in factories
sometimes hundreds of thousands of people using
their eyes to inspect parts as they come off as
the assembly line to check for
scratches and things and so on.
We find that we can, for the most part,
automate that with deep learning
and often do it at a level
of reliability and consistency
that's greater than the people are.
People squinting at something
20 centimeters away your whole day,
that's actually not great for your eyesight it turns out,
and I would love for computers
rather than often these young employees to do it.
So, Landing.AI is working with
a few different industries to
provide solutions like that.
We also engage companies
with broader transformation programs.
So, for both Google and Baidu,
it was not one thing,
it's not that implement
neural networks for ads and so it's a great AI company.
For a company become
a great AI company is much more than that.
And then having sort of helped two great companies do that,
we are trying to help other companies as well,
especially ones outside tech become
leading AI entities in their industry vertical.
So, I find that work very meaningful
and very exciting.
Several days ago, I tweeted out that on Monday,
I literally wake up at 5:00 AM
so excited about one of
the Landing.AI projects, I couldn't go back to sleep.
I started getting and scribbling on my notebook.
So, I find these are really, really meaningful.
>> That's awesome. One thing I want
to sort of press on a little bit
is this manufacturing quality
control example that you just gave.
I think the thing that a lot of folks
don't understand is it's
not necessarily about the jobs going away,
it's about these companies being able to do more.
So, I worked in a small manufacturing company while
I was in college and we had exactly the same thing.
So, we operated a infrared reflow soldering machine
there which sort of melts,
surface mount components onto circuit boards.
So, you have to visually inspect
the board before it goes on to make sure
the components are seated and the solder
has been screened and all the right parts.
When it comes out,
you have to visually inspect it to make sure
that none of the parts of tombstond.
There are a variety of like little things
that can happen in the process.
So, we have people doing that.
If there was some way for them not to do it,
they would go do something else
that was more valuable or we
could run more boards so actually, in a way,
you could create more jobs because
the more work that this company could do economically,
the more jobs in general that it can create.
And I'm sort of seeing AI in
several different places like
in manufacturing automation as helping to bring
back jobs from overseas
that were lost because it was just sort of
cheaper to do them with
low cost labor in some other part of the world.
They're coming back now because like
automation has gotten so good that you
can start doing them with
fewer more expert people but here,
in the United States,
locally in these communities where
whatever it is that they're manufacturing is needed.
It's like these really interesting phenomena.
>> There was one part of your career
I did not know about it.
I followed a lot of your work at
Google and Microsoft, and even today,
people still speak glowingly of their privacy practices
you put in place when you're at Google.
I did not know you were into
this soldering business way back.
>> Yeah, I had put myself through college
some way or another. It was interesting though.
I remember one of my first jobs,
I had to put brass rivets into 5,000 circuit boards.
Circuit boards were controllers
for commercial washing machines and there were
six little brass tabs that you would put
electrical connectors onto and
each one of them had to be riveted.
So, it was 30,000 rivets that had to be done
and we had a manual rivet press and
my job at this company in
its first three months of existence right
after I graduated high school was to press,
rivet press 30,000 times, and that's awful.
Automation is not a bad thing.
>> In a lot countries we
work with we're seeing,
for example Japan, the country is
actually very different than the United States,
because it has an aging population.
>> Yeah.
>>And there just aren't enough people to do the work.
>> Correct.
>> So, they welcome automation
because the options are either automate or well,
just shut down the whole plant because it is impossible to
hire with the aging population.
>> Yeah. In Japan, it actually is going to become
a crucial social issue
sometime in the next 100 years or so
because their fertility rates are such
that they're in major population decline.
So, you should hope for really good AI there,
because we're going to need
incredibly sophisticated things to take
care of the aging population there,
especially in healthcare and elder care and whatnot.
You know, I think when we automated elevators.
Right? Once elevators had
to have a person operating them,
a lot of elevator operators did lose
their jobs because we switched to automatic elevators.
I think one challenge that AI offers is
that there will be as connected as it is today,
I think this change will happen very quickly,
or the potential for jobs to
disappear is faster this time around.
So, I think when we work with customers,
we actually have a stance
on wanting to make sure that everyone is treated well,
and to the extent, we're able to step in and try
to encourage or even assist
directly with retraining to help them find
better options, we're truly going to do that.
