0:01 What do you want to be when you grow up Olivia? Umm, an astronaut! Do you happen to know what 0:11 a computer programmer is? Yeahh, umm, no. Umm, what what? I'm not really sure how to 0:18 explain it. Computer programming is pretty simple. It's a set of instructions, like a 0:22 recipe. You have to follow them step by step to get the end result you want. Computer science 0:27 is a way to impact the world. It can be music videos, it can be games, detect whether or 0:34 not someone is related to someone else. Find you know, people's friends. You can do all 0:38 sorts of other crazy things that actually save lives. You do have to have a drive I 0:43 think. It is to me like a paintbrush. I think great programming is not all that dissimilar 0:51 from great art. When I finally learned a little bit of programming, that blank wall resolved 0:56 into a bunch of doors and you open them and of course then you find behind them is another 1:01 hallway filled with a bunch of doors. Programming is fun and easy. You can do anything your 1:07 mind wants to do. Finally you start to open enough doors the light comes in. To me a finished 1:13 program is like a structure filled with light. All the corners are illuminated. The number 1:18 of people that you can touch and interact with is something the world has never seen 1:25 before. Our first lesson in this series is all about what computer science is, what a 1:31 computer scientist does and how you can be more responsible in your use of technology. 1:37 It's a very important lesson but it is a little text-heavy. At the end, you get to make your 1:42 very own customized encoding using your initials. It's a fun activity and it's very empowering 1:49 because binary is one of those things that feels very technical but once you understand 1:54 it, it's like you speak a secret language.
Transcripción : Youtube
0:01 "Everybody in this country should learn how to program a computer... 0:04 because it teaches you how to think." - Steve Jobs 0:09 What do you want to be when you grow up? Um... an astronaut. 0:12 I want to be a fashion designer. A basketball player. I want to be an actor. A doctor. A 0:17 teacher. A chef. An artist. What do you wanna be when you grow up? A mermaid! 0:24 (interviewer) Do you know what a computer programmer is? (student) Yeah--umm... no. No. Uhh, no. 0:32 I think it's something that has code and it's able to decode a mystery. I think that they... 0:41 umm, wait what? (interviewer) Computer programmer? (student) No. 0:48 Nowadays, just about everything requires some form of programming. So what is it? 0:55 Programming is basically explaining to a computer what you want it to do for you. When you're programming, you're teaching possibly 1:02 the stupidest thing in the universe, a computer, how to do something. Programming is one of 1:08 the only things in the world that you can do where you can sit down and just make something 1:13 completely new from scratch-whatever you want. It's really not unlike playing an instrument 1:18 or playing a sport. It starts out being very intimidating, but you kind of get the hang 1:26 of it over time. Coding is something that can be learned and I know it can be intimidating, 1:31 and a lot of things are intimidating, but what isn't? A lot of the coding people do 1:38 is actually fairly simple. It's more about the process of breaking down problems than 1:46 coming up with complicated algorithms as people traditionally think about it. Well if it's 1:51 fairly simple, why aren't there more of us doing it? Over the next 10 years there will 1:57 be 1.4 million jobs in computer science and only about 400,000 grads qualify for those 2:02 jobs. That's a shortage of a million people! So how do you start? 2:16 I was obsessed with maps when I was a kid, and cities specifically, so I taught myself how to program. I had a 2:25 very clear goal of what I wanted to do which was to see a map of the city on my screen 2:31 and play with it. Put things on the map, move things around the map, see what was happening 2:36 in the city. How it worked, how it lived, how it breathed. The best early thing was 2:41 actually using software to decide when the classes in my school would meet. And that 2:47 put me in a position to decide which girls were in my class. 2:54 The first program I wrote asked things like, "What's your favorite color?" or "How old are you?" I first learned how 3:00 to make a green circle and a red square appear on the screen. The first time I actually had 3:06 something come up and say "Hello world!" I made a computer do that? It was astonishing. 3:11 When I finally learned a little bit of programming, that blank wall resolved into a bunch of doors. 3:17 And you open them and finally you start to open enough doors that the light comes in. 3:21 And to me, a finished program is like a structure filled with light. All the corners are illuminated 3:27 and you understand the structure of it. It's a really serene feeling to have completed that. 3:41 It took me some time to realize that creating things with your hands or creating 3:45 code, creating programs is just a different way to express creativity. I think right now 3:52 there's a big emergence of the culture of making. People who make their own scarves 3:59 and hats, people who write their own apps. Now it's just limited by your imagination. 