Andrew Ng is 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 deeplearning.ai, Landing.ai, and the AI fund, and was the Chief Scientist at Baidu. As a Stanford professor, and with Coursera and deeplearning.ai, he has helped educate and inspire millions of students including me.

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OUTLINE:
0:00 - Introduction
2:23 - First few steps in AI
5:05 - Early days of online education
16:07 - Teaching on a whiteboard
17:46 - Pieter Abbeel and early research at Stanford
23:17 - Early days of deep learning
32:55 - Quick preview: deeplearning.ai, landing.ai, and AI fund
33:23 - deeplearning.ai: how to get started in deep learning
45:55 - Unsupervised learning
49:40 - deeplearning.ai (continued)
56:12 - Career in deep learning
58:56 - Should you get a PhD?
1:03:28 - AI fund - building startups
1:11:14 - Landing.ai - growing AI efforts in established companies
1:20:44 - Artificial general intelligence

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The following is a conversation with Andrew Ng, 1 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, Landing AI, and the AI Fund, and was the chief scientist at Baidu.

As a Stanford professor, and with Coursera and Deep Learning AI, he has helped educate and inspire millions of students, including me.

This is the Artificial Intelligence Podcast.

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And now here's my conversation with Andrew Ng.

The courses you taught on machine learning at Stanford and later on Coursera that you 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?

Growing up in Hong Kong and Singapore, I started learning to code when I was 5 or 6 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.

And as a result of all that typing, I would get to play these very simple, shoot them up games that, you know, I had implemented on my little computer.

So I thought it was fascinating as a young kid that I could write this code, that was really just copying code from a book into my computer to then play these cool little video games.

Another moment for me was when I was a teenager and my father, because he's a doctor, was reading about expert systems and about neural networks.

So he got me to read some of these books and I thought it was really cool that 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 as an office assistant.

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

So the teenager in 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.

Process of education, try 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, I was 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.

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 10pm and 3am.

A lot of times, launching the first Moocs out of Stanford, we'd already announced the course, about 100,000 people had signed up.

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

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

So many Fridays, Saturdays, I would go out, have dinner with my friends, and then I would think, okay, do I 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'm going to go to my office, go to my tiny little 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.

I think unfortunately that doesn't show that it was recorded that late at night, but it was really inspiring the thought that we could create content to help so many people learn about machine learning.

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 gonna reach sort of thousands of people, eventually millions of people.

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

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

I think 1 thing we, that I'm proud to say we got right from the early days was I told my whole team back then that the number 1 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?

What do 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 1 of the things we got right launched 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 as a guiding principle turned out to be really important to the rise of the MOOC movement.

And the kind of learner you imagined in your mind is as broad as possible, as global as possible.

So really try to reach as many people interested in machine learning and AI as possible.

I really want to help anyone that had an interest in machine learning to break into the field.

And I think sometimes, I've actually had people ask me, hey, why are you spending so much time explaining gradient descent?

And my answer was, if I look at what I think the learner needs and would benefit from, I felt that having that, 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 we've tried to consistently make decisions on that principle.

So 1 of the things you actually revealed to the narrow AI community at the time and to the world is that the amount of people who are actually interested in AI is much larger than we imagined.

By you teaching the class and how popular it became, it showed that, wow, this isn't just a small community of sort of people who go to NeurIPS and it's much bigger, it's developers, it's people from all over the world, I mean, I'm Russian, so everybody in Russia is really interested.

There's a huge number of programmers who are interested in machine learning, India, China, South America, everywhere.

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

So how big do you get a sense that this, the number of people is that are interested from your perspective?

I think it's 1 of those things that maybe it feels like it came out of nowhere, but there's an inside that building it.

It's not 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 then some other things, we had uploaded the whole world and so on.

But it's basically the 1 hour, 15 minute video that we put on YouTube.

And then we had 4 or 5 other versions of websites that I had built, most of which you would never have heard of because they reach small audiences, but that allowed me to iterate, allowed my team and me to iterate, to learn what the ideas that work and what doesn't.

For example, 1 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're logged in and then I want to log in, you need to log out.

But I thought, well, what if 2 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 school in San Francisco.

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 1 example of a tiny lesson learned out of many that allows us to hone into the set of features.

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.

I said, yeah, I saw that you really went through a lot of different features and a lot of ideas and to arrive at the final, at Coursera, the final kind of powerful thing that showed the world that MOOCs can educate millions.

And I think with the whole machine learning movement as well, I think it didn't come out of nowhere.

Instead, what happened was as more people learn about machine learning, they will tell their friends and their friends will see how it's applicable to their work.

I don't know in the future what percentage of all developers will be AI developers.

I could easily see it being north of 50 percent, because so many AI developers broadly construed, not just people doing the machine learning modeling, but the people building infrastructure, data pipelines, you know, all the software is surrounding the core machine learning model.

I feel like today, almost every software engineer has some understanding of the cloud, not all, but maybe it's just my microcontroller developer doesn't need to deal with the cloud.

