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

The following is a conversation with Andrew Ng, one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. He 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. If you enjoy it, subscribe on YouTube, give it five stars on Apple Podcast, support it on Patreon, or simply connect with me on Twitter @lexfridman, spelled F-R-I-D-M-A-N. As usual, I'll do one or two minutes of ads now and never any ads in the middle that can break the flow of the conversation. I hope that works for you and doesn't hurt the listening experience. This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as $1. Broker services are provided by Cash App Investing, a subsidiary of Square and member SIPC. Since Cash App allows you to buy Bitcoin, let me mention that cryptocurrency in the context of the history of money is fascinating. I recommend A Cent of Money as a great book on this history. Debits and credits on ledgers started over 30,000 years ago. The US dollar was created over 200 years ago. And Bitcoin, the first decentralized cryptocurrency, released just over 10 years ago. So given that history, cryptocurrency is still very much in its early days of development, but it's still aiming to and just might redefine the nature of money. So again, if you get Cash App from the App Store or Google Play and use the code LEXPODCAST, you'll get $10 and Cash App will also donate $10 to FIRST, one of my favorite organizations that is helping to advance robotics and STEM education for young people around the world. 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, is another way to put it?

Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old. Uh, at that time, I was learning the basic programming language and I would take these books and, you know, they'll tell you, "Type this program into your computer." So I'd type that program to my computer. And as a result of all that typing, uh, I would get to play these very simple shoot them up games that, that, you know, I had implemented on my, on my little computer. So I thought it was fascinating as a young kid, uh, that I could write this code that was really just copying code from a book into my computer to then play these cooler video games. Another moment for me was, um, when I was a teenager and my father, who's a doctor, was reading about expert systems and about neural networks. So he got me to read some of these books and, um, I thought it was really cool that you could provide a computer that started to exhibit intelligence. Then I remember doing an internship while I was in high school, uh, this is in Singapore, where I remember doing a lot of photocopying and, and, uh, as office assistant. Um, and the highlight of my job was when I got to use the shredder. So the teenager me remembers thinking, "Boy, this is a lot of photocopying. If only you 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, um, 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, um, or even launching the first, uh, 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.