The following is a conversation with Andrew Ng,

one of the most impactful educators, researchers, innovators, and leaders

in artificial intelligence and technology space in general.

He cofounded Coursera and Google Brain,

launched Deep Learning AI, Landing AI, and the AI Fund,

and was the chief scientist at Baidu.

As a Stanford professor and with Coursera and Deep Learning AI,

he has helped educate and inspire millions of students, including me.

This is the Artificial Intelligence Podcast.

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And now, here's my conversation with Andrew Ng.

The courses you taught on machine learning at Stanford

and later on Coursera that you cofounded have educated and inspired millions of people.

So let me ask you, what people or ideas inspired you

to get into computer science and machine learning when you were young?

When did you first fall in love with the field, is another way to put it.

Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old.

At that time, I was learning the basic programming language,

and they would take these books and they'll tell you,

type this program into your computer, so type that program to my computer.

And as a result of all that typing, I would get to play these very simple shoot them up games

that I had implemented on my little computer.

So I thought it was fascinating as a young kid that I could write this code.

I was really just copying code from a book into my computer

to then play these cool little video games.

Another moment for me was when I was a teenager and my father,

who's a doctor, was reading about expert systems and about neural networks.

So he got me to read some of these books, and I thought it was really cool.

You could write a computer that started to exhibit intelligence.

Then I remember doing an internship while I was in high school, this was in Singapore,

where I remember doing a lot of photocopying and as an office assistant.

And the highlight of my job was when I got to use the shredder.

So the teenager me, remote thinking, boy, this is a lot of photocopying.

If only we could write software, build a robot, something to automate this,

maybe I could do something else.

So I think a lot of my work since then has centered on the theme of automation.

Even the way I think about machine learning today,

we're very good at writing learning algorithms that can automate things that people can do.

Or even launching the first MOOCs, Mass Open Online Courses, that later led to Coursera.

I was trying to automate what could be automatable in how I was teaching on campus.

Process of education, trying to automate parts of that to make it more,

sort of to have more impact from a single teacher, a single educator.

Yeah, I felt, you know, teaching at Stanford,

teaching machine learning to about 400 students a year at the time.

And I found myself filming the exact same video every year,

telling the same jokes in the same room.

And I thought, why am I doing this?

Why don't we just take last year's video?

And then I can spend my time building a deeper relationship with students.

So that process of thinking through how to do that,

that led to the first MOOCs that we launched.

And then you have more time to write new jokes.

Are there favorite memories from your early days at Stanford,

teaching thousands of people in person and then millions of people online?

You know, teaching online, what not many people know was that a lot of those videos

were shot between the hours of 10 p.m. and 3 a.m.

A lot of times, we were launching the first MOOCs at Stanford.

We had already announced the course, about 100,000 people signed up.

We just started to write the code and we had not yet actually filmed the videos.

So a lot of pressure, 100,000 people waiting for us to produce the content.

So many Fridays, Saturdays, I would go out, have dinner with my friends,

and then I would think, OK, do you want to go home now?

Or do you want to go to the office to film videos?

And the thought of being able to help 100,000 people potentially learn machine learning,

fortunately, that made me think, OK, I want to go to my office,

go to my tiny little recording studio.

I would adjust my Logitech webcam, adjust my Wacom tablet,

make sure my lapel mic was on,

and then I would start recording often until 2 a.m. or 3 a.m.

I think unfortunately, that doesn't show that it was recorded that late at night,

but it was really inspiring the thought that we could create content

to help so many people learn about machine learning.

How did that feel?

The fact that you're probably somewhat alone,

maybe a couple of friends recording with a Logitech webcam

and kind of going home alone at 1 or 2 a.m. at night

and knowing that that's going to reach sort of thousands of people,

eventually millions of people, what's that feeling like?

I mean, is there a feeling of just satisfaction of pushing through?

I think it's humbling.

And I wasn't thinking about what I was feeling.

I think one thing that I'm proud to say we got right from the early days

was I told my whole team back then that the number one priority

is to do what's best for learners, do what's best for students.

And so when I went to the recording studio,

the only thing on my mind was what can I say?

How can I design my slides?

What I need to draw right to make these concepts as clear as possible for learners?

I think I've seen sometimes instructors is tempting to,

hey, let's talk about my work.

Maybe if I teach you about my research,

someone will cite my papers a couple more times.

And I think one of the things we got right,

launching the first few MOOCs and later building Coursera,

was putting in place that bedrock principle of

let's just do what's best for learners and forget about everything else.

And I think that that is a guiding principle

turned out to be really important to the rise of the MOOC movement.

And the kind of learner you imagined in your mind

is as broad as possible, as global as possible.

So really try to reach as many people

interested in machine learning and AI as possible.

I really want to help anyone that had an interest in machine learning

to break into the field.

And I think sometimes I've actually had people ask me,

hey, why are you spending so much time explaining gradient descent?

And my answer was, if I look at what I think the learner needs

and what benefit from, I felt that having that

a good understanding of the foundations coming back to the basics

would put them in a better stead to then build on a long term career.

So try to consistently make decisions on that principle.

So one of the things you actually revealed to the narrow AI community

at the time and to the world is that the amount of people

who are actually interested in AI is much larger than we imagined.

By you teaching the class and how popular it became,

it showed that, wow, this isn't just a small community

of sort of people who go to NeurIPS and it's much bigger.

It's developers, it's people from all over the world.

I mean, I'm Russian, so everybody in Russia is really interested.

There's a huge number of programmers who are interested in machine learning,

India, China, South America, everywhere.

There's just millions of people who are interested in machine learning.

So how big do you get a sense that the number of people

is that are interested from your perspective?

I think the number has grown over time.

I think it's one of those things that maybe it feels like it came out of nowhere,

but it's an insight that building it, it took years.

It's one of those overnight successes that took years to get there.

My first foray into this type of online education

was when we were filming my Stanford class

and sticking the videos on YouTube and some other things.