4:03 And sort of what kinds of ideas, what kind of understanding can you build into a computer 4:10 to do these things that were previously impossible. 4:18 All great things are built in teams when you 4:22 collaborate with other smart people. You're testing your ideas, you're stimulating each 4:28 other, that's what makes us successful. It's not some flash of brilliance from somebody 4:33 who codes 24 hours a day for 3 weeks. The magic happens when we're all on the same page 4:41 collaborating and building something together. There's a much greater need in the world for 4:46 engineers and people who can write code than there will ever be supply. And so we all live 4:54 these very charmed lives. To get the very best people we try to make the office as awesome as possible. 5:21 We have a fantastic chef. Free food. Breakfast, lunch, and dinner. Free laundry. 5:28 Snacks. Even places to play and video games and scooters. There's all these kind of interesting 5:35 things around the office. 5:38 Places where people can play or relax, or go to think, or play music, or be creative. 5:46 I went on the Bureau of Labor Statistics for the United States 5:49 and there's about a third of the pie that's all the things that you would expect. They're 5:54 working for the government, they're working in typical technology jobs, but then the rest 6:00 of the pie--the majority of the pie--just split down into these little tiny slices of 6:04 every industry imaginable. And what it is, is computers are everywhere! Do you want to 6:09 work in agriculture? Do you want to work in entertainment? Do you want to work in manufacturing? 6:15 It's just all over. 6:28 Here we are, 2013, and we all depend on technology to communicate, 6:33 to bank. Information. And none of us know how to read and write code. 6:43 So you guys, what else? Who else has an idea of what we can change with our program? 6:48 What else can we do? 6:51 What I saw my students take away from using Scratch and programming in our classroom 6:57 is that they're willing to push through problems. It really builds critical thinking. It builds 7:02 problem solving. And it's something that they can then apply to math in the classroom. Or 7:08 their reading skills. We integrated science with this programming and I saw my scores 7:15 go up 30%. When I was in school I was in this after school club called the Whiz Kids and 7:23 when people found out they laughed at me. You know all these things. And I'm like, man 7:27 I don't care. I think it's cool. You know I'm learning a lot and some of my friends 7:33 have jobs. It's important for these kids. It should be mandatory. To be a citizen on 7:40 this planet, to read and write code. 7:47 I just think you have to start small. That's one of the biggest misconceptions about 7:52 computer science and programming overall is that you 7:55 have to learn this big body of information before you can do anything. You don't have 8:00 to be a genius to know how to code. You need to be determined. Addition, subtraction, that's 8:06 about it. You should probably know your multiplication tables. You don't have to be a genius to code. 8:11 Do you have to be a genius to read? Do you have to be a genius to do math? No. I think 8:18 if someone had told me that software is really about humanity. That it's really about helping 8:26 people by using computer technology, it would have changed my outlook a lot earlier. Whether 8:31 you're trying to make a lot of money or whether you just want to change the world, computer 8:34 programming is an incredibly empowering skill to learn. To be able to actually come up with 8:38 an idea and then see it in your hands and then press a button and have it be in millions 8:44 of people's hands, I think we're the first generation in the world that's really had 8:48 that kind of experience. The programmers of tomorrow are the wizards of the future. You're 8:53 going to look like you have magic powers compared to everybody else. I think it's amazing. I 8:57 think it's the closest thing we have to a superpower. Great coders are today's rockstars. 9:03 That's it. 9:06 To start learning a superpower go to Code.org.
Transcripción : Youtube
