The following is a conversation with Andrew Ang, 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, Lending 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.

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And now here's my conversation with Andrew Eng. The courses you taught on machine learning

at Stanford and later on Coursera that you co founded 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?

There's 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 I 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 on my little computer. So I thought it's fascinating as a young

kid that I could write this code that's really just copying code from a book into my computer

to then play these cooler video games. Another moment for me was when I was a teenager and my

father was 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 I was

office assistant. And the highlight of my job was when I got to use the shredder. So the teenager

of me remember 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.

The process of education tried 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

out of Stanford with 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 we

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

Fridays, Saturdays, I would go out and have dinner with my friends and then I would think,

okay, 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, okay, I want to go to my office, go to my tiny recording studio, I would adjust my logic

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 later

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, is 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 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, you know, 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're

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 grade and descent? And my answer was, if I look at what I think the learner needs and

what benefit from, I felt that having a good understanding of the foundations, coming back

to the basics, would put them in a better state to then build on a long term career.

So try to consistently make decisions on that principle.

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 people who go to New York 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 numbers grown over time. I think one of those things that maybe it feels

like it came out of nowhere, but it's an insight that building 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 of the

things we had uploaded, the whole works and so on, but basically the one hour 15 minute video that

we put on YouTube. Then we had four or five other versions of websites that had built,

most of which you would never have heard of because they reached small audiences,

but that allowed me to iterate, allow 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. 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. Inferences feature rolled it out in a high in a 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. And 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,

doesn't actually have the impact that you think it might have. So yeah, I saw that you've really

went through a lot of different features and a lot of ideas to arrive at the final, at Corsair,

at 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'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 more for 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 doesn't need to do the

cloud. But I feel like the vast majority of software engineers today are sort of having

the patience to cloud. I think in the future, maybe we're approaching nearly 100% of all developers

being in some way an AI developer, 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, 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, you know, could read and write. And maybe you thought maybe not everyone

needs to learn to read and write. You know, you just go listen to a few monks read to you,

and maybe that was enough, or maybe we just need a few handful of authors to write the best sellers

and then no one else needs to write. But what we found was that by giving as many people, you know,

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 advice, engine 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'll

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, where, yeah, 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 interesting thing, you know, 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, you know, 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 of data science

in their daily lives and their jobs may be even larger than the number of people with a concrete

use for software engineering. For example, if you actually 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, you know, people that

work in 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. 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,

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 they're frozen columns. 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. So some of your talks

involve the whiteboard. I mean, it's really not, 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 to 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 Abiel, who's now one of the

top roboticists and reinforcement learning experts in the world, was your first PhD student.

And so I bring him up just because I kind of imagine this was must have been an interesting time in

your life. Do you have any favorite memories of working with Peter, your first student in those

uncertain times, especially before deep learning really, really sort of blew up any favorite

memories from those times? Yeah, I was really fortunate to have had Peter Abiel 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, you can watch videos of us using reinforcement learning to make a helicopter

fly upside down, fly loops, 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. Something like that. It was over 10 years old. That was really inspiring

to a lot of people. What not many people see is how hard it was. So Peter and Adam Codes 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

is flying around upside down doing stunts, how do you figure out the position? How do you localize

the helicopter? So we want to try all sorts of things. Having one GPS unit doesn't work because

you're flying upside down. The GPS unit is 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, it's completely insane. Spent about a year working on that. Didn't work. So I remember

Peter, a 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, you know, done it like what now? Because we tried so many

things and it just didn't work. In the end, what we did, and Adam Codes 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, you know, I'll remind it, 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 for flying helicopters was one of the few, you know,

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 Kunhe has come up with.

What was the drive, sort of in the face of the fact that most people are doing theoretical work,

what motivate 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 he kind of just said, hey, why do you do what you do? And then he said, he actually 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, he said. 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. 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

Rain, 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, you know, New Europe's at that time

called NIPPS, but now New Europe's had ended. And Geoff Hinton and I were sitting in the

cafeteria outside the conference where lunch was just chatting. And Geoff pulled up this napkin,

he started sketching this argument on the napkin. It was very compelling as I'll repeat it.

