YouSaid · the spoken record

Andrew Ng

lines on the record: 94
first: 2020-02-20
most recent: 2020-02-20
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I still teach reinforcement learning in one of my standard classes, in my PhD thesis was on reinforcement learning, so I totally loved I find it 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 worth working today. Now our work changes so fast. Maybe it'll be totally different in a couple years. But I think we need a couple more things for reinforcement learning to get there.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Or attention mold in a certain way, building on top of the earlier concepts. 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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's 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 more of confidence. Hopefully you have enough intuitions from the earlier sections to then understand why we structure confidence in certain way. And then eventually why we built RNNs on LSTMs.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, often the question is why doesn't it work yet? Or can I expect this 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 heading down the, so you don't spend six months heading down the blind alley before someone comes and says, why should you spend six months doing this?”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Syntax errors were horrible debugging services. So I think we learned how to debug and I think in machine learning the way you debug a machine learning program is very different than the way you Like, do binary research or whatever, use a debugger, trace through the code in traditional software engineering. So as an evolving discipline, but I find that the people that are really good at debugging machine learning algorithms are easily 10x, maybe 100x faster at getting something to work.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 equally important or maybe even more important part of coding is to understand how to string together these lines of code into coherent things. So when should you put something on a function call and when should you not? How do you think about abstraction? So those frameworks are what makes a programmer efficient, even more than understanding the syntax. I remember when I was an undergrad at Carnegie Mellon, one of my friends would debug their code by first trying to compile it and then it was C++ code. And then every line that 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 DeVization, you have those skills to be very efficient in how you build these networks.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So, if you take the deep length 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 of 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 political 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Basically, the algebra, even very, very basically the algebra in some programming, I think that people that have done the machine learning calls will find a 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 does an objective function, which is covered more slowly in the machine learning course.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, need to understand basic programming since there are programming exercises in Python. And the map prerec 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 desk rate.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So the deep learning specialization offered by DBON.ai is, I think, was Coursera's top specialization. It might still be. So it's a very popular way for people to take that specialization, to learn about everything from neural networks to how to tune a neural network, to what is a confNet to, what is a RNN or sequence model, or what is an attention model. And so the design specialization steps everyone through those algorithms so you deeply understand it and can implement it and use it for whatever application.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“No, yeah, thank you. You know, I was once reading a news article. I think it was tech review. And I'm going to mess up the statistic. But I remember reading an article that said something like one third of our programmers are self-taught. I may have the number one third round. It 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Deep lender AI is working to create causes to help people break into AI. So my machine learning course that I taught through Stanford remains one of the most popular courses on Coursera.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I find it 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 go to one of the several good websites, very good websites with clean scoped data sets that you could just work on”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Dream of himself of a self in the span of a day. So I think these are the types of very practical, very messy data problems that my teams wrestle with. In the case of large consumer internet companies where you have a billion users, you have a lot of data, you don't worry about it, just take the average. It kind of works. But in a case of other industry settings, we don't have big data, just a small data, very small data sets, maybe around 100 de facto parts or a hundred examples of a defect. If you have only 100 examples, these little labeling errors, if 10 of your 100 labels are wrong, that actually is 10% of your data set has a big impact. So how do you clean this up? What are you supposed to do? This is an example of the types of things that my team, 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 doing the work in visual inspection, we will say a plastic pot and show it to one inspector. And the inspector sometimes very opinionated. They'll go, clearly that's a defect, this scratch, unacceptable, gotta reject this part. Take the same part to different inspector, different very affinitated, clearly this 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 part, show it to the same inspector in the afternoon and I suppose in the morning and very affinity ago in the morning they said, clearly it's okay and the afternoon equally confident. Clearly this is a defect. And so what is an AI team supposed to do if sometimes even one person doesn't”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Find that today we have mature processes of 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 it CVS aversion git, maybe something else in the future? We're very immature in terms of tools of managing data and think about how the clean data and how the software”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think both are important. And it's also problem dependent. So for a few data sets, 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 a base 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 A. And bigger data sets with neural networks without further African innovation will be sufficient to take us further. But on the flip side, if we look at the recent breakthroughs using transformer networks for language models, it was a combination of novel architecture, but also scale had a lot to do with it. If we look at what happened with GP2 and birds, I think scale was a large part of the story.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 some of whom we know. My well meaning friends came and were trying to give me friendly events. I was like, hey, Andrew, why are you doing this? This is crazy. It's in the near national architecture. Look at these architectures of building. You just want to go for scale. Like, it's a bad career move. So my well-meaning friends, we're trying to, some of them, we're 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 his 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 bigly make this thing the better's performance accuracy is the vertical axis. So it's really based on that chart that Adam generated that gave me the conviction that it 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 Thun to pitch starting.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“So from this very crude, definitely problematic argument, there's just no way that most of what we know is through supervised learning. But what if you get so many bits of information is from sucking in images, audio, fields, experiences in the world. And so that argument, and there are a lot of known forces argument going to really convince me that there's a lot of power to unsupervise 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Each synaptic connection, each weight in your brain's neural network has just a one bit parameter that's 10 to the 14 bits you need to learn in up to 10 to the nine seconds of your life. So via this simple argument, which is a lot of problems, it's very simplified, that's 10 to the 5 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 5 bits per second of labels to her. And I think I'm a very loving parent, but I'm just not going to do that.