0:00 TOM SIMONITE: Hi. 0:10 Good morning. 0:11 Welcome to day three of Google I/O, 0:14 and what should be a fun conversation about machine 0:16 learning and artificial intelligence. 0:18 My name is Tom Simonite. 0:19 I'm San Francisco bureau chief for MIT Technology Review. 0:23 And like all of you, I've been hearing a lot recently 0:26 about the growing power of machine learning. 0:28 We've seen some striking results come out 0:30 of academic and industrial research labs, 0:33 and they've moved very quickly into the hands of developers, 0:36 who have been using them to make new products and services 0:39 and companies. 0:40 I'm joined by three people this morning 0:42 who can tell us about how this new technology 0:45 and the capabilities it brings are coming out into the world. 0:48 They are Aparna Chennapragada, who 0:51 is the director of product management 0:53 and worked on the Google Now mobile assistant, 0:56 Jeff Dean, who leads the Google Brain research group here 1:00 in Mountain View, and John Giannandrea, 1:02 who is head of search and machine intelligence at Google. 1:06 Thanks for joining me, all of you. 1:08 We're going to talk for about 30 minutes, 1:10 and then there will be time for questions from the floor. 1:15 John, why don't we start with you? 1:16 You could set the scene for us. 1:19 Artificial intelligence and machine learning 1:21 are not brand new concepts. 1:23 They've been around for a long time, 1:24 but we're suddenly hearing a lot more about them. 1:27 Large companies and small companies 1:28 are investing more in this technology, 1:30 and there's a lot of excitement. 1:31 You can even get a large number of people 1:33 to come to a talk about this thing early in the morning. 1:37 So what's going on? 1:39 Tell these people why they're here. 1:41 JOHN GIANNANDREA: What's going on? 1:41 Yeah, thanks, Tom. 1:42 I mean, I think in the last few years, 1:44 we've seen extraordinary results in fields that hadn't really 1:48 moved the needle for many years, like speech recognition 1:51 and image understanding. 1:52 The error rates are just falling dramatically, 1:55 mostly because of advances in deep neural networks, 1:58 so-called deep learning. 2:00 I think these techniques are not new. 2:03 People have been using neural networks for many, many years. 2:06 But a combination of events over the last few years 2:09 has made them much more effective, 2:11 and caused us to invest a lot in getting them 2:14 into the hands of developers. 2:17 People talk about it in terms of AI winters, 2:19 and things like this. 2:20 I think we're kind of an AI spring right now. 2:23 We're just seeing remarkable progress 2:25 across a huge number of fields. 2:26 TOM SIMONITE: OK. 2:27 And now, how long have you worked 2:28 in artificial intelligence, John? 2:30 JOHN GIANNANDREA: Well, we started 2:31 investing heavily in this at Google about four years ago. 2:33 I mean, we've been working in these fields, 2:35 like speech recognition, for over a decade. 2:38 But we kind of got serious about our investments 2:40 about four years ago, and getting organized 2:44 to do things that ultimately resulted 2:46 in the release of things like TensorFlow, which 2:48 Jeff's team's worked on. 2:49 TOM SIMONITE: OK. 2:49 And we'll talk more about that later, I'm sure. 2:52 Aparna, give us a perspective from the view of someone 2:56 who builds products. 2:57 So John says this technology has suddenly 2:59 become more powerful and accurate and useful. 3:03 Does that open up new horizons for you, 3:05 when you're thinking about what you can build? 3:06 APARNA CHENNAPRAGADA: Yeah, absolutely. 3:08 I think for me, these are great as a technology. 3:12 But as a means to an end, they're 3:13 powerful tool kits to help solve real problems, right? 3:17 And for us, as building products, and for you guys, 3:20 too, there's two ways that machine learning 3:22 changes the game. 3:24 One is that it can turbo charge existing use cases-- that 3:27 is, existing problems like speech recognition-- 3:30 by dramatically changing some technical components 3:33 that power the product. 3:34 If you're building a voice enabled assistant, the word 3:37 error rate that John was talking about, as soon as it dropped, 3:40 we actually saw the usage go up. 3:42 So the product gets more usable as machine learning improves 3:46 the underlying engine. 3:47 Same thing with translation. 3:48 As translation gets better, Google Translate, 3:51 it scales to 100-plus languages. 3:54 And photos is a great example. 3:55 You've heard Sundar talk about it, too, 3:57 that as soon as you have better image understanding, 4:00 the photo labeling gets better, and therefore, I 4:02 can organize my photos. 4:03 So it's a means to an end. 4:04 That's one way, certainly, that we have seen. 4:06 But I think the second way that's, personally, far more 4:09 exciting to see is where it can unlock new product use cases. 4:14 So turbocharging existing use cases is one thing, 4:17 but where can you kind of see problems 4:19 that really weren't thought of as AI or data problems? 4:22 And thanks to mobile, here-- 3 billion phones-- a lot 4:26 of the real world problems are turning into AI problems, 4:29 right? 4:29 Transportation, health, and so on. 4:31 That's pretty exciting, too. 4:32 TOM SIMONITE: OK. 4:33 And so is one consequence of this 4:35 that we can make computers less annoying, do you think? 