Human brain has about 100 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 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. I think I'm a very

loving parent, but I'm just not going to 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, just experiences

in the world. And so that argument, and there are a lot of known forces argument,

going to really convince 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

they could make this thing the better performance accuracy as a vertical axis. So it's really

based on that chart that Adam generated that he gave me the conviction that we could scale these

models way bigger than what we could on a few CPUs, which is where we had a 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 Thun to pitch starting a project at Google,

which became the Google Brain project. Brain, you go find 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 don't realize how, how groundbreaking of it is 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, you know,

senior people in the machine learning community, I won't name, but who's people, some of whom we

know. My well meaning friends came and we're trying to give me a friend and say, Hey, Andrew,

why are you doing this? This is crazy. It's in the near and after architecture. Look at these

architectures of building. You just want to go for scale. Like there's a bad career move. So my

well meaning friends, you know, we're trying to, 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

there. So you want to increase the efficiency of learning. You want to make better learning

mechanisms. And I personally believe that just 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 is 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,

you know, Bayes error rate or approaching or surpassing human level performance. And then

there's that theoretical ceiling that we will never surpass or 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 average innovation will be sufficient to take us further. But on the flip

side, if we look at the recent breakthroughs using your transformer networks or language models,

it was a combination of novel architecture, but also scale had a lot to do with it. We look at

what happened with GP2 and BERT. I think scale was the 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 and email,

you know, but then we had was that CVS subversion, Git, maybe something else in the future.

We're very immature in terms of tools for managing data and thinking about the clean data and how

the soft, 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, during the work in visual inspection, we will take, say a plastic pot and show it to

one inspector. And the inspector, sometimes very opinionated, they'll go, clearly, that's a defect.

The scratch unacceptable. Gotta reject this pot. 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

going to make us, you know, and then sometimes you take the same plastic pot, show it to the same

inspector in the afternoon, I suppose, in the morning, and very opinionated go in the morning to

say, clearly, it's okay in the afternoon, equally confident. Clearly, this is a defect. And so what

does the AI team supposed to do if sometimes even one person doesn't agree with himself or

himself 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 the case of other industry settings, we don't have big data.

If you're just a small data, very small data sets, maybe in the 100 defective parts,

or 100 examples of a defect. If you have only 100 examples, these little labeling errors,

you know, 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 hardware 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 deep learning.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

deep learning AI is for education of everyone else or of individuals interested in of 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 causes to help people break into AI. So

my machine learning course that I taught through Stanford means one of the most popular causes

on course era. 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 get you

interested, it always goes back to and you're at some point. 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, thank you. I was once reading a news article. I think it was tech review and I'm going to

mess up the statistic. But I remember reading an article that said something like one third of our

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 because you

teach yourself, I don't teach people. That's well played. So 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 deep learning.ai is I think what it was called Sarah's talk 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 confnet to what is a

RNN or 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. 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 that's great. So a little basic linear algebra is great.

Basically linear algebra, even very, very basically 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 we covered most 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 know how of how to actually make these things work. So what are the differences between

different optimization algorithms? What do you do with the algorithm overfit? So 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 know how 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 that equally

important or maybe even more important part of coding is to understand how to string together

these lines of code and to 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 that is 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, so 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,

you know, like do binary search or whatever, use a debug, trace through the code in traditional

software engineering. So as 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,

learning, improving, sort of going into the questions of overfitting and all those kinds of

things. That's, that's the logical space that the debugging is happening in with neural networks.

Yeah, the often 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, you know, different types of data. So to answer those

questions systematically, so that you don't hitting down the, so 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 this is the biggest challenge for them wants to get over that hill. It's,

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 you have confnets, hopefully you have enough intuitions from the earlier sections

to then understand why we structure confnets in a certain way and then eventually why we

build 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 the 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 deeper 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 and 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 not?

