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 co-founded 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 Ng. 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 little computer. So I thought it was 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, because the 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 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, 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 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. 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.
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 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 it's 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
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. 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. Influences 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 timely 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. 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 Coursera, 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 is applicable to their work. And then the community kept on growing. And I think
it was still growing. I don't know in the future what percentage of our 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,
or maybe it's 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 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 realized there's this set of machine learning tools that allow
you to use the data set. So they actually become, they learned to program, and they become new
programmers. So 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,
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 call 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 the 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 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, 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 of data science in
their daily lives and their jobs may be even larger than the number of people with 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, 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 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
it's 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 you 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.
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 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, you know, actually, even today, the website
heli.stanford.edu, H-E-L-I.stanford.edu is still up. You can watch videos of us using reinforcement
learning to make a helicopter fly upside down, fly loose rows. So it's cool. It's one of the most
incredible robotics videos ever. Some people should watch it. Oh, yeah, thank you. Inspiring.
That's from like 2008 or seven or six, like that range. Something like that. Yeah, so 10 years out.
That was really inspiring to a lot of people. What not many people see is how hard it was.
So Peter and Adam Coates and Morgan Quigley and I were working on various versions of the helicopter.
And a lot of things did not work. For example, turns out one of the hardest problems we had was
when helicopters flying around upside down, doing stones, how do you figure out the position? How do
you localize a helicopter? So we want to try all sorts of things. Having one GPS unit doesn't work
because you're flying upside down. GPS unit is facing down so you can't see the satellite.
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, 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 when 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, you know, 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,
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 the community's 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, will 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? And then he said, he actually,
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, 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 arts?
Yeah. I can tell you the thing we got wrong and the thing we got right. The thing we really got
wrong was 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 New Europe's at that time called NIPS, but now New
Europe's had ended. And Jeff Hinton and I were sitting in the cafeteria outside the conference
where lunch was just chatting. And Jeff 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 by 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. So, and 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. The way 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 in this argument, 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, you know, where the x-axis, y-axis lines going up into the right.
So it's really based on that chart that Adam generated, that he gave me the conviction that
he could scale these models way bigger than what we could on a few CPUs, which is what 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 Thune to pitch, you know,
starting a project at Google, which became the Google Brain project.
You go find Google Brain, and there the intuition was scale will bring performance for the system
so we should chase larger and larger scale. And I think people don't realize how groundbreaking
of it is simple, but it's a groundbreaking idea that bigger datasets 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 were trying to give me a friend and I was like,
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, 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 in 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
dataset, but growing the scale of the dataset is going to lead to even further breakthroughs
in deep learning. And there's currently pushback at that idea that larger datasets 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 datasets 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 datasets that will improve performance?
I think both are important. And it's also problem dependent. So for a few datasets, we may be
approaching 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 datasets 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 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
BERTs. I think scale was the large part of the story. Yeah, that's not often talked about is the scale,
the dataset it was trained on and the quality of the dataset because there's some, so it was like
redded threads that had, they were operated highly. So there's already some weak supervision on a very
large dataset 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,
but then we had, was that CVS subversion, Git, maybe something else in the future.
We're very immature in terms of truth 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 pot 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, deep learning.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,
is sort of, if I ask 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 engineering 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, yeah, thank you. 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 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. Because you teach yourself. I don't teach people.
That's well put. 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, was called Sarah'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 a neural network,
to what does a confnet do, 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 happens.
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 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 the deep learning specialization a bit
easier, but it's also possible to jump into the deep learning specialization directly,
but it'll 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'll cover 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 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 dataset, to build the intuition about
it without building it up too big to where you spend, like I 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 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 did 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, so we learn how to debug. And I think in machine learning, the way you debug,
machine learning program is very different than the way you do binary search or whatever,
use a debugger, trace through the code in the traditional software engineering.
So it isn't 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 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, 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 is the biggest challenge for them wants 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 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 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 enforcement 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 a lot of the inspired fire and passion
in people's eyes 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 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 Neurorefs, a bunch of us were standing around saying, hey, what's your
best example of an actual deploy 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 know 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, 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. I think reinforcement learning should
be in that portfolio, and 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 helps 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 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 these parts 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. 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 datasets 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,
what behavior you take an image, cuts it up into a three by three grid. So like a nine, three by
three puzzle piece, jump out 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 Beesman
doing some work on this too, 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 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 learning is starting to get
traction. We're very close to it being useful. Well, web 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.
Other unsupervised learning, the things that I worked on have 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 studying
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.ai's 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 as 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
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 the 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 loads. But this would be so much harder if we
have to make a decision every morning. And 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 wear my shirt.