4:38 I mean, that would be nice. 4:40 We'd all had these experiences where 4:41 you have a very clear idea of what it is you're trying to do, 4:44 but it feels like the software is doing 4:46 everything it can to stop you. 4:47 Maybe that's a form of artificial intelligence, too. 4:50 I don't know. 4:50 But can you make more seamless experiences 4:53 that just make life easier? 4:55 APARNA CHENNAPRAGADA: Yeah. 4:56 And I think in this case, again, one of the things 4:59 to think about is, how do you make sure-- especially 5:01 as you build products-- how do you 5:03 make sure your interface scales with the intelligence? 5:06 The UI needs to be proportional to AI. 5:09 I cannot believe I said some pseudo formula in front of Jeff 5:12 Dean. 5:14 But I think that's really important, 5:15 to make sure that the UI scales with the AI. 5:18 TOM SIMONITE: OK. 5:19 And Jeff, for people like Aparna, 5:23 building products, to do that, we 5:26 need this kind of translation step 5:27 which your group is working on. 5:29 So Google Brain is a research group. 5:30 Works in some very fundamental questions in its field. 5:33 But you also build this infrastructure, 5:36 which you're kind of inventing from scratch, that makes 5:38 it possible to use this stuff. 5:41 JEFF DEAN: Yeah. 5:42 I mean, I think, obviously, in order 5:44 to make progress on these kinds of problems, 5:46 it's really important to be able to try lots of experiments 5:50 and do that as quickly as you can. 5:52 There's a very fundamental difference 5:55 between having an experiment take a few hours, 5:58 versus something that takes six weeks. 5:59 It's just a very different model of doing science. 6:03 And so, one of the things we work on 6:06 is trying to build really scalable systems that are also 6:10 flexible and easy to express new kinds of machine learning 6:13 ideas. 6:14 So that's how TensorFlow came about. 6:16 It's sort of our internal research vehicle, 6:19 but also robust enough to take something you've done and done 6:23 lots of experiments on, and then, when you get something 6:25 that works well, to take that and move it into a production 6:28 environment, run things on phones or in data 6:31 centers, on RTPUs, that we announced a couple days ago. 6:36 And that seamless transition from research 6:39 to putting things into real products 6:41 is what we're all about. 6:43 TOM SIMONITE: OK. 6:44 And so, TensorFlow is this very flexible package. 6:48 It's very valuable to Google. 6:49 You're building a lot of things on top of it. 6:51 But you're giving it away for free. 6:52 Have you thought this through? 6:54 Isn't this something you should be keeping closely held? 6:56 JEFF DEAN: Yeah. 6:57 There was actually a little bit of debate internally. 7:00 But I think we decided to open source it, 7:02 and it's got a nice Apache 2.0 license which basically 7:05 means you can take it and do pretty much whatever 7:07 you want with it. 7:09 And the reason we did that is several fold. 7:14 One is, we think it's a really good way of making research 7:18 ideas and machine learning propagate more quickly 7:20 throughout the community. 7:22 People can publish something they've done, 7:26 and people can pick up that thing 7:27 and reproduce those people's results or build on them. 7:30 And if you look on GitHub, there's 7:33 like 1,500 repositories, now, that mention TensorFlow, 7:36 and only five of them are from Google. 7:38 And so, it's people doing all kinds of stuff with TensorFlow. 7:41 And I think that free exchange of ideas and accelerating 7:43 of that is one of the main reasons we did that. 7:47 TOM SIMONITE: OK. 7:47 And where is this going? 7:49 So I imagine, right now, that TensorFlow is mostly 7:52 used by people who are quite familiar with machine learning. 7:55 But ultimately, the way I hear people 7:59 talk about machine learning, it's 8:00 just going to be used by everyone everywhere. 8:03 So can developers who don't have much 8:05 of a background in this stuff pick it up yet? 8:07 Is that possible? 8:08 JEFF DEAN: Yeah. 8:09 So I think, actually, there's a whole set 8:12 of ways in which people can take advantage of machine learning. 8:15 One is, as a fundamental machine learning researcher, 8:18 you want to develop new algorithms. 8:19 And that's going to be a relatively small fraction 8:21 of people in the world. 8:23 But as new algorithms and models are developed 8:26 to solve particular problems, those models 8:29 can be applied in lots of different kinds of things. 8:31 If you look at the use of machine learning 8:35 in the diabetic retinopathy stuff 8:36 that Sundar mentioned a couple days ago, 8:39 that's a very similar problem to a lot of other problems 8:41 where you're trying to look at an image 8:43 and detect some part of it that's unusual. 8:45 We have a similar problem of finding text 8:48 in Street View images so that we can read the text. 8:51 And that looks pretty similar to a model 8:54 to detect diseased parts of an eye, just different training 8:58 data, but the same model. 8:59 So I think the broader set of models 9:02 will be accessible to more and more people. 