I still teach reinforcement learning in one of my Stanford classes, and my PhD thesis was on

reinforcement learning, so I currently love the field. 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. 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, at least today, outside of playing video games

in a few of the games, the scope. Actually, at Neurov's, a bunch of us were standing around

saying, hey, what's your best example of an actual deployment of reinforcement learning

application among senior machine learning researchers? Again, there are some emerging

ones, but there are not that many great examples.

Well, I think you're absolutely right. The sad thing is there hasn't been a big application

impactful real world application 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 and it all 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 on the real world

setting. 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. 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. 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 rural impact. I think reinforcement learning should be in that portfolio and then

it's about balancing how much we teach all of these things. The world should have diverse

skills. It would be sad if everyone just learned one narrow thing. Yeah, the diverse skill helped

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 with an unlimited budget

and didn't have to worry about short term impact and only focus on long term impact,

I've really spent all my time doing research on unsupervised learning. I still think unsupervised

learning is a beautiful idea. At both this past in Europe and ICML, I was attending workshops or

listening to various talks about self supervised learning, which is one vertical segment, maybe

of sort of unsupervised learning that I'm excited about. Maybe just to summarize the idea. I guess

you know the idea we'll describe briefly. No, please. So here's an 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 simply rotated 90 degrees, 180 degrees, 270 degrees, or zero degrees. So you can generate

an infinite amount of labeled data because you rotated the image so you know what's the

drunk 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 to read the 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

to Facebook, Google, Brain, I think DeepMind. Oh, actually, Aaron Vendorold has great work on the

CPC objective. So many teams are doing exciting work. And I think this is a way to generate

infinite labeled 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 learning is starting to get traction. We're very

close to it being useful. Well, word embeddings are 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 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 the steam of ideas and other ideas to come up with better algorithms.

So if we could return to maybe talk quickly about the specifics of deeplearning.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 finish it in less than a month by working more intensely and study

more intensely. So it really depends on the individual. When we created the deep learning

specialization, we wanted to make it very accessible and very affordable. And with

Coursera and deep learning 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 that 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 and 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

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. So and then actually, that's the reason why we're the same thing every day as well.

It's just one less decision. I just get up and where I prefer it. So if I think 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 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's adds up over time.

Yeah. And I think it's often not about the burst 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 isn't 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 100 papers and you actually learn a lot when you read 100 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 course error, 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 of my little Moscan notebook and I was sitting in my desk

just taking down notes of what the instructor was saying. And 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 that you can hand write notes. And so when people

type, they're more likely to just try to strive 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 it's 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 is 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.

And I've spent, I think because of Coursera, I've spent so much time studying pedagogy.

It's actually one of my passions. I really love learning how to more efficiently help others

learn. Yeah, 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 make a time efficient for the learners,

because you know, everyone's busy. So when we're editing, I often tell my teams,

every word needs to fight for his life. And if you can delete a word, this is deleted and not

wait, let's not waste the learners time. That's so amazing that you think that way. Because

there is millions of people, they're impacted by your teaching and sort of that one minute spent

has a ripple effect, right? Through years of time, which is 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 do it? So most important thing is to get

started. Right. And I think in the early parts of a career coursework, like the deep learning

specialization, or it's a very efficient way to master this material. So because, you know,

instructors, be it me or someone else, or, you know, Lawrence Maroney teaches intensive

specialization or other things we're working on, spend effort to try to make it time efficient for

you to learn new concepts. So coursework is actually a very efficient way for people to learn

concepts in the beginning parts of breaking into new fields. In fact, one thing I see at Stanford,

some of my PhD students want to jump in the research right away and actually tend to say,

look, in your first couple years of PhD, 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 courses we've 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. If you're like, oh, all these people are doing such exciting

things, whether 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, be it MNIST or upgrade to fashion

MNIST to whatever, 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 up 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 of 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 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 at the top academic university, you just need a PhD to do that.