That actually hasn't been needed so far for
us because we're actually not displacing any jobs.
But if it ever happens, that is our stance.
But I think this actually speaks to
the important role of government with the rise of AI.
So, I think the world is not
about to run out of jobs anytime soon,
but as LinkedIn has said through
the LinkedIn data and many organizations,
and Coursera has seen and Coursera's data as well,
our population in the United States and globally
is not well-matched to the jobs that are being created.
And we can't find enough people for-
we can't find enough nurses,
we can't find enough wind turbine technicians,
a lot of cities,
the highest paid person might be
the auto mechanic and we can't find enough of those.
So, I think a lot of the challenge and
also the responsibility for nations or
for governments of a society is
to provide a safety net so that everyone has
a shot at learning new skills they need in order to
enter these other trades
that we just can't find enough
people to work in right now.
>> I could not agree more.
I think this is one of
the most important balances that
we're going to have to strike as a society,
and it's not just the United States,
it's a worldwide thing.
We don't want to under invest
in AI in this technology because we're
frightened about the negative consequences
it's going to have on jobs that might be disrupted.
On the other hand, we don't want
to be inhumane, incompassionate,
unethical about how we provide
support for folks who are going
to be disrupted potentially.
>> Yeah.
>> I think Coursera plays
an incredibly important role in
managing this sea change in that we have
to make reskilling and
education much cheaper and much more accessible to folks.
Because one of the things that we're doing is,
we're entering this new world
where the work of the mind is going to be far,
far, far more valuable even than it
already is than the work of the body.
So, that's the muscle that has
to get worked out and we've just got
to get people into
that habit and make it cheap and accessible.
>> Yeah. It is actually really interesting.
When you look at the careers of athletes,
you can't just train them in
great shape at age 21 and then stop working out.
The human body doesn't work like
that. Human mind is the same.
You can't just train, work on your brain until you're
21 and then stop working out your brain.
Your brain you go flabby if you do that.
>> Yes.
>> So, I think one of the ways I want the world to be
different is I want us to
build a lifelong learning society.
We need this because the pace of change is faster.
There's going to be technology invented next year and
that will affect your job five years after that.
So, all of us had better keep on learning new things.
I think this is a cultural sea change
that needs to happen across society,
because for us to all contribute
meaningfully to the world
and make other people's lives better,
the skills you need five years from now may
be very different than the skills you have today.
If you are no longer in college, well,
we still need you to go and acquire those skills.
So, I think we just need to acknowledge
also that learning and studying is hard work.
I want people if they have the capacity.
Sometimes your life circumstances prevent you from
working in certain ways, and everyone deserves
a lot of support throughout all phases of life.
But if someone has the capacity to spend
more time studying rather than
spend that equal amount of time watching TV,
I would rather they spend
that time studying so that they can
better contribute to their own lives
and to the broader society.
>> Yeah, and speaking again about the role of government,
one of the things that I think the government
could do to help with this transition
is AI has this enormous potential
to lower the costs of subsistence.
So, through precision agriculture
and artificial intelligence and healthcare,
there are probably things that we can do to affect
housing costs with AI and automation.
So, looking at Maslow's Hierarchy of Needs,
the bottom two levels
where you've got food, clothing, shelter,
and your personal safety and security,
I think the more that we can be
investing in those sorts of things,
like technologies that address
those needs and address
them across the board for everyone,
it does nothing but lift all boats basically.
I wish I had a magic wand that I could
wave over more young entrepreneurs and
encourage them to create startups that are
taking this really interesting,
increasingly valuable AI toolbox
that they have and apply it to these problems.
They really could change
the world in this incredible way.
>> You make such a good point.
>> So, the last tech thing that I wanted to ask you is,
there is sort of just an incredible rate of innovation
right now on AI in general,
and some of the stuff is what I call "stunt AI"
not in the sense that it's not valuable but it's-
>> Know go ahead. Name of names. I want to hear.
>> No, so I'll name our own name.
So, we, at Microsoft did
this really interesting AI stunt where
we had this hierarchical reinforcement learning system
that beat Ms. Pac-Man.
So, that's the flavor of what I would call "stunt AI."
I think they're useful
in a way because a lot of what we do is
very difficult for layfolks to understand.
So, the value of the stunt is holy crap,
you can actually have a piece of AI do this?