9:05 And then there's even an easier way, 9:07 where you don't really need much machine learning knowledge 9:09 at all, and that is to use pre-trained APIs. 9:12 Essentially, you can use our Cloud Vision API 9:15 or our Speech APIs very simply. 9:17 You just give us an image, and we give you back good stuff. 9:20 And as part of the TensorFlow flow open source, 9:22 we also released, for example, an inception model that 9:26 does image classification that's the same model as underlies 9:29 Google Photos. 9:30 TOM SIMONITE: OK. 9:31 So will it be possible for someone-- maybe they're 9:33 an experienced builder of apps, but don't know much about 9:37 machine learning-- they could just 9:39 have an idea and kind of use these building blocks to put it 9:42 together? 9:42 JEFF DEAN: Yeah. 9:42 Actually, I think one of the reasons TensorFlow has taken 9:45 off, is the tutorials in TensorFlow are actually 9:47 quite good at illustrating six or seven important kinds 9:53 of models in machine learning, and showing people 9:55 how they work, stepping through both the machine learning 9:58 that's going on underneath, and also how you express them 10:01 in TensorFlow. 10:01 That's been pretty well received. 10:03 TOM SIMONITE: OK. 10:04 And Aparna, I think we've seen in the past 10:06 that when a new platform of mode of interaction comes forward, 10:10 we have to experiment with it for some time 10:13 before we figure out what works, right? 10:16 And sometimes, when we look back, 10:17 we might think, oh, those first generation 10:19 mobile apps were kind of clunky, and maybe not so smart. 10:23 How are we going with that process 10:25 here, where we're starting to have to understand 10:28 what types of interaction work? 10:30 APARNA CHENNAPRAGADA: Yeah. 10:32 And I think it's one of the things that's not intuitive 10:34 when you start out, you rush out into a new area, 10:36 like we've all done. 10:38 So one experience, for example, when 10:39 we started working on Google Now, one thing we realized 10:42 is, it's really important to make sure 10:44 that, depending on the product domain, some of these black box 10:49 systems, you need to pay attention 10:51 to what we call internally as the wow to WTH ratio. 10:55 That is, as soon as you kind of say, 10:57 hey, there are some delightful magical moments, right? 11:00 But then, if you kind of get it wrong, 11:02 there's a high cost to the user. 11:04 So to give you an example, in Google Search, 11:06 let's say you search for, I don't know, Justin Timberlake, 11:09 and we got a slightly less relevant answer. 11:12 Not a big deal, right? 11:13 But then, if the assistant told you to sit in the car, 11:16 go drive to the airport, and you missed 11:18 your flight, what the hell? 11:21 So I think it's really important to get that ratio right, 11:23 especially in the early stages of this new platform. 11:27 The other thing we noticed also is 11:29 that explainability or interpretability really builds 11:33 trust in many of these cases. 11:35 So you want to be careful about looking 11:37 at which parts of the problem you use machine learning 11:41 and you drop this into. 11:43 You want to look at problems that are easy for machines 11:46 and hard for humans, the repetitive things, 11:48 and then make sure that those are the problems that you 11:50 throw machine learning against. 11:52 But you don't want to be unpredictable and inscrutable. 11:56 TOM SIMONITE: And one mode of interaction that everyone seems 11:59 to be very excited about, now, is this idea 12:01 of conversational interface. 12:02 So we saw the introduction on Wednesday of Google Assistant, 12:06 but lots of other companies are building these things, too. 12:10 Do we know that definitely works? 12:13 What do we know about how you design 12:15 a conversational interface, or what the limitations 12:17 and strengths are? 12:19 APARNA CHENNAPRAGADA: I think, again, at a broad level, 12:21 you want to make sure that you can have this trust. 12:24 So [INAUDIBLE] domains make it easy. 12:26 So it's very hard to make a very horizontal system 12:29 work that works for anything. 12:31 But I'm actually pretty excited at the progress. 12:33 We just launched-- open sourced-- the sentence parser, 12:36 Parsey Mcparseface. 12:37 I just wanted to say that name. 12:41 But it's really exciting, because then you say, 12:43 OK, you're starting to see the beginning of conversational, 12:46 or at least a natural language sentence understanding, 12:49 and then you have building blocks that build on top of it. 12:52 TOM SIMONITE: OK. 12:52 And John, with your search hat on for a second, 12:56 we heard on Wednesday that, I think, 20% of US searches 13:01 are now done by voice. 13:02 So people have clearly got comfortable with this, 13:04 and you've managed to provide something 13:06 that they want to use. 13:09 Is the Assistant interface to search 13:12 going to grow in a similar way, do you think? 13:14 Is it going to take over a big chunk of people's search 13:17 queries? 