But if it goes to start a company, build a company, do great technical work, I think

PhD is a good experience. But I would look at the different options available to someone.

Where are the places where you can get a job? Where are the places you can get in a PhD program

and weigh the pros and cons of those? Just to linger on that for a little bit longer,

what final dreams and goals do you think people should have? What options should they explore?

So you can work in industry for a large company, like Google, Facebook, Baidu,

all these large companies that already have huge teams of machine learning engineers.

You can also do with an industry more research groups, 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,

Moses, 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 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 be working with quite quickly. And I tend to advise people,

if a company refuses to tell you who you work with, so you can say, oh, join us. The rotation

system will figure it out. I think that that's a worrying answer 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 it's not about 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 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, I'm going to

think, well, maybe that's because you don't have a good answer. It may not be someone I like.

And if you don't particularly connect, if something feels off with the people,

then don't stick to it. That's a really important signal to consider.

Yeah. And actually, in my standard class, CS230, as well as an ACM talk, I think I gave like a

hour long talk on career advice, including on the job search process and then some of these.

So if you can find those videos online. Awesome. And I'll point them. I'll point people to them.

Beautiful. So the AI fund helps AI startups get off the ground. Or perhaps you can elaborate on all

the fun things it's evolved with. What's your advice and how does one build a successful AI startup?

You know, in Silicon Valley, a lot of start up failures come from building other products that

no one wanted. So when, you know, cool technology, but who's going to use it? So

I think I tend to be very outcome driven and customer obsessed. Ultimately, we don't get

to vote if we succeed or fail. It's only the customer that they're the only one that gets a

thumbs up or thumbs down vote in the long term. In the short term, you know, there are various

people that get various votes, but in the long term, that's what really matters.

So as you build this startup, you have to constantly ask the question,

will the customer give a thumbs up on this?

I think so. I think startups that are very customer focused, customers that

deeply understand the customer and are oriented to serve the customer are more likely to succeed.

With the provisional, I think all of us should only do things that we think

create social good and move the world forward. So I personally don't want to build

addictive digital products just to sell off ads. There are things that could be lucrative that I

won't do. But if we can find ways to serve people in meaningful ways, I think those can be great

things to do, either in an academic setting or in a corporate setting or a startup setting.

So can you give me the idea of why you started the AI fund?

I remember when I was leading the AI group at Baidu, I had two jobs, two parts of my job. One was

to build an AI engine to support the existing businesses, and that was running, just ran,

just performed by itself. The second part of my job at the time was to try to systematically

initiate new lines of businesses using the company's AI capabilities. So, you know,

the self driving car team came out of my group, the smart speaker team, similar to what is

Amazon Echo or Alexa in the US, but we actually announced it before Amazon did. So Baidu wasn't

following Amazon. That came out of my group, and I found that to be actually the most fun part of

my job. So what I want to do was to build AI fund as a startup studio to systematically create new

startups from scratch. With all of the things we can now do of AI, I think the ability to build new

teams to go after this rich space of opportunities is a very important way, a very important mechanism

to get these projects done that I think will move the world forward. So I've been fortunate to build

a few teams that had a meaningful positive impact, and I felt that we might be able to do this in a

more systematic, repeatable way. So a startup studio is a relatively new concept. There are

maybe dozens of startup studios right now, but I feel like all of us, many teams are still trying

to figure out how do you systematically build companies with a high success rate. So I think

even a lot of my venture capital friends seem to be more and more building companies rather than

investing in companies. But I find a fascinating thing to do, to figure out the mechanisms by

which we could systematically build successful teams, successful businesses in areas that we

find meaningful. So a startup studio is a place and a mechanism for startups to go from zero to

success. So try to develop a blueprint. It's actually a place for us to build startups from

scratch. So we often bring in founders and work with them or maybe even have existing ideas

that we match founders with. And then this launches, hopefully into successful companies.

