Andrew Ng: Artificial Intelligence is the New Electricity at Stanford GSB (Transcript)

And maybe throughout this presentation, maybe at the end of doing Q&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, 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. 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.

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 to 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, right. 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.

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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.

So 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.

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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, will the user click on this ad? So leading Internet companies have a ton of data about this, because of showing people some number of ads that we saw 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 of AI 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?

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