I'm a big classical piano fan and one of
the things I've always lamented about
being a computer scientist is,
there's no performance of computer science in general,
where a normal person can listen to
it or if you're talking about
an athlete like Steph Curry,
who has done an incredible amount of
technical preparation and becoming as
good as he is at basketball,
there's a performance at the end where you can
appreciate his skill and ability.
And these "stunt AI" things in a way are
a way for folks to appreciate what's happening.
Those are the exciting AI things for the layfolks.
What are the exciting things as
a specialists that you see on the horizon?
Like new things and reinforcement learning, coming,
people are doing some interesting stuff with transfer
learning now where I'm starting to
see some promise that
not every machine learning problem is
something where you're solving it in isolation.
What's interesting to you?
>> So, in the short term,
one thing I'm excited about is turning machine learning from
a bit of a black art into more of
a systematic engineering discipline.
I think, today, too much of machine learning
among a few wise people who happen to say,
"Oh, change the activation function in layer five."
And if for some reason it works,
then that can turn into a systematic
engineering process that would
demystify a lot of it and help
a lot more people access these tools.
>> Do you think that that's going to
come from there becoming
a real engineering practice
of deep neural network architect
or is that going to get solved with
this learning to learn stuff or
auto ML stuff that folks are working on, or maybe both?
>> I think auto ML is a very nice piece of work,
and ia a small piece of the puzzle,
maybe surrounding, optimizing
[inaudible] preferences, things like that.
But I think there are even bigger questions like,
when should you collect more data,
or is this data set good enough,
or should you synthesize more data,
or should you switch
algorithms from this type of algorith to that type of algorithm,
and do you have two neural networks
or one neural network offering a pipeline?
I think those bigger architectural questions go
beyond what the current automatic algorithm is able to do.
I've been working on this book,
"Machine Learning Yearning"
mlyearning.org, that I've been
emailing out to people on the mailing list for free
that's trying to conceptualize my own ideas, I guess,
to turn machine learning into
more of the engineering discipline
to make it more systematic.
But I think there's a lot more that
the community needs to do beyond what I,
as one individual, could do as well.
But that will be really exciting when we can
take the powerful tools of
supervised learning and help a lot more people are
able to use them systematically.
With the rise of software engineering
came the rise of ideas like,
"Oh, maybe we should have a PM."
I think those are Microsoft invention, right?
The PM, product manager, and then program manager,
project manager types of roles way back.
Then eventually came ideas like
the waterfall planning models or the scrum agile models.
I think we need new software engineering practices.
How do you get people to work
together in a machine learning world?
So all sorting it out to Landing.AI ask
our product managers do things differently,
then I think I see
any other company tell their product managers to do.
So we're still figure out these workflows and practices.
Beyond that, I think on a more pure technology side
[inaudible] again as I do
transform entertainment and art.
It'll be interesting to see how it goes beyond that.
I think the value of reinforcement
learning in games is very overhyped,
but I'm seeing some real attraction in
using reinforced learning to control robots.
So early signs from my friends
working on projects that are not
yet public for the most part,
but there are signs of meaningful progress
in the reinforced learning applied to robotics.
Then, I think transfer learning is vastly underrated.
The ability to learn from-
so there was a paper out of Facebook where
they trained on an unprecedented
3.5 billion images which is very, very big
3.5 images is very large,
even by today's standards,
and found that it turns out
training from 3.5 billion, in their case,
Instagram images, is actually better than
training on only one billion images.
So this is a good sign for
the microprocessor companies, I think,
because it means that, "Hey,
keep building these faster processes.
We'll find a way to suck up their processing power."
But with the ability to train on really,
really massive data sets to do
transfer learning or pre-training
or some set of ideas around there,
I think that is very
underrated today still. And then super long term-
We used the term unsupervised learning to describe a really,
really complicated set of
ideas that we don't even fully understand.
But I think that also will be
very important in the longer term.
>> So tell us something that people wouldn't know about you.
>> Sometimes, I just look at those bookstore
and deliberately buy a magazine
in some totally strange area that I
would otherwise never have bought a magazine in.
So whatever, five dollars,
you end up with a magazine in some area that you
just previously knew absolutely nothing about.
>> I think that's awesome.
>> One thing that not many people know about me,
is I actually really love stationery.