So how close are you to figuring out a way to automate the process of starting from scratch

and building successful AI startup? Yeah, I think we've been constantly improving and iterating on

our processes, how we do that. So things like how many customer calls do we need to make in order

to get customer validation? How do we make sure this technology can be built? Quite a lot of our

businesses need cutting edge machine learning algorithms. So kind of algorithms have developed

in the last one or two years. And even if it works in a research paper, it turns out taking

the production is really hard. There are a lot of issues, but making these things work in the real

life that are not widely addressed in academia. So how do we validate that this is actually doable?

How do we build a team, get specialized domain knowledge, be it in education or healthcare,

whatever sector we're focusing on? So I think we've actually been getting much better at

giving the entrepreneurs a high success rate. But I think the whole world is still in the

early phases. But do you think there are some aspects of that process that are transferable

from one startup to another, to another, to another? Yeah, very much so. Starting a company

to most entrepreneurs is a really lonely thing. And I've seen so many entrepreneurs not know how

to make certain decisions like when do you need to, how do you do BDP sales? If you don't know

that, it's really hard. Or how do you market this efficiently other than buying ads, which is

really expensive. Are there more efficient tactics to that? Or for a machine learning project,

basic decisions can change the course of whether machine learning product works or not. And so

there are so many hundreds of decisions that entrepreneurs need to make. And making a mistake

in a couple of key decisions can have a huge impact on the fate of the company.

So I think a startup studio provides a support structure that makes starting a company much

less of a lonely experience. And also, when facing with these key decisions, like trying to hire your

first VP of engineering, what's a good selection criteria? How do you solve? Should I hire this

person or not? By having our ecosystem around the entrepreneurs, the founders to help, I think we

help them at the key moments and hopefully significantly make them more enjoyable and

then higher success rate. So they have somebody to brainstorm with in these very difficult

decision points. And also to help them recognize what they may not even realize is a key decision

point. That's the first and probably the most important part, yeah. I can say one other thing.

You know, I think building companies is one thing, but I feel like it's really important that we

build companies that move the world forward. For example, within the AFUN team, there was once an

idea for a new company that if it had succeeded, would have resulted in people watching a lot

more videos in a certain narrow vertical type of video. I looked at it, the business case was fine,

the revenue case was fine, but I looked and I just said, I don't want to do this. I don't actually

just want to have a lot more people watch this type of video. It wasn't educational, it was an

educational baby. And so I code the idea on the basis that I didn't think it would actually

help people. So whether building companies or working enterprises or doing personal projects,

I think it's up to each of us to figure out what's the difference we want to make in the world.

With Lending AI, you help already established companies grow their AI and machine learning

efforts. How does a large company integrate machine learning into their efforts?

AI is a general purpose technology and I think it will transform every industry. Our community has

already transformed to a large extent the software internet sector. Most software internet companies

outside the top right 506 or 304 already have reasonable machine learning capabilities or

getting there. It's the room for improvement. But when I look outside the software internet sector,

everything from manufacturing, every culture, healthcare, logistics, transportation, there's

so many opportunities that very few people are working on. So I think the next wave for AI

is first also transform all of those other industries. There was a McKinsey company,

estimating $13 trillion of global economic growth. U.S. GDP is $19 trillion. So $13 trillion is a big

number. Or PwC has been $16 trillion. So whatever number is this large. But the interesting thing

to me was a lot of that impact will be outside the software internet sector. So we need more teams

to work with these companies to help them adopt AI. And I think this is one of the things that

help drive global economic growth and make humanity more powerful.

And like you said, the impact is there. So what are the best industries, the biggest industries

where AI can help perhaps outside the software tech sector?

