Subtitles section Play video Print subtitles Good afternoon. Welcome to the future forum, a series of discussions where we are exploring trends that are changing the future. This series is presented by the Sloan Fellows from the Stanford MSX program. My name is Ravi. I'm an engineer by training, with over ten years of experience. I've been fortunate to design and develop products for some of the leading high tech companies here in the US. Currently, as a Sloan Fellow, I'm privileged to spend a year in Silicon Valley and at the Stanford Graduate School of Business participating in the evolution of technology and learning from some of the brightest minds in business. The MSX Program is a full time on-campus one-year management degree specifically designed for accomplished and experienced professionals from around the world. My classmates on average have over 13 years of experience, come from over 40 different industries, and have been leaders in driving change. Today I had the honor of introducing professor Andrew Ng. Andrew is one of the leading thinkers in artificial intelligence with research focusing on deep learning. He has taught machine learning for over 100,000 students through his online course at Coursera. He founded and led the Google Brain project, which developed massive scale, deep learning algorithms. He's currently the VP and chief scientist of Baidu, the co-chairman and co-founder of Coursera, and last but not least, an adjunct professor right here at Stanford University. Please join me, and the 2017 Sloan Fellows in welcoming Professor Andrew Ng. >> Thank you. >> [APPLAUSE] >> Thank you, and thank you, Ravi. So what I want to do today is talk to you about AI. So as Ravi mentioned, right now I lead a large AI team at Baidu, about 1300 scientists and engineers and so on. So I've been fortunate to see a lot of AI applications, a lot of research in AI as well as a lot of users in AI in many industries and many different products. So as I was preparing for this presentation, I asked myself what I thought would be most useful to you. And what I thought I'd talk about is four things. I want to share with you what I think are the major trends in AI. Because I guess the title of this talk was AI is the New Electricity. Just as electricity transformed industry after industry 100 years ago, I think AI will now do the same. So I share with you some of these exciting AI trends that I and many of my friends are seeing. I want to discuss with you some of the impact of AI on business. Whether, I guess, to the GSPC and to the Sloan Fellows, whether you go on to start your own company after you leave Stanford, or whether you join a large enterprise, I think that there's a good chance that AI will affect your work. So I'll share with you some of the trends for that. And then talk a little bit about the process of working with AI. This is some kind of practical advice for how to think about, not just how it affects businesses, but how AI affects specifically products and how to go about growing those products. And then finally, I think for the sign up of this event, there was a space for some of you to ask some questions and quite a lot of you asked questions about the societal impact of AIs. I'll talk a little bit about that as well, all right? So the title of this talk is projected, no, I guess not, all right. I think on the website the title was listed as the AI is the New Electricity. So it's an analogy that we've been making over half a year or something. About 100 years ago, we started to electrify the United States, right, develop electric power. And that transformed transportation. It transformed manufacturing, using electric power instead of steam power. It transformed agriculture, right. I think refrigeration was a really, a transformed healthcare and so on and so on. And I think that AI is now positioned to have an equally large transformation on many industries. The IT industry, which I work in, [COUGH] is already transformed by AI. So today at Baidu, Web search, advertising, all powered by AI. The way we decide whether or not to approve a consumer loan, really that's AI. When someone orders takeout through the Baidu on-demand food delivery service, AI helps us with the logistics. They route the driver to your door, helps us estimate to tell you how long we think it'll take to get to your door. So it's really up and down. Both the major services, many other products in the IT industry are now powered by AI, just literally possible by AI. But we're starting to see this transformation of AI technology in other industries as well. So I think FinTech is well on its way to being totally transformed by AI. We're seeing the beginnings of this in other industries as well. I think logistics is part way through its transformation. I think healthcare is just at the very beginnings, but there's huge opportunities there. Everyone talks about self-driving cars. I think that will come as well, a little bit, that will take a little bit of time to land, but that's another huge transformation. But I think that we live in a world where just as electricity transformed almost everything almost 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years, right? And maybe throughout this presentation, maybe at the end of doing Q and A, if you can think of an industry that AI won't transform, okay, like a major industry, not a minor one. Raise your hand and let me know. I can just tell you now, my best answer to that. So I once, [COUGH] when my friends and I, sometimes my friends and I actually challenge each other to name an industry that we don't think would be transformed by AI. My personal best example is hairdressing, right, cutting hair. >> [LAUGH] >> I don't know how to build a robot to replace my hairdresser. Although I once said this same statement on stage. And one of my friends, who is a robotics professor, was in the audience. And so my friend stood up, and she pointed at my head, and she said, Andrew, for most people's hairstyles, I would agree you can't build a robot. But for your hairstyle, Andrew, I can- >> [LAUGH] >> All right. So despite all this hype about AI, what is AI doing? What can AI really do? It's driving tremendous economic value, easily billions. At least tens of billions, maybe hundreds of billions of dollars worth of market cap. >> But what exactly is AI doing? It turns out that almost all this ridiculously huge amounts of value of AI, at