13:18 JOHN GIANNANDREA: Yeah. 13:19 We think of the Assistant as a fundamentally different product 13:22 than search, and I think it's going 13:24 to be used in a different way. 13:25 But we've been working on what we 13:26 call voice search for many, many years, 13:28 and we have this evidence that people 13:30 like it and are using it. 13:32 And I would say our key differentiator, there, is just 13:36 the depth of search, and the number of questions 13:38 we can answer, and the kinds of complexities 13:40 that we can deal with. 13:43 I think language and dialogue is the big unsolved problem 13:46 in computer science. 13:48 So imagine you're reading an article 13:50 and then writing a shorter version of it. 13:52 That's currently beyond the state of the art. 13:54 I think the important thing about the open source release 13:56 we did of the parser is it's using TensorFlow as well. 14:02 So in the same way as Jeff explained, 14:03 the functionality of this in Google Photos for finding 14:06 your photos is actually available open source, 14:08 and people can actually play with it 14:09 and run a cloud version of it. 14:11 We feel the same way about natural language understanding, 14:13 and we have many more years of investment 14:15 to make in getting to really natural dialogue systems, 14:19 where you can say anything you want, 14:20 and we have a good shot of understanding it. 14:23 So for us, this is a journey. 14:25 Clearly, we have a fairly usable product in voice search today. 14:29 And the Assistant, we hope, when we launch 14:31 later this year, people will similarly 14:33 like to use it and find it useful. 14:36 TOM SIMONITE: OK. 14:36 Do you need a different monetization model 14:39 for the Assistant dialogue? 14:40 Is that something-- 14:42 JOHN GIANNANDREA: We're really focused, right now, 14:42 on building something that users like to use. 14:45 I think Google has a long history 14:46 of trying to build things that people find useful. 14:49 And if they find them useful, and they use them at scale, 14:52 then we'll figure out a way to actually have a business 14:54 to support that. 14:56 TOM SIMONITE: OK. 14:57 So you mentioned that there are still 14:58 a lot of open research questions here, 14:59 so maybe we could talk about that a little bit. 15:03 As you described, there have been 15:05 some very striking improvements in machine learning recently, 15:08 but there's a lot that can't be done. 15:09 I mean, if I go to my daughter's preschool, 15:11 I would see young children learning and using 15:14 language in ways that your software can't match right now. 15:17 So can you give us a summary of the territory that's 15:21 still to be explored? 15:22 JOHN GIANNANDREA: Yeah. 15:23 There's a lot still to be done. 15:25 I think there's a couple of areas 15:28 which researchers around the world 15:30 are furiously trying to attack. 15:32 So one is learning from smaller numbers of examples. 15:35 Today, the learning systems that we have, 15:37 including deep neural networks, typically 15:39 require really large numbers of examples. 15:41 Which is why, as Jeff was describing, 15:43 they can take a long time to train, 15:44 and the experiment time can be slow. 15:48 So it's great that we can give systems 15:51 hundreds of thousands or millions of labeled examples, 15:53 but clearly, small children don't need to do that. 15:56 They can learn from very small numbers of examples. 15:58 So that's an open problem. 16:00 I think another very important problem in machine learning 16:02 is what the researchers call transfer learning, which 16:05 is learning something in one domain, 16:07 and then being able to apply it in another. 16:09 Right now, you have to build a system 16:11 to learn one particular task, and then that's not 16:13 transferable to another task. 16:14 So for example, the AlphaGo system that 16:17 won the Go Championship in Korea, 16:20 that system can't, a priori, play chess or tic tac toe. 16:24 So that's a big, big open problem 16:26 in machine learning that lots of people are interested in. 16:28 TOM SIMONITE: OK. 16:29 And Jeff, this is kind of on your group, to some extent, 16:33 isn't it? 16:34 You need to figure this out. 16:35 Are there particular avenues or recent results 16:38 that you would highlight that seem to be promising? 16:41 JEFF DEAN: Yeah. 16:42 I think we're making, actually, pretty significant progress 16:46 in doing a better job of language understanding. 16:48 I think, if you look at where computer vision was three 16:53 or four or five years ago, it was 16:54 kind of just starting to show signs of life, 16:57 in terms of really making progress. 16:58 And I think we're starting to see the same thing in language 17:02 understanding kinds of models, translation, parsing, question 17:06 answering kinds of things. 17:08 In terms of open problems, I think unsupervised 17:12 learning, being able to learn from observations 17:14 of the world that are not labeled, 17:15 and then occasionally getting a few labeled examples that 17:18 tell you, these are important things about the world 17:21 to pay attention to, that's really 17:23 one of the key open challenges in machine learning. 