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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, actually I think it was around 2005 after Europe's at that time called NIPS, but now Europe's had ended. And Jeff Hinton and I were sitting in the cafeteria outside the conference. We had lunch, we just chatting. And Jeff pulled up this napkin. He started sketching this argument on a napkin. It was very compelling. Human brain has about 100 trillion, so that'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 9, maybe three by 10 to 9 seconds. So just let's say 10 to the nine. So if”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, I like theory, but when I work on theory myself, and just personal taste, I'm not seeing 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 speaking with a mathematics professor and it kind of just said, hey, why do you do what you do? And he said he actually had stars in his eyes when he answered. And this mathematician, not from Stanford, different university, he said, I do what I do because it helps me to discover truth and beauty in the universe. He had starts in his eyes, he said. And I thought that's great Don't want to do that.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Practical applications of reinforced learning at the time, which caused it to become pretty well known. I feel like we might have almost come full circle where today there's so much hype, so much excitement. Reinforcing their earning, but the game we're hunting for more applications and all of these great ideas that the communities come up with.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 Coles 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 that we could then focus on the reinforcement learning and inverse reinforcement learning techniques to then 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. The autonomous helicopter for a flying helicopters was one of the few, you know,”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“What not many people see how hard it was. So Peter and Adam Cole 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 the helicopter's flying around upside down doing stunts, 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 satellites. So we tried, 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 quickly was exploring this crazy configuration of specialized hardware to interpret GPS signals, look into FPGA. It's completely insane. Spent about a year working on that didn't work.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I was really fortunate to have had Peter Abu 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 actually even today, the website helli.stanfit.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, roses. So it's cool.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 pinist stylist is a very easy way to build up an 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 stylist.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think 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 concrete use for software engineering. For example, actually, if you run a small mom and pop store, I think if you can analyze the data about your sales, your customers, I think there's actually real value there, maybe even more than traditional software engineering. So I find that for a lot of my friends in various professions, be it recruiters or accountants or 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 of a lot of people than the software engineering.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. We could give them some reasons. But what I found with the rise of”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. I think it will enhance human to computer communications, which is becoming more and more important inday as well.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I think once upon a time only a small part of humanity was literate, could read and write. And maybe you thought, maybe not everyone needs to learn to read and write. Just go listen to a few monks to you. And maybe that was enough. Or maybe just need a few handful of authors to write the bestsellers. And then no one else needs to write. But what we found was that by giving as many people in some countries, almost everyone basic literacy, it dramatically enhanced human-to-human communications. And we can now write for an audience of one, such as if I send you an email or you send me an email. I think in computing, we're still in that phase where so few people know how to code that the code is mostly have the code for relatively large audiences. But if everyone, or most people became developers at some level, similar to how most people in developing countries”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Of soft ranges today, sort of have an appreciation of the cloud. I think in the future, maybe we'll approach nearly 100% of our developers being in some way an AI developer, at least having an appreciation of machine learning.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think with the whole machine learning movement as well, I think it didn't come out of nowhere instead. What happened was as more people learn about machine learning, they will tell their friends and their friends will see how it's applicable to their work. And then the community kept on growing. And I think we're still growing. I don't know in the future what percentage of all developers will be AI developers. I could easily see it being nor for 50% because so many AI developers broadly construed, not just people doing the machine learning modeling, but the people building the infrastructure, data pipelines, all the softwares surrounding the core machine learning model maybe is even bigger. I feel like today almost every software engineer has some understanding of the cloud. Not all, but maybe this microcontroller developer doesn't need to deal with the cloud. But I feel like the vast majority of you”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Log in, you need to log out if 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 school in San Francisco. We had about 20 something users. Whereas the teacher there, Sacred Heart Cathedral Prep, the teacher is great. And guess what? Zero people use this feature. It turns out people studying online, they want to watch the videos by themselves. So you can play back, pause at your own speed rather than in groups. So that was one example of a tiny lesson learned out of many that allowed us to hone in to the set of features.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 insider 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're filming my Stanford class and sticking the videos on YouTube and some other things we had uploaded a whole works and so on but you know basically the one hour 15 minute video that we put on YouTube and then we had four or five other versions of websites that had built most of which you would never have heard of because they reached small audiences but that allowed me to iterate 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 you know if you are logged in and then I want”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 to learn and needs and would benefit from, I felt that having a good understanding of the foundations, kind of back to the basics, would put them in a better stead to then build on a long-term career. So try to consistently make decisions on that principle.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Think as humbling, and I wasn't thinking about what I was feeling. I think one thing we, 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 the learners, do what's best for students. And so when I went to the recording studio, the only thing on my mind was, what can I say? How can I design my slides? What I need to draw right to make these concepts as clear as possible for learners. I think I've seen sometimes instructors is tempting to, hey, let's talk about my work. Maybe if I teach you about my research, someone will cite my papers a couple more times. And I think one of the things we got right launched the first few MOOCs and later building called Sarah 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 is a guide.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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. Lot of times we're launching the first exhaust advert, we've already announced the calls, but 100,000 people had signed up. We just started to write the code and we had not yet actually filmed the videos. So a lot of pressure, hundreds of thousand 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 you want to go home now or do you want to go to the office to film videos? The thought of being able to help hundred thousand 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. or 3 a.m. I think unfortunate that it doesn't show that it was recorded that later night, but it was really inspiring.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“I felt teaching at Stanford teaching machine learning to about 400 sins a year at a 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“Then I remember doing an internship while I was in high school. This was in Singapore, where I remember doing a lot of photocopying and as office assistant. And the highlight of my job was when I got to use the shredder. So the teenager, me, remote thinking, boy, this is a lot of photocopying. If only we could write software, build a robot on 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source
“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 it would take these folks and they'll tell you, type this program into your computer. So type that programs on 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 local 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 the doctor was reading about expert systems and about neuronetworks. 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.”
2020-02-20 · Lex Fridman Podcast · #73 – Andrew Ng: Deep Learning, Education, and Real-World AI · IDENTIFIED FROM THE TRANSCRIPT · source