We had uploaded the horrors and so on,

but it's basically the one hour, 15 minute video that we put on YouTube.

And then we had four or five other versions of websites that I had built,

most of which you would never have heard of

because they reached small audiences,

but that allowed me to iterate,

allowed my team and me to iterate,

to learn what are the ideas that work and what doesn't.

For example, one of the features I was really excited about

and really proud of was build this website

where multiple people could be logged into the website at the same time.

So today, if you go to a website,

if you are logged in and then I want to log in,

you need to log out because it's the same browser, the same computer.

But I thought, well, what if two people say you and me

were watching a video together in front of a computer?

What if a website could have you type your name and password,

have me type my name and password,

and then now the computer knows both of us are watching together

and it gives both of us credit for anything we do as a group.

Influencers feature rolled it out in a high school in San Francisco.

We had about 20 something users.

Where's the teacher there?

Sacred Heart Cathedral Prep, the teacher is great.

I mean, guess what?

Zero people use this feature.

It turns out people studying online,

they want to watch the videos by themselves.

So you can play back, pause at your own speed rather than in groups.

So that was one example of a tiny lesson learned out of many

that allowed us to hone into the set of features.

It sounds like a brilliant feature.

So I guess the lesson to take from that is

there's something that looks amazing on paper and then nobody uses it.

It doesn't actually have the impact that you think it might have.

And so, yeah, I saw that you really went through a lot of different features

and a lot of ideas to arrive at Coursera,

the final kind of powerful thing that showed the world

that MOOCs can educate millions.

And I think with the whole machine learning movement as well,

I think it didn't come out of nowhere.

Instead, what happened was as more people learn about machine learning,

they will tell their friends and their friends will see

how it's applicable to their work.

And then the community kept on growing.

And I think we're still growing.

I don't know in the future what percentage of all developers

will be AI developers.

I could easily see it being north of 50%, right?

Because so many AI developers broadly construed,

not just people doing the machine learning modeling,

but the people building infrastructure, data pipelines,

all the software surrounding the core machine learning model

maybe is even bigger.

I feel like today almost every software engineer

has some understanding of the cloud.

Not all, but maybe this is my microcontroller developer

that doesn't need to deal with the cloud.

But I feel like the vast majority of software engineers today

are sort of having an appreciation of the cloud.

I think in the future, maybe we'll approach nearly 100% of all developers

being in some way an AI developer

or at least having an appreciation of machine learning.

And my hope is that there's this kind of effect

that there's people who are not really interested in being a programmer

or being into software engineering, like biologists, chemists,

and physicists, even mechanical engineers,

all these disciplines that are now more and more sitting on large data sets.

And here they didn't think they're interested in programming

until they have this data set and they realize

there's this set of machine learning tools

that allow you to use the data set.

So they actually become, they learn to program

and they become new programmers.

So like the, not just because you've mentioned

a larger percentage of developers become machine learning people.

So it seems like more and more the kinds of people

who are becoming developers is also growing significantly.

Yeah, I think once upon a time,

only a small part of humanity was literate, could read and write.

And maybe you thought, maybe not everyone needs to learn to read and write.

You just go listen to a few monks read to you and maybe that was enough.

Or maybe you just need a few handful of authors to write the bestsellers

and no one else needs to write.

But what we found was that by giving as many people,

in some countries, almost everyone, basic literacy,

it dramatically enhanced human to human communications.

And we can now write for an audience of one,

such as if I send you an email or you send me an email.

I think in computing, we're still in that phase

where so few people know how to code

that the coders mostly have to code for relatively large audiences.

But if everyone, or most people became developers at some level,

similar to how most people in developed economies are somewhat literate,

I would love to see the owners of a mom and pop store

be able to write a little bit of code to customize the TV display

for their special this week.

And I think it will enhance human to computer communications,

which is becoming more and more important today as well.

So you think it's possible that machine learning

becomes kind of similar to literacy,

where like you said, the owners of a mom and pop shop,

is basically everybody in all walks of life

would have some degree of programming capability?

I could see society getting there.

There's one other interesting thing.

If I go talk to the mom and pop store,

if I talk to a lot of people in their daily professions,

I previously didn't have a good story for why they should learn to code.

We could give them some reasons.

But what I found with the rise of machine learning and data science is that

I think the number of people with a concrete use for data science

in their daily lives, in their jobs,

may be even larger than the number of people

who have concrete use for software engineering.

For example, if you run a small mom and pop store,

I think if you can analyze the data about your sales, your customers,

I think there's actually real value there,

maybe even more than traditional software engineering.

So I find that for a lot of my friends in various professions,

be it recruiters or accountants or people that work in the factories,

which I deal with more and more these days,

I feel if they were data scientists at some level,

they could immediately use that in their work.

So I think that data science and machine learning

may be an even easier entree into the developer world

for a lot of people than the software engineering.

That's interesting.

And I agree with that, but that's beautifully put.

But we live in a world where most courses and talks have slides,

PowerPoint, keynote,

and yet you famously often still use a marker and a whiteboard.

The simplicity of that is compelling,

and for me at least, fun to watch.

So let me ask, why do you like using a marker and whiteboard,

even on the biggest of stages?

I think it depends on the concepts you want to explain.

For mathematical concepts,

it's nice to build up the equation one piece at a time,

and the whiteboard marker or the pen and stylus

is a very easy way to build up the equation,

to build up a complex concept one piece at a time

while you're talking about it,

and sometimes that enhances understandability.

The downside of writing is that it's slow,

and so if you want a long sentence, it's very hard to write that.

So I think there are pros and cons,

and sometimes I use slides,

and sometimes I use a whiteboard or a stylus.

The slowness of a whiteboard is also its upside,

because it forces you to reduce everything to the basics.

Some of your talks involve the whiteboard.

I mean, you go very slowly,

and you really focus on the most simple principles,

and that's a beautiful,

that enforces a kind of a minimalism of ideas

that I think is surprising at least for me is great for education.