least today, and the future may be different, but at least today almost all this massive economic value of AI is driven by one type of AI, by one idea. And This technical term is that it's called Supervised Learning. And what that means is using AI to figure out a relatively simple A to B mapping, or A to B response. Relatively simple A to B or input those response mappings. So, for example, given a piece of email, if I input that, and I ask you to tell me if this is spam or not. So, given an email, output 0 or 1 to tell me if this is spam or not, yes or no? This is an example of a problem where you have an input A, you can email, and you want a system to give your response B, 0 or 1. And this today is done with Supervised Learning. Or, given an image. Tell me what is the object in this image and maybe of a thousand objects or 10,000 objects. Just try to recognize it. So you input a picture and output a number from say, one to 1000 that tells you what object this is. This, AI can do. Some more interesting examples. When you're given an audio clip, maybe you want to output the transcript. So this is speech recognition, right. Input an audio clip and output detects transcript of what was said, so that's speech recognition. And the way that a lot of AI is built today is by having a piece of software learn, I'll say exactly in a second what I mean by the word learn, what it means for a computer to learn, but a lot of the value of AI today is having a machine learn these input to response mappings. Given a piece of English text, I'll put the French translation, or I talked about going from audio to text or maybe you want to go from text, and have a machine read out the text in a very natural-sounding voice. So, it turns out, that the idea of supervised learning, is that, when you have a lot of data, of both A and B both. Today, a lot of the time, we have very good techniques for automating, for automatically learning a way to map from A to B. For example, If you have a giant database of emails, as well as annotations of what is spam and what isn't spam, you could probably learn a pretty good spam filter. Or I guess I've done a lot of work on speech recognition. If you have, let's say, 50,000 hours of audio, and if you have the transcript of all 50,000 hours of audio, then you could do a pretty good job of having a machine figure out what is the mapping between audio and text. So, the reason I want to go into this level of detail is because despite all the hype and excitement about AI, it's still extremely limited today, relative to what human intelligence is. And clearly you and I, every one of us can do way more than figure out input to response mappings. But this is driving incredible amounts of economic value, today. Just one example. Given some information about an ad, and about a user, can you tell me whether you usually click on this ad? Leading Internet companies have a ton of data about this, because of showing people some number of ads that we sold whether they clicked on it or not. So we have incredibly good models for predicting whether a given user will click on a particular ad. And by showing users the most relevant ads this is actually good for users because you see more relevant ads and this is incredibly lucrative for many of the online internet advertising companies, right. This is certainly one of the most lucrative applications we have today, possibly the most lucrative, I don't know. Now, at Baidu, you have worth of a lot of product managers. And one question that I got from a lot of product managers is, you're trying to design a product and you want to know, how can you fit AI in some bigger product? So, do you want to use this for spam filter? Do you want to use this to maybe tag your friends' faces? Or do you want to use this, where do you want to build speech recognition in your app, but can AI do other things as well. Where can you fit AI into, you know, a bigger product or a bigger application. So, some of the product managers I was working with were struggling to understand what can AI do and what can't AI do. So I'm curious. How many of you know what a product manager is or what a product manager does? Okay good, like half of you. Is that right? Okay, cool. I asked the same question at an academic AI conference and I think only about one fifth of the hands went up, which is interesting. Just to summarize when we in the workflow, a lot of tech companies, it's the product manager's responsibility to work with users, look at data, to figure out what is a product that users desire. To design the features and sometimes also the marketing and the pricing, as well. But let me just say design the features and figure out what the product is supposed to do, for example, should you have a light button or not? Do you try to have a speech recognition feature or not? So it's really to design the product. If you give the product spec to engineering which is responsible for building it, right, that's a common division of labor in technology companies between product managers and engineers. So product managers, when I was working with them, was trying to understand what can AI do? So there's this rule of thumb that I gave many product managers, which is that anything that a typical human can do. With, at most, one second of thought. Right, we can probably now or soon, automate with AI. And this is an imperfect rule. There are false positives and false negatives with these heuristics so this rule is imperfect but we found this rule to be quite helpful. So today, actually at Baidu, there are some product managers running around looking for tasks that they could do in less than a second and thinking about how to automate that. >> [LAUGH] >> I have to say, before we came up with this rule, they were given a different rule by someone else. And before I gave this heuristic, someone else told them product managers, assume AI can do anything. >> [LAUGH] >> And that actually turned out to be useful. Some progress was made with that heuristic, but I think this one was a bit better. A lot of these things on the left you could do with less than a second of thought. So one of the patterns we see is that there are a lot of things that AI can do, but AI progress tends to be fastest if you're trying to do something that a human can do. For example, build a self-driving car, right? Humans can drive pretty well, so AI is making actually pretty decent progress on that.