17:27 And one more, I would add, is, right now, 17:31 what you need a lot of machine learning expertise for 17:34 is to kind of device the right model structure 17:36 for a particular kind of problem. 17:38 For an image problem, I should use convolutional neural nets, 17:41 or for language problems, I should use this particular kind 17:44 of recurrent neural net. 17:46 And I think one of the things that 17:48 would be really powerful and amazing 17:50 is if the system itself could device the right structure 17:54 for the data it's observing. 17:57 So learning model structure concurrently 17:59 with trying to solve some set of tasks, I think, 18:02 would be a really great open research problem. 18:05 TOM SIMONITE: OK. 18:05 So instead of you having to design the system 18:08 and then setting it loose to learn, 18:11 the learning system would build itself, to some extent? 18:13 JEFF DEAN: Right. 18:14 Right now, you kind of define the scaffolding of the model, 18:17 and then you fiddle with parameters 18:18 as part of the learning process, but you don't sort of 18:21 introduce new kinds of connections 18:22 in the model structure itself. 18:24 TOM SIMONITE: Right. 18:25 OK. 18:25 And unsupervised learning, just giving it that label, 18:29 it makes it sound like one unitary problem, which 18:31 may not be true. 18:32 But will big progress on that come 18:36 from one flash of insight and a new algorithm, 18:41 or will it be-- I don't know-- a longer slog? 18:46 JEFF DEAN: Yeah. 18:47 If I knew, that would be [INAUDIBLE]. 18:50 I have a feeling that it's not going to be, like, 18:53 100 different things. 18:54 I feel like there's a few key insights 18:57 that new kinds of learning algorithms 19:00 could pick up on as to what aspects 19:03 of the world the model is observing are important. 19:06 And knowing which things are important 19:08 is one of the key things about unsupervised learning. 19:11 TOM SIMONITE: OK. 19:12 Aparna, so what Jeff's team kind of works out, eventually, 19:18 should come through into your hands, 19:19 and you could build stuff with it. 19:21 Is there something that you would really 19:23 like him to invent tomorrow, so you can start building 19:26 stuff with it the day after? 19:28 APARNA CHENNAPRAGADA: Auto generate emails. 19:30 No, I'm kidding. 19:32 I do think, actually, what's interesting is, you've heard 19:35 these building blocks, right? 19:36 So machine perception, computer vision, wasn't a thing, 19:40 and now it's actually reliable. 19:41 Language understanding, it's getting there. 19:44 Translation is getting there. 19:45 To me, the next other building block you can make machines do 19:49 is hand-eye coordination. 19:51 So you've seen the robot arms video 19:53 that Sundar talked about and showed at the keynote, 19:56 but imagine if you could kind of have these rote tasks that 20:00 are harder, tedious for humans, but if you 20:03 had reliable hand-eye coordination built in, that's 20:07 in a learned system versus a controlled system code 20:09 that you usually write, and it's very brittle, 20:11 suddenly, it opens up a lot more opportunities. 20:13 Just off the top of my head, why isn't there 20:16 anything for, like, elderly care? 20:18 Like, you are an 80-year-old woman with a bad back, 20:21 and you're picking up things. 20:23 Why isn't there something there? 20:24 Or even something as mundane with natural language 20:27 understanding, right? 20:28 I have a seven-year-old. 20:29 I'm a mom of a 7-year-old. 20:31 Why isn't there something for, I don't know, 20:33 math homework, with natural language understanding? 20:36 JOHN GIANNANDREA: So I think one of things 20:38 we've learned in the last few years 20:39 is that things that are hard for people 20:42 to do, we can teach computers to do, 20:44 and things that are easy for us to do 20:45 are still the hard problems for computers. 20:47 TOM SIMONITE: Right. 20:48 OK. 20:49 And does that mean we're still missing some big new field 20:56 we need to invent? 20:57 Because most of the things we've been talking about so far 20:59 have been built on top of this deep learning 21:01 and neural network. 21:02 JOHN GIANNANDREA: I think robotics work is interesting, 21:04 because it gives the computer system an embodiment 21:08 in the world, right? 21:10 So learning from tactile environments 21:13 is a new kind of learning, as opposed to just looking 21:16 at unsupervised or supervised. 21:17 Just reading text is a particular environment. 21:21 Perception, looking at images, looking at audio, 21:23 trying to understand what this song is, 21:25 that's another kind of problem. 21:27 I think interacting with the real world 21:29 is a whole other kind of problem. 21:30 TOM SIMONITE: Right. 21:30 OK. 21:31 That's interesting. 21:33 Maybe this is a good time to talk a little bit more 21:35 about DeepMind. 