Like a great talk, I think, is not one that has a lot of content.

A great talk is one that just clearly says a few simple ideas,

and I think the whiteboard somehow enforces that.

Peter Abbeel, who's now one of the top roboticists

and reinforcement learning experts in the world,

was your first PhD student.

So I bring him up just because I kind of imagine

this must have been an interesting time in your life,

and do you have any favorite memories of working with Peter,

since you were your first student in those uncertain times,

especially before deep learning really sort of blew up?

Any favorite memories from those times?

Yeah, I was really fortunate to have had Peter Abbeel

as my first PhD student,

and I think even my long term professional success

builds on early foundations or early work

that Peter was so critical to.

So I was really grateful to him for working with me.

What not a lot of people know is just how hard research was,

and still is.

Peter's PhD thesis was using reinforcement learning

to fly helicopters.

And so, even today, the website heli.stanford.edu,

heli.stanford.edu is still up.

You can watch videos of us using reinforcement learning

to make a helicopter fly upside down,

fly loose roses, so it's cool.

It's one of the most incredible robotics videos ever,

so people should watch it.

Oh yeah, thank you.

It's inspiring.

That's from like 2008 or seven or six, like that range.

Yeah, something like that.

Yeah, so it was over 10 years old.

That was really inspiring to a lot of people, yeah.

What not many people see is how hard it was.

So Peter and Adam Coase and Morgan Quigley and I

were working on various versions of the helicopter,

and a lot of things did not work.

For example, it turns out one of the hardest problems we had

was when the helicopter's flying around upside down,

doing stunts, how do you figure out the position?

How do you localize the helicopter?

So we wanted to try all sorts of things.

Having one GPS unit doesn't work

because you're flying upside down,

the GPS unit's facing down, so you can't see the satellites.

So we experimented trying to have two GPS units,

one facing up, one facing down.

So if you flip over, that didn't work

because the downward facing one couldn't synchronize

if you're flipping quickly.

Morgan Quigley was exploring this crazy,

complicated configuration of specialized hardware

to interpret GPS signals.

Looking at the FPG is completely insane.

Spent about a year working on that, didn't work.

So I remember Peter, great guy, him and me,

sitting down in my office looking at some of the latest things

we had tried that didn't work and saying,

done it, what now?

Because we tried so many things and it just didn't work.

In the end, what we did, and Adam Coles was crucial to this,

was put cameras on the ground and use cameras on the ground

to localize the helicopter.

And that solved the localization problem

so that we could then focus on the reinforcement learning

and inverse reinforcement learning techniques

so it didn't actually make the helicopter fly.

And I'm reminded, when I was doing this work at Stanford,

around that time, there was a lot of reinforcement learning

theoretical papers, but not a lot of practical applications.

So the autonomous helicopter work for flying helicopters

was one of the few practical applications

of reinforcement learning at the time,

which caused it to become pretty well known.

I feel like we might have almost come full circle with today.

There's so much buzz, so much hype, so much excitement

about reinforcement learning.

But again, we're hunting for more applications

of all of these great ideas that David Kuhnke has come up with.

What was the drive sort of in the face of the fact

that most people are doing theoretical work?

What motivates you in the uncertainty and the challenges

to get the helicopter sort of to do the applied work,

to get the actual system to work?

Yeah, in the face of fear, uncertainty, sort of the setbacks

that you mentioned for localization.

I like stuff that works.

In the physical world.

So like, it's back to the shredder.

You know, I like theory, but when I work on theory myself,

and this is personal taste,

I'm not saying anyone else should do what I do.

But when I work on theory, I personally enjoy it more

if I feel that the work I do will influence people,

have positive impact, or help someone.

I remember when many years ago,

I was speaking with a mathematics professor,

and it kind of just said, hey, why do you do what you do?

It kind of just said, hey, why do you do what you do?

And then he said, he had stars in his eyes when he answered.

And this mathematician, not from Stanford,

different university, he said, I do what I do

because it helps me to discover truth and beauty

in the universe.

He had stars in his eyes when he said that.

And I thought, that's great.

I don't want to do that.

I think it's great that someone does that,

fully support the people that do it,

a lot of respect for people that do that.

But I am more motivated when I can see a line

to how the work that my teams and I are doing helps people.

The world needs all sorts of people.

I'm just one type.

I don't think everyone should do things

the same way as I do.

But when I delve into either theory or practice,

if I personally have conviction that here's a pathway

to help people, I find that more satisfying

to have that conviction.

That's your path.

You were a proponent of deep learning

before it gained widespread acceptance.

What did you see in this field that gave you confidence?

What was your thinking process like in that first decade

of the, I don't know what that's called, 2000s, the aughts?

Yeah, I can tell you the thing we got wrong

and the thing we got right.

The thing we really got wrong was the importance of,

the early importance of unsupervised learning.

So early days of Google Brain,

we put a lot of effort into unsupervised learning

rather than supervised learning.

And there was this argument,

I think it was around 2005 after NeurIPS,

at that time called NIPS, but now NeurIPS had ended.

And Jeff Hinton and I were sitting in the cafeteria

outside the conference.

We had lunch, we were just chatting.

And Jeff pulled up this napkin.

He started sketching this argument on a napkin.

It was very compelling, as I'll repeat it.

Human brain has about a hundred trillion.

So there's 10 to the 14 synaptic connections.

You will live for about 10 to the nine seconds.

That's 30 years.

You actually live for two by 10 to the nine,

maybe three by 10 to the nine seconds.

So just let's say 10 to the nine.

So if each synaptic connection,

each weight in your brain's neural network

has just a one bit parameter,

that's 10 to the 14 bits you need to learn

in up to 10 to the nine seconds.

10 to the nine seconds of your life.

So via this simple argument,

which is a lot of problems, it's very simplified.

That's 10 to the five bits per second

you need to learn in your life.

And I have a one year old daughter.