So my wife knows, when we travel to foreign countries,
sometimes I'll spend way too
long looking at pens and pencils and paper.
I think part of me feels like, "Boy,
if only I had the perfect pen and the perfect paper,
I could come up with better ideas."
It has not worked out so far,
but that dream lives on and on.
>> That's awesome. All right.
Well, thank you so much,
Andrew, for coming in today.
>> Thanks a lot for having me here, Kevin.
>> That was a really terrific conversation.
>> Yes, it was a ton of fun.
It was like all of my best conversations,
I felt like it wasn't
long at all and was glancing now at my phone and
I'm like, "Oh, my god. We've just spent 48 minutes."
>> One of the questions that you asked Andrew was,
what technology is he
most impressed by and excited by
this coming down the pike with AI?
I wanted to turn that back on you
because you've been working with
AI for a really long time at Google,
and at LinkedIn, and now at Microsoft.
So what have you seen that really excites you?
>> Several things. I'm excited that
this trend that started a whole bunch of years ago,
more data plus more compute equals
more practical AI and machine learning solutions.
It's been surprising to me that
that trend continues to have legs.
So, when I look forward into
the future and I see more data coming online,
particularly with IoT and the intelligent edge as
we get more things connected to the Cloud that
are sensing either through cameras or
far field microphone arrays or
temperature sensors or whatever it is that they are,
we will increasingly be digitizing the world.
Honestly, my prediction is that
the volumes of data that we're gathering now will
seem trivial by comparison to the volumes that
will be produced sometime in the next 5-10 years.
I think you take that with all of
the super exciting stuff that's happening with AI silicon
right now and just the
number of startups that are working
on brand new architectures
for a training machine learning models,
it really is an exciting time,
and I think that combo of more compute,
more data is going to continue
to surprise and delight us with
interesting new results and also deliver
this real world GDP
impacting value that folks are seeing.
So that's super cool.
But I tell you, the things that really move me,
that I have been seeing lately are the applications
into which people are putting this technology in
precision agriculture and healthcare.
Just recently, we went out to one of our farm partners.
The Microsoft Research has been working
with the things that they're doing with
AI machine learning and edge computing in
this small organic farm in
rural Washington state is absolutely incredible.
They're doing all of this stuff with a mind towards
"How do you take a small independent farmer
and help them optimize yields, reduce the amount of
chemicals that they have to use on their crop,
how much water they have to use so you're minimizing
environmental impacts and raising
more food and doing it in this local way?"
In the developing world,
that means that more people are going to get fed.
In the developed world,
it means that we all get to be a little more healthy
because the quality of
the food that we're eating is going to increase.
There's just this trend, I think,
right now where people are just
starting to apply this technology to
these things that are parts of human subsistence.
Here's the food, clothing, shelter,
the things that all of us need in order to
live a good quality life.
I think as I see these things and
I see the potential that AI has
to help everyone have access to a high quality of life,
the more excited I get.
I think in some cases, it may be
the only way that you're able to deliver these things at
scale to all of society
because some of them are just really expensive right now.
No matter how you redistribute the world's wealth,
you're not going to be able to tend to the needs of
a growing population without
some sort of technological intervention.
>> See, I thought you were
going to say something like, "Oh,
we're going to be able to live in the world of
Tron Legacy or the Matrix or whatever."
Instead, you get all serious on me and
talk about all the great things that in
the world changing awesome things
that are going to happen.
I'm going to live in my fantasy but I
like that there are very cool things happening.
>> I did
>> over my vacation read "Ready Player One" and
despite its mild dystopian overtones.
>> It's a great book. I like the book.
>> That's a damn good book.
I was like, "I want some of this."
>> I'm with you. I'm with you.
I was a little disappointed in
the movie but I loved the book.
Yeah. We can talk about this offline but
we'll end this now.
>> Yeah.
>> Well, awesome Christina.
I look forward to chatting with
you again on the next episode.
>> Me too. I can't wait.
>> Next time on Behind the Tech,
we're going to talk with Judy Estrin
who is a former CTO Cisco,
serial entrepreneur, and as a Ph.D. student,
a member of the lab that created the Internet protocols.
Hope you will join us. Be sure to
tell your friends about our new podcast,
Behind the Tech, and to subscribe. See you next time.
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