So if 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, 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 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 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 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 Coursera and I
still take some every now and then, the most recent one I took was a course on clinical trials.
I was just interested about that. I got out my little Moscan notebook and I was sitting in my desk
just taking down notes of what the instructor was saying. 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 actually,
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. OK, typing is better than nothing,
right? And 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 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 it's so true. There's something fundamentally different about writing
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 with us going to be a more efficient
learning experience than one minute spent anywhere else? And we really try to make it
time efficient for the learners because I 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 let's not waste the learner's 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 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 course
work, like the deep learning specialization, it's a very efficient way to master this material.
So because instructors, be it me or someone else, or 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 and the beginning parts of break 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 course isn't being created yet, there's some practical experience that
were 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 with 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'd be working with quite quickly. And I tend to advise people,
if a company refuses to tell you who you work with, someone 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 an hour-long
talk on career advice, including on the job search process and some of these. So if you can find
those videos online. Awesome. And 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 that's
evolved with what's your advice and how does one build a successful AI startup?
You know, in Silicon Valley, a lot of startup 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
who 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, customer obsessed, 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 a lot of 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
those can be great things to do either in the academic setting or in the corporate setting or
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 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 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 with AI, I think the ability to build new teams to go
after this rich space of opportunities is 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 just launch us 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 startups?
I think we've been constantly improving and iterating on our processes, but 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
the specialized domain knowledge, be it in education or healthcare or 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 figuring this out.
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 and a couple 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 helping, 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. 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 AFN 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 at it and 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 work
of 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 still 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 study estimating $13 trillion of global economic growth.
US 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 would 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 will 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
agriculture, 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 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
thus help 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. 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. So that changed stuff. So increasing our algorithm
makes robustness. So all the changes happen in the factory. I find that we run a lot of practical
problems that are not as widely discussed in academia. It's really fun being on the cutting
edge, solving these problems before maybe before many people are even aware that there is a problem
there. 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? What's the early journey that you recommend that you see these companies
taking? I published a document called the AI Transformation Playbook online and taught briefly
in the AI for Everyone course on course era about the long-term journey that companies 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, neural nets tried it, didn't work,
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, but 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 fave and deep learning. My second internal customer was the Google Maps team,
where we used computer vision to read health 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 show that it works in these cases, and it just
propagates through the entire company that 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 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 Jupiter
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 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 then 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 deploy something in a factory, maybe they chop
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, especially in academia,
we don't know how to deal with test set distributions that are dramatically different
than the training set distribution. There's research, there's stuff like domain annotation,
transfer learning. 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 work
loads. 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 when
these are happening. So I think what Lambda AI has become good at, and then I think we learned by
making the steps and painful experiences, or might even, what would become good at is working
with our partners to think through all the things beyond just the machine learning model
running that you put in a notebook, but to build the entire system, manage the change process,
and figure out how to deploy this in a 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, a lounge 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 depth ops or the MO ops 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 and 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 and your work and our discussion today has been on applied AI. Maybe you can even call
narrow AI where the goals 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 super human level intelligence. Is this something you dream
of as well? Do you think we human beings will ever build a human level intelligence or super
human level intelligence system? I would love to get the AGI, and I think humanity will. But
whether it takes 100 years or 500 or 5,000, I find hard to estimate. Do you have, so 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 AI systems with the values of us humans, sort of 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? Who do I crash into? So I think
self-driving cars will run into that problem roughly as often as we do when we drive our cars.
The biggest problem with self-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 self-driving car
fails and it crashes into it. So I think we need to solve that problem for us.
I think the problem with some of these discussions about AGI alignment, the paperclip problem,
is that there's 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. The 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,
we've affected every industry. So the internet industry has a lot of win-and-take modes that
win-and-take all dynamics, but we've infected all these other industries. So we're also giving these
other industries win-and-take modes and 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. So we're creating tremendous wealth,
but how do we make sure that the wealth is fairly shared? And then how do we help people whose jobs
are displaced? 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. There are adverse uses 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 something much harder problems. Yeah, they overshadow the problems
that we have already today. They're 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
Jupyter Notebook and something that actually transforms an entire industry potentially.
Yeah, and I think, and just to some companies, our 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 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, I could have been so much better.
But I discovered that book had not yet been written, we're 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 spend 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 was 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 today. No, thank you so much. Thanks.
Thanks for listening to this conversation with Andrew Aang. And thank you to our
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some words of wisdom from Andrew Aang. Ask yourself, if what you're working on
succeeds beyond your wildest dreams, would you 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.