I am not pointing out 10 to five bits per second

of labels to her.

And I think I'm a very loving parent,

but I'm just not gonna do that.

So from this very crude, definitely problematic argument,

there's just no way that most of what we know

is through supervised learning.

But where you get so many bits of information

is from sucking in images, audio,

those experiences in the world.

And so that argument,

and there are a lot of known forces argument

you should go into,

really convinced me that there's a lot of power

to unsupervised learning.

So that was the part that we actually maybe got wrong.

I still think unsupervised learning is really important,

but in the early days, 10, 15 years ago,

a lot of us thought that was the path forward.

Oh, so you're saying that that perhaps

was the wrong intuition for the time.

For the time, that was the part we got wrong.

The part we got right was the importance of scale.

So Adam Coates, another wonderful person,

fortunate to have worked with him,

he was in my group at Stanford at the time

and Adam had run these experiments at Stanford

showing that the bigger we train a learning algorithm,

the better its performance.

And it was based on that.

There was a graph that Adam generated

where the X axis, Y axis lines going up into the right.

So the bigger you make this thing,

the better its performance accuracy is the vertical axis.

So it's really based on that chart that Adam generated

that he gave me the conviction

that you could scale these models way bigger

than what we could on a few CPUs,

which is where we had at Stanford

that we could get even better results.

And it was really based on that one figure

that Adam generated

that gave me the conviction to go with Sebastian Thrun

to pitch starting a project at Google,

which became the Google Brain project.

The Brain, you go find a Google Brain.

And there the intuition was scale

will bring performance for the system.

So we should chase a larger and larger scale.

And I think people don't realize how groundbreaking of it.

It's simple, but it's a groundbreaking idea

that bigger data sets will result in better performance.

It was controversial at the time.

Some of my well meaning friends,

senior people in the machine learning community,

I won't name, but some of whom we know,

my well meaning friends came

and were trying to give me friendly,

I was like, hey, Andrew, why are you doing this?

This is crazy.

It's in the near natural architecture.

Look at these architectures of building.

You just want to go for scale?

Like this is a bad career move.

So my well meaning friends,

some of them were trying to talk me out of it.

But I find that if you want to make a breakthrough,

you sometimes have to have conviction

and do something before it's popular,

since that lets you have a bigger impact.

Let me ask you just a small tangent on that topic.

I find myself arguing with people saying that greater scale,

especially in the context of active learning,

so very carefully selecting the data set,

but growing the scale of the data set

is going to lead to even further breakthroughs

in deep learning.

And there's currently pushback at that idea

that larger data sets are no longer,

so you want to increase the efficiency of learning.

You want to make better learning mechanisms.

And I personally believe that bigger data sets will still,

with the same learning methods we have now,

will result in better performance.

What's your intuition at this time

on this dual side?

Do we need to come up with better architectures for learning

or can we just get bigger, better data sets

that will improve performance?

I think both are important and it's also problem dependent.

So for a few data sets,

we may be approaching a Bayes error rate

or approaching or surpassing human level performance

and then there's that theoretical ceiling

that we will never surpass,

so Bayes error rate.

But then I think there are plenty of problems

where we're still quite far

from either human level performance

or from Bayes error rate

and bigger data sets with neural networks

without further algorithmic innovation

will be sufficient to take us further.

But on the flip side,

if we look at the recent breakthroughs

using transforming networks or language models,

it was a combination of novel architecture

but also scale had a lot to do with it.

If we look at what happened with GP2 and BERTZ,

I think scale was a large part of the story.

Yeah, that's not often talked about

is the scale of the data set it was trained on

and the quality of the data set

because there's some,

so it was like reddit threads that had,

they were operated highly.

So there's already some weak supervision

on a very large data set

that people don't often talk about, right?

I find that today we have maturing processes

to managing code,

things like Git, right?

Version control.

It took us a long time to evolve the good processes.

I remember when my friends and I

were emailing each other C++ files in email,

but then we had,

was it CVS or version Git?

Maybe something else in the future.

We're very mature in terms of tools for managing data

and think about the clean data

and how to solve down very hot, messy data problems.

I think there's a lot of innovation there

to be had still.

I love the idea that you were versioning through email.

I'll give you one example.

When we work with manufacturing companies,

it's not at all uncommon

for there to be multiple labels

that disagree with each other, right?

And so we would do the work in visual inspection.

We will take, say, a plastic part

and show it to one inspector

and the inspector, sometimes very opinionated,

they'll go, clearly, that's a defect.

This scratch, unacceptable.

Gotta reject this part.

Take the same part to different inspector,

different, very opinionated.

Clearly, the scratch is small.

It's fine.

Don't throw it away.

You're gonna make us, you know.

And then sometimes you take the same plastic part,

show it to the same inspector

in the afternoon, I suppose, in the morning,

and very opinionated go, in the morning,

they say, clearly, it's okay.

In the afternoon, equally confident.

Clearly, this is a defect.

And so what is an AI team supposed to do

if sometimes even one person doesn't agree

with himself or herself in the span of a day?

So I think these are the types of very practical,

very messy data problems that my teams wrestle with.

In the case of large consumer internet companies

where you have a billion users,

you have a lot of data.

You don't worry about it.

Just take the average.

It kind of works.

But in a case of other industry settings,

we don't have big data.

If just a small data, very small data sets,

maybe around 100 defective parts

or 100 examples of a defect.

If you have only 100 examples,

these little labeling errors,

if 10 of your 100 labels are wrong,

that actually is 10% of your data set has a big impact.

So how do you clean this up?

What are you supposed to do?

This is an example of the types of things

that my teams, this is a landing AI example,

are wrestling with to deal with small data,

which comes up all the time

once you're outside consumer internet.

Yeah, that's fascinating.

So then you invest more effort and time

in thinking about the actual labeling process.

What are the labels?

What are the how are disagreements resolved

and all those kinds of like pragmatic real world problems.

That's a fascinating space.