Frankly, I think it's all of them. Some of the ones I'm spending a lot of time on are manufacturing,

every culture, looking to healthcare. For example, in manufacturing, we do a lot of work in visual

inspection. Where today, there are people standing around using the human eye to check if, you know,

this plastic part or the smartphone or this thing has a scratch or a dent or something in it.

We can use a camera to take a picture, use a deep learning and other things to check if it's

defective or not. And does our factories improve yield and improve quality and improve throughput.

It turns out the practical problems we run into are very different than the ones you might read

about in most research papers. The data sets are really small. So we face small data problems.

You know, the factories keep on changing the environment. So it works well on your test set.

But guess what? Something changes in the factory. The lights go on or off. Recently,

there was a factory in which a bird threw through the factory and pooped on something.

And so that changed stuff. And so increasing our algorithm to make robustness to all the changes

happened in the factory. I find that we run a lot of practical problems that are not as widely

discussed in academia. And it's really fun kind of being on the cutting edge solving these problems

before, you know, maybe before many people are even aware that there is a problem there.

And that's such a fascinating space. You're absolutely right. But what is the first step

that a company should take? It's just a scary leap into this new world of going from the human eye

inspecting to digitizing that process, having a camera, having an algorithm. What's the first step?

Like, what's the early journey that you recommend that you see these companies taking?

I published a document called the AI Transformation Playbook. There's online and taught briefly in

the AI for Everyone course on course era about the long term journey that a company should take.

But the first step is actually to start small. I've seen a lot more companies fail by starting

too big than by starting too small. Take even Google. Most people don't realize how hard it was

and how controversial it was in the early days. So when I started Google Brain, it was controversial.

People thought deep learning near and at tried it, didn't work. Why would you want to do deep learning?

Why would you want to do deep learning? So my first internal customer in Google was the Google

Speech Team, which is not the most lucrative project in Google, not the most important.

It's not web search or advertising. But by starting small, my team helped the speech team

build a more accurate speech recognition system. And this caused their peers, other teams to start

to have more faith in deep learning. My second internal customer was the Google Maps team,

where we used computer vision to read host numbers from basic street view images to more

accurately locate houses with Google Maps to improve the quality of the geodata.

And it was only after those two successes that I then started a more serious conversation with

the Google Ads team. And so there's a ripple effect that you showed that it works in these

in these cases. And they just propagates through the entire company that this

this thing has a lot of value and use for us. I think the early small scale projects,

it helps the teams gain faith, but also helps the teams learn what these technologies do.

I still remember when our first GPU server, it was a server under some guy's desk,

and you know, and then that taught us early important lessons about how do you have multiple

users share a set of GPUs, which is really not obvious at the time. But those early lessons

were important. We learned a lot from that first GPU server that later helped the teams think through

how to scale it up to much larger deployments. Are there concrete challenges that companies face

that the UC is important for them to solve? I think building and deploying machine learning

systems is hard. There's a huge gap between something that works in a Jupyter notebook on

your laptop versus something that runs in a production deployment setting in a factory

or a culture plant or whatever. So I see a lot of people, you know, get something to work on

your laptop and say, wow, look what I've done. And that's great. That's hard. That's a very

important first step. But a lot of teams underestimate the rest of the steps needed.

So for example, I've heard this exact same conversation between a lot of machine learning

people and business people. The machine learning person says, look, my algorithm does well on the

test set. And it's a clean test set. I didn't peek. And the machine in the business person says,

thank you very much, but your algorithm sucks. It doesn't work. And the machine learning person

says, no, wait, I did well on the test set. And I think there is a gulf between what it takes to

do well on a test set on your hard drive versus what it takes to work well in a deployment setting.

Some common problems, robustness and generalization, you know, you deploy something in a factory,

maybe they chopped down a tree outside the factory so the tree no longer covers the

window and the lighting is different. So the test set changes. And in machine learning,

and especially in academia, we don't know how to deal with test set distributions that are

dramatically different than the training set distribution. This research, this stuff like

domain annotation, transfer learning, you know, there are people working on it, but we're really

not good at this. So how do you actually get this to work because your test set distribution is

going to change? And I think also, if you look at the number of lines of code in the software system,

the machine learning model is maybe 5% or even fewer relative to the entire software system

you need to build. So how do you get all that work done and make it reliable and systematic?

