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  • 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.