Yeah, I find that actually when I'm teaching at Stanford,

I increasingly encourage students at Stanford

to try to find their own project

for the end of term project,

rather than just downloading someone else's

nicely clean data set.

It's actually much harder if you need to go

and define your own problem and find your own data set,

rather than you go to one of the several good websites,

very good websites with clean scoped data sets

that you could just work on.

You're now running three efforts,

the AI Fund, Landing AI, and deeplearning.ai.

As you've said, the AI Fund is involved

in creating new companies from scratch.

Landing AI is involved in helping

already established companies do AI

and deeplearning.ai is for education of everyone else

or of individuals interested in getting into the field

and excelling in it.

So let's perhaps talk about each of these areas.

First, deeplearning.ai.

How, the basic question,

how does a person interested in deep learning

get started in the field?

Deep learning.ai is working to create courses

to help people break into AI.

So my machine learning course that I taught through Stanford

is one of the most popular courses on Coursera.

To this day, it's probably one of the courses,

sort of, if I asked somebody,

how did you get into machine learning

or how did you fall in love with machine learning

or would get you interested,

it always goes back to Andrew Ng at some point.

I see, yeah, I'm sure.

You've influenced, the amount of people

you've influenced is ridiculous.

So for that, I'm sure I speak for a lot of people

say big thank you.

No, yeah, thank you.

I was once reading a news article,

I think it was tech review

and I'm gonna mess up the statistic,

but I remember reading an article that said

something like one third of all programmers are self taught.

I may have the number one third,

around me was two thirds,

but when I read that article,

I thought this doesn't make sense.

Everyone is self taught.

So, cause you teach yourself.

I don't teach people.

That's well put.

Yeah, so how does one get started in deep learning

and where does deeplearning.ai fit into that?

So the deep learning specialization offered by deeplearning.ai

is I think it was Coursera's top specialization.

It might still be.

So it's a very popular way for people

to take that specialization

to learn about everything from neural networks

to how to tune in your network

to what is a ConvNet to what is a RNN

or a sequence model or what is an attention model.

And so the deep learning specialization

steps everyone through those algorithms

so you deeply understand it

and can implement it and use it for whatever application.

From the very beginning.

So what would you say are the prerequisites

for somebody to take the deep learning specialization

in terms of maybe math or programming background?

Yeah, need to understand basic programming

since there are programming exercises in Python

and the math prereq is quite basic.

So no calculus is needed.

If you know calculus is great, you get better intuitions

but deliberately try to teach that specialization

without requiring calculus.

So I think high school math would be sufficient.

If you know how to multiply two matrices,

I think that's great.

So a little basic linear algebra is great.

Basic linear algebra,

even very, very basic linear algebra in some programming.

I think that people that have done the machine learning course

will find a deep learning specialization a bit easier

but it's also possible to jump

into the deep learning specialization directly

but it will be a little bit harder

since we tend to go over faster concepts

like how does gradient descent work

and what is the objective function

which is covered more slowly in the machine learning course.

Could you briefly mention some of the key concepts

in deep learning that students should learn

that you envision them learning in the first few months

in the first year or so?

So if you take the deep learning specialization,

you learn the foundations of what is a neural network.

How do you build up a neural network

from a single logistic unit to a stack of layers

to different activation functions.

You learn how to train the neural networks.

One thing I'm very proud of in that specialization

is we go through a lot of practical knowhow

of how to actually make these things work.

So what are the differences between different optimization algorithms?

What do you do if the algorithm overfits

or how do you tell if the algorithm is overfitting?

When do you collect more data?

When should you not bother to collect more data?

I find that even today, unfortunately,

there are engineers that will spend six months

trying to pursue a particular direction

such as collect more data

because we heard more data is valuable

but sometimes you could run some tests

and could have figured out six months earlier

that for this particular problem, collecting more data isn't going to cut it.

So just don't spend six months collecting more data.

Spend your time modifying the architecture or trying something else.

So go through a lot of the practical knowhow

so that when someone, when you take the deep learning specialization,

you have those skills to be very efficient

in how you build these networks.

So dive right in to play with the network, to train it,

to do the inference on a particular data set,

to build intuition about it without building it up too big

to where you spend, like you said, six months

learning, building up your big project

without building any intuition of a small aspect of the data

that could already tell you everything you need to know about that data.

Yes, and also the systematic frameworks of thinking

for how to go about building practical machine learning.

Maybe to make an analogy, when we learn to code,

we have to learn the syntax of some programming language, right?

Be it Python or C++ or Octave or whatever.

But the equally important or maybe even more important part of coding

is to understand how to string together these lines of code

into coherent things.

So when should you put something in a function column?

When should you not?

How do you think about abstraction?

So those frameworks are what makes a programmer efficient

even more than understanding the syntax.

I remember when I was an undergrad at Carnegie Mellon,

one of my friends would debug their code

by first trying to compile it, and then it was C++ code.

And then every line in the syntax error,

they want to get rid of the syntax errors as quickly as possible.

So how do you do that?

Well, they would delete every single line of code with a syntax error.

So really efficient for getting rid of syntax errors

for horrible debugging errors.

So I think we learn how to debug.

And I think in machine learning,

the way you debug a machine learning program

is very different than the way you do binary search or whatever,

or use a debugger, trace through the code

in traditional software engineering.

So it's an evolving discipline,

but I find that the people that are really good

at debugging machine learning algorithms

are easily 10x, maybe 100x faster at getting something to work.

And the basic process of debugging is,

so the bug in this case,

why isn't this thing learning, improving,

sort of going into the questions of overfitting

and all those kinds of things?

That's the logical space that the debugging is happening in

with neural networks.

Yeah, often the question is, why doesn't it work yet?

Or can I expect it to eventually work?

And what are the things I could try?

Change the architecture, more data, more regularization,

different optimization algorithm,

different types of data.

So to answer those questions systematically,

so that you don't spend six months hitting down the blind alley

before someone comes and says,

why did you spend six months doing this?