So good software engineering work is fundamental here to building a successful small machine

learning system? Yes. And the software system needs to interface with people's workloads. So

machine learning is automation on steroids. If we take one task out of many tasks that are done

in the factory, so the factory does lots of things. One task is visual inspection. If we

automate that one task, it can be really valuable, but you may need to redesign a lot of other tasks

around that one task. For example, say the machine learning algorithm says this is defective. What

are you supposed to do? Do you throw it away? Do you get a human to double check? Do you want to

rework it or fix it? So you need to redesign a lot of tasks around that thing you've now automated.

So planning for the change management and making sure that the software you write is consistent

with the new workflow. And you take the time to explain to people what needs to happen. So I think

what Lambda AI has become good at, and I think we learned by making the steps and

painful experiences, or what would become good at is working upon this to think through

all the things beyond just the machine learning model that you put in a notebook,

but to build the entire system, manage the change process, and figure out how to deploy

this in the way that has an actual impact. The processes that the large software tech companies

use for deploying don't work for a lot of other scenarios. For example, when I was leading large

speech teams, if the speech recognition system goes down, what happens? Well, allowance goes off,

and then someone like me would say, hey, you 20 engineers, please fix this. But if you have a

system go down in the factory, there are not 20 machine learning engineers sitting around,

you can page a duty and have them fix it. So how do you deal with the maintenance or the

DevOps or the MOOps or the other aspects of this? So these are concepts that I think Lambda AI and

a few other teams on the cutting Asia, but we don't even have systematic terminology yet to

describe some of the stuff we do, because I think we're indenting it on the fly.

So you mentioned some people are interested in discovering mathematical beauty and truth in

the universe, and you're interested in having a big positive impact in the world. So let me ask

you. The two are not inconsistent. No, they're all together. I'm only half joking because

you're probably interested a little bit in both. But let me ask a romanticized question. So much

of the work, your work and our discussion today has been on applied AI. Maybe you can even call

narrow AI, where the goal is to create systems that automate some specific process that adds a

lot of value to the world. But there's another branch of AI starting with Alan Turing that kind

of dreams of creating human level or superhuman level intelligence. Is this something you dream

of as well? Do you think we human beings will ever build a human level intelligence or superhuman

level intelligence system? I would love to get the AGI, and I think humanity will. But whether it

takes 100 years or 500 or 5000, I find hard to estimate. Some folks have worries about the

different trajectories that path would take, even existential threats of an AGI system. Do you have

such concerns, whether in the short term or the long term? I do worry about the long term fate

of humanity. I do wonder as well. I do worry about overpopulation on the planet Mars, just not

today. I think there will be a day when maybe someday in the future, Mars will be polluted,

there are all these children dying, and someone will look back at this video and say,

Andrew, how is Andrew so heartless? He didn't care about all these children dying on the planet

Mars. And I apologize to the future viewer. I do care about the children, but I just don't know

how to productively work on that today. Your picture will be in the dictionary for the people

who are ignorant about the overpopulation on Mars. Yes. So it's a long term problem. Is there

something in the short term we should be thinking about in terms of aligning the values of our AGI

systems with the values of us humans? Something that Stuart Russell and other folks are thinking

about as this system develops more and more, we want to make sure that it represents the

better angels of our nature, the ethics, the values of our society.

You know, if you take cell driving cars, the biggest problem with cell driving cars is not

that there's some trolley dilemma and you teach this. So, you know, how many times when you are

driving your car, did you face this moral dilemma as food I trash into? So I think

the cell driving cars will run into that problem roughly as often as we do when we drive our cars.