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

I think in the future, maybe we'll approach nearly 100% of all developers being, you know, in some way an AI developer, or at least having an appreciation of machine learning.

And my hope is that there's this kind of effect that there's people who are not really interested in being a programmer or being into software engineering, like biologists, chemists, and physicists, even mechanical engineers, all these disciplines that are now more and more sitting on large data sets.

And here, they didn't think they're interested in programming until they have this data set and they realize there's this set of machine learning tools that allow you to use the data set.

So they actually become, they learn to program and they become new programmers.

So like the, not just, because you've mentioned a larger percentage of developers become machine learning people, 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, right?

Or maybe we just need a few handful of authors to write the bestsellers, and then no 1 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 1, such as if I send you an email or you send me an email.

I think in computing, we're still in that phase where so few people know how to code that the coders mostly have to code for relatively large audiences.

But if everyone, or most people, became developers at some level, similar to how most people in developed economies are somewhat literate.

I would love to see the owners of a mom and pop store be able to write a little bit of code to customize the TV display for their special this week.

And I think it'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, 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.

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 for data science in their daily lives, in their jobs, may be even larger than the number of people with a concrete use for data science in their daily lives, in their jobs, maybe even larger than the number of people with a concrete use for software engineering.

For example, if you run a small mom and pop store, I think if you can analyze the data about your sales, your customers, I think there's actually real value there, maybe even more than traditional software engineering.

So I find that for a lot of my friends in various professions, be it recruiters or accountants or people that work in 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.

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 1 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 1 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.

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, there's really not, but 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 surprisingly at least for me is great for education.

Like a great talk, I think, is not 1 that has a lot of content.

A great talk is 1 that just clearly says a few simple ideas.

And I think you, the whiteboard somehow enforces that.

Peter Abbeel, who's now 1 of the top roboticists and reinforcement learning experts in the world, was your first PhD student.

So I bring him up just because I kind of imagine this must have been an interesting time in your life.

Do you have any favorite memories of working with Peter, since you're 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 Abue 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.

You know, 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 loops, rows, it's just cool.

So 1 of the most incredible robotics videos ever.

It was like, yeah, so 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 Coates 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 1 of the hardest problems we had was when the helicopter's flying around upside down doing stunts, how do you figure out the position?

Having 1 GPS unit doesn't work because you're flying upside down, GPS unit is facing down, so you can't see the satellites.

So we tried, we experimented trying to have 2 GPS units, 1 facing up, 1 facing down.

So if you flip over, that didn't work because the downward facing 1 couldn't synchronize if you're flipping quickly.

Morgan Quigley was exploring this crazy complicated configuration of specialized hardware to interpret GPS signals.

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

So I remember Peter, great guy, him and me, you know, 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 Coates was crucial to this, was put cameras on the ground and use cameras on the ground to localize a helicopter.

And that solved the localization problem so they were good at 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 1 of the few practical applications of reinforcement learning at the time, which caused it to become pretty well known.

I feel like we might've almost come full circle, where 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 communities 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.

So like it's back to the shredder and the- Yeah.

You know, I like theory, but when I work on theory myself, and this is personal taste, I'm not saying anyone else should do what I do, but when I work on theory, I personally enjoy it more if I feel that the work I do will influence people, have positive impact, or help someone.

I remember when many years ago, I was speaking with a mathematics professor, And it kind of just said, hey, why do you do what you do?

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.

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 1 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, you know, that here's a path for us to help people, I find that more satisfying to have that conviction.

You were a proponent of deep learning before it gained widespread acceptance.

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

What was your thinking process like in that first decade of the, I don't know what that's called, 2000s, the aughts?

Yeah, I can tell you the thing we got wrong and the thing we got right.

The thing we really got wrong was the importance of, the early importance of unsupervised learning.

So early days of Google Brain, we put a lot of effort into unsupervised learning rather than supervised learning.

And there was this argument, I think it was around

after NeurIPS, at that time called NIPS, but now NeurIPS had ended, and Geoff Hinton and I were sitting in the cafeteria outside the conference, we had lunch, we were just chatting, and Jeff pulled out this napkin, he started sketching this argument on the napkin, which was very compelling, I'll repeat it.

Human brain has about a hundred trillion, so that's 10 to the 14 synaptic connections.

You will live for about 10 to the 9 seconds.

You actually live for 2 by 10 to the 9, maybe 3 by 10 to the 9 seconds.

So if each synaptic connection, each weight in your brain's neural network has just a 1 bit parameter.

That's 10 to the 14 bits you need to learn in up to 10 to the 9 seconds of your life.

So via this simple argument, which is a lot of problems, it's very simplified, that's 10 to the 5 bits per second you need to learn in your life.

I am not pointing out 10 to 5 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, you know, 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 no forces argument, you know, we should go into, really convinced me that there's a lot of power to unsupervised learning.

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

I still think unsupervised learning is really important, but in the early days, 10, 15 years ago, a lot of us thought that was the path forward.

Oh, so you're saying that that perhaps was the wrong intuition for the time.

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

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Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73