What concepts in deep learning

do you think students struggle the most with?

Or sort of is the biggest challenge for them

was to get over that hill.

It hooks them and it inspires them and they really get it.

Similar to learning mathematics,

I think one of the challenges of deep learning

is that there are a lot of concepts

that build on top of each other.

If you ask me what's hard about mathematics,

I have a hard time pinpointing one thing.

Is it addition, subtraction?

Is it a carry?

Is it multiplication?

There's just a lot of stuff.

I think one of the challenges of learning math

and of learning certain technical fields

is that there are a lot of concepts

and if you miss a concept,

then you're kind of missing the prerequisite

for something that comes later.

So in the deep learning specialization,

try to break down the concepts

to maximize the odds of each component being understandable.

So when you move on to the more advanced thing,

we learn confidence,

hopefully you have enough intuitions

from the earlier sections

to then understand why we structure confidence

in a certain way

and then eventually why we built RNNs and LSTMs

or attention models in a certain way

building on top of the earlier concepts.

Actually, I'm curious,

you do a lot of teaching as well.

Do you have a favorite,

this is the hard concept moment in your teaching?

Well, I don't think anyone's ever turned the interview on me.

I'm glad you get first.

I think that's a really good question.

Yeah, it's really hard to capture the moment

when they struggle.

I think you put it really eloquently.

I do think there's moments

that are like aha moments

that really inspire people.

I think for some reason,

reinforcement learning,

especially deep reinforcement learning

is a really great way

to really inspire people

and get what the use of neural networks can do.

Even though neural networks

really are just a part of the deep RL framework,

but it's a really nice way

to paint the entirety of the picture

of a neural network

being able to learn from scratch,

knowing nothing and explore the world

and pick up lessons.

I find that a lot of the aha moments

happen when you use deep RL

to teach people about neural networks,

which is counterintuitive.

I find like a lot of the inspired sort of fire

in people's passion,

people's eyes,

it comes from the RL world.

Do you find reinforcement learning

to be a useful part

of the teaching process or no?

I still teach reinforcement learning

in one of my Stanford classes

and my PhD thesis was on reinforcement learning.

So I clearly loved a few.

I find that if I'm trying to teach

students the most useful techniques

for them to use today,

I end up shrinking the amount of time

I talk about reinforcement learning.

It's not what's working today.

Now, our world changes so fast.

Maybe this will be totally different

in a couple of years.

But I think we need a couple more things

for reinforcement learning to get there.

One of my teams is looking

to reinforcement learning

for some robotic control tasks.

So I see the applications,

but if you look at it as a percentage

of all of the impact

of the types of things we do,

it's at least today outside of

playing video games, right?

In a few of the games, the scope.

Actually, at NeurIPS,

a bunch of us were standing around

saying, hey, what's your best example

of an actual deploy reinforcement

learning application?

And among like

senior machine learning researchers, right?

And again, there are some emerging ones,

but there are not that many great examples.

I think you're absolutely right.

The sad thing is there hasn't been

a big impactful real world application

of reinforcement learning.

I think its biggest impact to me

has been in the toy domain,

in the game domain,

in the small example.

That's what I mean for educational purpose.

It seems to be a fun thing to explore

in your networks with.

But I think from your perspective,

and I think that might be

the best perspective is

if you're trying to educate

with a simple example

in order to illustrate

how this can actually be grown

to scale and have a real world impact,

then perhaps focusing on the fundamentals

of supervised learning

in the context of a simple data set,

even like an MNIST data set

is the right way,

is the right path to take.

The amount of fun I've seen people

have with reinforcement learning

has been great,

but not in the applied impact

in the real world setting.

So it's a trade off,

how much impact you want to have

versus how much fun you want to have.

Yeah, that's really cool.

And I feel like the world

actually needs all sorts.

Even within machine learning,

I feel like deep learning

is so exciting,

but the AI team

shouldn't just use deep learning.

I find that my teams

use a portfolio of tools.

And maybe that's not the exciting thing

to say, but some days

we use a neural net,

some days we use a PCA.

Actually, the other day,

I was sitting down with my team

looking at PCA residuals,

trying to figure out what's going on

with PCA applied

to manufacturing problem.

And some days we use

a probabilistic graphical model,

some days we use a knowledge draft,

which is one of the things

that has tremendous industry impact.

But the amount of chatter

about knowledge drafts in academia

is really thin compared

to the actual real world impact.

So I think reinforcement learning

should be in that portfolio.

And then it's about balancing

how much we teach all of these things.

And the world should have

diverse skills.

It'd be sad if everyone

just learned one narrow thing.

Yeah, the diverse skill

help you discover the right tool

for the job.

What is the most beautiful,

surprising or inspiring idea

in deep learning to you?

Something that captivated

your imagination.

Is it the scale that could be,

the performance that could be

achieved with scale?

Or is there other ideas?

I think that if my only job

was being an academic researcher,

if an unlimited budget

and didn't have to worry

about short term impact

and only focus on long term impact,

I'd probably spend all my time

doing research on unsupervised learning.

I still think unsupervised learning

is a beautiful idea.

At both this past NeurIPS and ICML,

I was attending workshops

or listening to various talks

about self supervised learning,

which is one vertical segment

maybe of unsupervised learning

that I'm excited about.

Maybe just to summarize the idea,

I guess you know the idea

about describing fleet.

No, please.

So here's the example

of self supervised learning.

Let's say we grab a lot

of unlabeled images off the internet.

So with infinite amounts

of this type of data,

I'm going to take each image

and rotate it by a random

multiple of 90 degrees.

And then I'm going to train

a supervised neural network

to predict what was

the original orientation.

So it has to be rotated 90 degrees,

180 degrees, 270 degrees,

or zero degrees.

So you can generate

an infinite amounts of labeled data

because you rotated the image

so you know what's the

ground truth label.