The biggest problem with cell driving cars is when there's a big white truck across the road

and what you should do is break and not crash into it and the cell driving car fails and it

crashes into it. So I think we need to solve that problem first. I think the problem with some of

these discussions about AGI, you know, alignments, the paperclip problem is that is a huge distraction

from the much harder problems that we actually need to address today. Some of the hard problems

need to address today. I think bias is a huge issue. I worry about wealth and equality.

AI and internet are causing an acceleration of concentration of power because we can now

centralize data, use AI to process it and so industry after industry will affect every industry.

So the internet industry has a lot of win and take, most of win and take all dynamics,

both with infected all these other industries. So also giving these other industries win and

take most of win and take all flavors. So look at what Uber and Lyft did to the taxi industry.

So we're doing this type of thing. It's a lot. So this, so we're creating tremendous wealth,

but how do we make sure that the wealth is fairly shared? I think that and how do we help people

whose jobs are displaced? You know, I think education is part of it. There may be even more

that we need to do than education. I think bias is a serious issue. They're adverse

users of AI like deep fakes being used for various nefarious purposes. So I worry about

some teams maybe accidentally and I hope not deliberately making a lot of noise about

things that problems in the distant future rather than focusing on sometimes the much

harder problems. Yeah, overshadow the problems that we have already today that are exceptionally

challenging. Like those you said, and even the silly ones, but the ones that have a huge impact,

which is the lighting variation outside of your factory window that ultimately is what makes

the difference between like you said, the Jupiter notebook and something that actually

transforms an entire industry potentially. Yeah. And I think, and just to some companies

or regulator comes to you and says, look, your product is messing things up. Fixing it may have

a revenue impact. Well, it's much more fun to talk to them about how you promise not to wipe out

humanity and to face the actually really hard problems we face. So your life has been a great

journey from teaching to research to entrepreneurship. Two questions. One, are there regrets moments

that if you went back, you would do differently? And two, are there moments you're especially proud

of moments that made you truly happy? You know, I've made so many mistakes. It feels like every

time I discover something, I go, why didn't I think of this, you know, five years earlier or even

10 years earlier? And as recently, and then sometimes I read a book and I go, I wish I read

this book 10 years ago, my life would have been so different. Although that happened recently.

And then I was thinking, if only I read this book, when we're starting up called Sarah,

it could have been so much better. But I discovered the book had not yet been written

or starting called Sarah. So that made me feel better. But I find that the process of discovery,

we keep on finding out things that seem so obvious in hindsight. But it always takes us so

much longer than I wish to figure it out. So on the second question, are there moments in your

life that if you look back that you're especially proud of or you're especially happy that filled

you with happiness and fulfillment? Well, two answers. One, that's my daughter Nova.

Yes, of course. Because I know how much time I spent with her. I just can't spend enough time with her.

Congratulations, by the way. Thank you. And then second is helping other people. I think to me,

I think the meaning of life is helping others achieve whatever are their dreams.

And then also, to try to move the world forward by making humanity more powerful as a whole.

So the times that I felt most happy, most proud with when I felt someone else

allowed me the good fortune of helping them a little bit on the path to their dreams.

I think there's no better way to end it than talking about happiness and the meaning of life.

So it's true. It's a huge honor. Me and millions of people, thank you for all the work you've done.

Thank you for talking to me today. Thank you so much. Thanks.

Thanks for listening to this conversation with Andrew Eng and thank you to our presenting

sponsor, Cash App. Download it, use code LEX Podcast. You'll get $10 and $10 will go to first,

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Podcasts, support on Patreon, or simply connect with me on Twitter at Lex Freedman.

And now let me leave you with some words of wisdom from Andrew Eng.

Ask yourself if what you're working on succeeds beyond your wildest dreams,

which have significantly helped other people. If not, then keep searching for something else to

work on. Otherwise, you're not living up to your full potential. Thank you for listening

and hope to see you next time.