And so various researchers

have found that by taking

unlabeled data and making

up labeled data sets

and training a large neural network

on these tasks,

you can then take the hidden

layer representation and transfer

it to a different task

very powerfully.

Learning word embeddings

where we take a sentence,

delete a word,

predict the missing word,

which is how we learn.

One of the ways we learn

word embeddings

is another example.

And I think there's now

this portfolio of techniques

for generating these made up tasks.

Another one called jigsaw

would be if you take an image,

cut it up into a three by three grid,

so like a nine,

three by three puzzle piece,

jump up the nine pieces

and have a neural network predict

which of the nine factorial

possible permutations

it came from.

So many groups,

including OpenAI,

Peter B has been doing

some work on this too,

Facebook, Google Brain,

I think DeepMind,

oh actually,

Aaron van der Oort

has great work on the CPC objective.

So many teams are doing exciting work

and I think this is a way

to generate infinite label data

and I find this a very exciting

piece of unsupervised learning.

So long term you think

that's going to unlock

a lot of power

in machine learning systems

is this kind of unsupervised learning.

I don't think there's

a whole enchilada,

I think it's just a piece of it

and I think this one piece

unsupervised,

self supervised learning

is starting to get traction.

We're very close

to it being useful.

Well, word embedding

is really useful.

I think we're getting

closer and closer

to just having a significant

real world impact

maybe in computer vision and video

but I think this concept

and I think there'll be

other concepts around it.

You know, other unsupervised

learning things that I worked on

I've been excited about.

I was really excited

about sparse coding

and ICA,

slow feature analysis.

I think all of these are ideas

that various of us

were working on

about a decade ago

before we all got distracted

by how well supervised

learning was doing.

So we would return

we would return to the fundamentals

of representation learning

that really started

this movement of deep learning.

I think there's a lot more work

that one could explore around

this theme of ideas

and other ideas

to come up with better algorithms.

So if we could return

to maybe talk quickly

about the specifics

of deep learning.ai

the deep learning specialization

perhaps how long does it take

to complete the course

would you say?

The official length

of the deep learning specialization

is I think 16 weeks

so about four months

but it's go at your own pace.

So if you subscribe

to the deep learning specialization

there are people that finished it

in less than a month

by working more intensely

and studying more intensely

so it really depends on

on the individual.

When we created

the deep learning specialization

we wanted to make it

very accessible

and very affordable.

And with you know

Coursera and deep learning.ai

education mission

one of the things

that's really important to me

is that if there's someone

for whom paying anything

is a financial hardship

then just apply for financial aid

and get it for free.

If you were to recommend

a daily schedule for people

in learning whether it's

through the deep learning.ai

specialization or just learning

in the world of deep learning

what would you recommend?

How do they go about day to day

sort of specific advice

about learning

about their journey in the world

of deep learning machine learning?

I think getting the habit of learning

is key and that means regularity.

So for example

we send out a weekly newsletter

the batch every Wednesday

so people know it's coming Wednesday

you can spend a little bit of time

on Wednesday

catching up on the latest news

catching up on the latest news

through the batch on Wednesday

and for myself

I've picked up a habit of spending

some time every Saturday

and every Sunday reading or studying

and so I don't wake up on the Saturday

and have to make a decision

do I feel like reading

or studying today or not

it's just what I do

and the fact is a habit

makes it easier.

So I think if someone can get into that habit

it's like you know

just like we brush our teeth every morning

I don't think about it

if I thought about it

it's a little bit annoying

to have to spend two minutes doing that

but it's a habit that it takes

no cognitive load

but this would be so much harder

if we have to make a decision every morning

and actually that's the reason

why I wear the same thing every day as well

it's just one less decision

I just get up and wear my blue shirt

so but I think if you can get that habit

that consistency of studying

then it actually feels easier.

So yeah it's kind of amazing

in my own life

like I play guitar every day for

I force myself to at least for five minutes

play guitar

it's just it's a ridiculously short period of time

but because I've gotten into that habit

it's incredible what you can accomplish

in a period of a year or two years

you can become

you know exceptionally good

at certain aspects of a thing

by just doing it every day

for a very short period of time

it's kind of a miracle

that that's how it works

it adds up over time.

Yeah and I think this is often

not about the bursts of sustained efforts

and the all nighters

because you could only do that

a limited number of times

it's the sustained effort over a long time

I think you know reading two research papers

is a nice thing to do

but the power is not reading two research papers

it's reading two research papers a week

for a year

then you read a hundred papers

and you actually learn a lot

when you read a hundred papers.

So regularity and making learning a habit

do you have general other study tips

for particularly deep learning

that people should

in their process of learning

is there some kind of recommendations

or tips you have as they learn?

One thing I still do

when I'm trying to study something really deeply

is take handwritten notes

it varies

I know there are a lot of people

that take the deep learning courses

during a commute or something

where it may be more awkward to take notes

so I know it may not work for everyone

but when I'm taking courses on Coursera

and I still take some every now and then

the most recent one I took

was a course on clinical trials

because I was interested about that

I got out my little Moleskine notebook

and what I was seeing on my desk

was just taking down notes

so what the instructor was saying

and that act we know that

that act of taking notes

preferably handwritten notes

increases retention.

So as you're sort of watching the video

just kind of pausing maybe

and then taking the basic insights down on paper.

Yeah so there have been a few studies

if you search online

you find some of these studies

that taking handwritten notes

because handwriting is slower

as we're saying just now

it causes you to recode the knowledge

in your own words more

and that process of recoding

promotes long term retention

this is as opposed to typing

which is fine

again typing is better than nothing

or in taking a class

and not taking notes is better

than not taking any class at all

but comparing handwritten notes

and typing

you can usually type faster

for a lot of people

you can handwrite notes

and so when people type

they're more likely to just transcribe

verbatim what they heard

and that reduces the amount of recoding

and that actually results

in less long term retention.

I don't know what the psychological effect

there is but so true

there's something fundamentally different

about writing hand handwriting

I wonder what that is

I wonder if it is as simple

as just the time it takes to write it slower

yeah and because you can't write

as many words

you have to take whatever they said

and summarize it into fewer words

and that summarization process

requires deeper processing of the meaning

which then results in better retention

that's fascinating

oh and I think because of Coursera

I spent so much time studying pedagogy

this is actually one of my passions

I really love learning

how to more efficiently

help others learn

you know one of the things I do

both when creating videos

or when we write the batch is

I try to think is one minute spent of us

going to be a more efficient learning experience

than one minute spent anywhere else

and we really try to you know

make it time efficient for the learners

because you know everyone's busy

so when when we're editing

I often tell my teams

every word needs to fight for its life

and if you can delete a word

let's just delete it and not wait

let's not waste the learning time

let's not waste the learning time

oh that's so it's so amazing

that you think that way

because there is millions of people

that are impacted by your teaching

and sort of that one minute spent

has a ripple effect right

through years of time

which is it's just fascinating to think about

how does one make a career

out of an interest in deep learning

do you have advice for people

we just talked about

sort of the beginning early steps

but if you want to make it

an entire life's journey

or at least a journey of a decade or two

how do you how do you do it

so most important thing is to get started

right and and I think in the early parts

of a career coursework

um like the deep learning specialization

or it's a very efficient way

to master this material

so because you know instructors

uh be it me or someone else

or you know Lawrence Maroney

teaches our TensorFlow specialization

or other things we're working on

spend effort to try to make it time efficient

for you to learn a new concept

so coursework is actually a very efficient way

for people to learn concepts

and the beginning parts of breaking

into a new field

in fact one thing I see at Stanford

some of my PhD students want to jump

in the research right away

and I actually tend to say look

in your first couple years of PhD

and spend time taking courses

because it lays a foundation

it's fine if you're less productive

in your first couple years

you'll be better off in the long term

beyond a certain point

there's materials that doesn't exist in courses

because it's too cutting edge

the course hasn't been created yet

there's some practical experience

that we're not yet that good

as teaching in a course

and I think after exhausting

the efficient coursework

then most people need to go on

to either ideally work on projects

and then maybe also continue their learning

by reading blog posts and research papers

and things like that

doing projects is really important

and again I think it's important

to start small and just do something

today you read about deep learning

feels like oh all these people

doing such exciting things

what if I'm not building a neural network

that changes the world

then what's the point?

Well the point is sometimes building

that tiny neural network

you know be it MNIST or upgrade

to a fashion MNIST to whatever

so doing your own fun hobby project

that's how you gain the skills

to let you do bigger and bigger projects

I find this to be true at the individual level

and also at the organizational level

for a company to become good at machine learning

sometimes the right thing to do

is not to tackle the giant project

is instead to do the small project

that lets the organization learn

and then build out from there

but this is true both for individuals

and for companies

taking the first step

and then taking small steps is the key

should students pursue a PhD

do you think you can do so much

that's one of the fascinating things

in machine learning

you can have so much impact

without ever getting a PhD

so what are your thoughts

should people go to grad school

should people get a PhD?

I think that there are multiple good options

of which doing a PhD could be one of them

I think that if someone's admitted

to a top PhD program

you know at MIT, Stanford, top schools

I think that's a very good experience

or if someone gets a job

at a top organization

at the top AI team

I think that's also a very good experience

there are some things you still need a PhD to do

if someone's aspiration is to be a professor

you know at the top academic university

you just need a PhD to do that

but if it goes to you know

start a company, build a company

do great technical work

I think a PhD is a good experience

but I would look at the different options

available to someone

you know where are the places

where you can get a job

where are the places to get a PhD program

and kind of weigh the pros and cons of those

So just to linger on that for a little bit longer

what final dreams and goals

do you think people should have

so what options should they explore

so you can work in industry

so for a large company

like Google, Facebook, Baidu

all these large sort of companies

that already have huge teams

of machine learning engineers

you can also do with an industry

sort of more research groups

that kind of like Google Research, Google Brain

then you can also do

like we said a professor in academia

and what else

oh you can build your own company

you can do a startup

is there anything that stands out

between those options

or are they all beautiful different journeys

that people should consider

I think the thing that affects your experience more

is less are you in this company

versus that company

or academia versus industry

I think the thing that affects your experience most

is who are the people you're interacting with

in a daily basis

so even if you look at some of the large companies

the experience of individuals

in different teams is very different

and what matters most is not the logo above the door

when you walk into the giant building every day

what matters the most is who are the 10 people

who are the 30 people you interact with every day

so I actually tend to advise people

if you get a job from a company

ask who is your manager

who are your peers

who are you actually going to talk to

we're all social creatures

we tend to become more like the people around us

and if you're working with great people

you will learn faster

or if you get admitted

if you get a job at a great company

or a great university

maybe the logo you walk in is great

but you're actually stuck on some team

doing really work that doesn't excite you

and then that's actually a really bad experience

so this is true both for universities

and for large companies

for small companies you can kind of figure out

who you'll be working with quite quickly

and I tend to advise people

if a company refuses to tell you

who you will work with

someone say oh join us

the rotation system will figure it out

I think that that's a worrying answer

because it because it means you may not get sent

to you may not actually get to a team

with great peers and great people to work with

it's actually a really profound advice

that we kind of sometimes sweep

we don't consider too rigorously or carefully

the people around you are really often

especially when you accomplish great things

it seems the great things are accomplished

because of the people around you

so that's a it's not about the the

where whether you learn this thing

or that thing or like you said

the logo that hangs up top

it's the people that's a fascinating

and it's such a hard search process

of finding just like finding the right friends

and somebody to get married with

and that kind of thing

it's a very hard search

it's a people search problem

yeah but I think when someone interviews

you know at a university

or the research lab or the large corporation

it's good to insist on just asking

who are the people

who is my manager

and if you refuse to tell me