21:35 I know that they are very interested in this idea 21:38 of embodiment, the idea you have to submerge this learning 21:43 agent in a world that it can learn from. 21:45 Can you explain how they're approaching this? 21:47 JOHN GIANNANDREA: Yeah, sure. 21:48 I mean, DeepMind is another research group 21:49 that we have at Google, and we work closely with them 21:52 all the time. 21:53 They are particularly interested in learning from simulations. 21:57 So they've done a lot of work with video games 21:59 and simulations of physical environments, 22:01 and that's one of the research directions that they have. 22:04 It's been very productive. 22:06 TOM SIMONITE: OK. 22:08 Is it just games? 22:09 Are they moving into different types of simulation? 22:12 JOHN GIANNANDREA: Well, there's a very fine line 22:14 between a video game-- a three-dimensional video game-- 22:17 and a physics simulation already environment, right? 22:20 I mean, some video games are, in fact, 22:22 full simulations of worlds, so there's not really 22:26 a bright line there. 22:27 TOM SIMONITE: OK. 22:27 And do DeepMind work on robotics? 22:29 They don't, I didn't think. 22:30 JOHN GIANNANDREA: They're doing a bunch of work 22:32 in a bunch of different fields, some of which 22:33 gets published, some of which is not. 22:35 TOM SIMONITE: OK. 22:36 And the robot arms that we saw in the keynote on Wednesday, 22:40 are they within your group, Jeff? 22:41 JEFF DEAN: Yes. 22:42 TOM SIMONITE: OK. 22:42 So can you tell us about that project? 22:44 JEFF DEAN: Sure. 22:44 So that was a collaboration between our group 22:46 and the robotics teams in Google X. Actually, what happened was, 22:52 one of our researchers discovered 22:53 that the robotics team, actually, 22:55 had 20 unused arms sitting in a closet somewhere. 22:59 They were a model that was going to be discontinued 23:01 and not actually used. 23:02 So we're like, hey, we should set these up in a room. 23:06 And basically, just the idea of having 23:10 a little bit larger scale robotics test environment 23:12 than just one arm, which is what you typically 23:14 have in a physical robotics lab, would 23:18 make it possible to do a bit more exploratory research. 23:22 So one of the first things we did with that was just 23:24 have the robots learn to pick up objects. 23:27 And one of the nice properties that has, 23:29 it's a completely supervised problem. 23:32 The robot can try to grab something, 23:34 and if it closes its griper all the way, it failed. 23:36 And if it didn't close it all the way, 23:38 and it picked something up, it succeeded. 23:40 And so it's learning from raw camera pixel inputs 23:44 directly to torque motor controls. 23:45 And there's just a neural net there 23:47 that's trained to pick things up based on the observations it's 23:51 making of things as it approaches a particular object. 23:55 TOM SIMONITE: And is that quite a slow process? 23:57 I mean, that fact that you have multiple arms going 23:59 at once made me think that, maybe, you 24:02 were trying to maximize your throughput, or something. 24:04 JEFF DEAN: Right. 24:05 So if you have 20 arms, you get 20 times as much experience. 24:08 And if you think about how small kids learn to pick stuff up, 24:11 it takes them maybe a year, or something, 24:13 to go from being able to move their arm to really be 24:17 able to grasp simple objects. 24:19 And by parallelizing this across more arms, 24:22 you can pool the experience of the robotic arms a bit. 24:24 TOM SIMONITE: I see. 24:25 OK. 24:27 JEFF DEAN: And they need less sleep. 24:29 TOM SIMONITE: Right. 24:31 John, at the start of the session, 24:32 you referred to this concept of AI winter, 24:35 and you said you thought it was spring. 24:39 When do we know that it's summer? 24:43 JOHN GIANNANDREA: Summer follows spring. 24:45 I mean, there's still a lot of unsolved problems. 24:47 I think problems around dialogue and language 24:49 are the ones that I'm particularly interested in. 24:52 And so, until we can teach a computer to really read, 24:56 I don't think we can declare that it's summer. 24:59 I mean, if you can imagine a computer's really reading 25:02 and internalizing a document. 25:04 So it's interesting. 25:05 So translation is reading a paragraph in one language 25:08 and writing it in another language. 25:10 In order to do that really, really well, 25:12 you have to be able to paraphrase. 25:13 You have to be able to reorder words, and so on and so 25:15 forth So imagine translating something 25:17 from English to English. 25:18 So you read a paragraph, and you write a different paragraph. 25:21 If we could do that, I think I would declare summer. 25:25 TOM SIMONITE: OK. 25:26 Reading is-- well, there are different levels of reading, 25:30 aren't there? 25:31 Do you know-- 25:33 JOHN GIANNANDREA: If you can paraphrase, then you really-- 25:35 TOM SIMONITE: Then you think that-- if you 25:36 could reach that level. 25:37 JOHN GIANNANDREA: And actually understood-- 25:37 TOM SIMONITE: Then you've got some argument. 25:39 JOHN GIANNANDREA: And to a certain extent, 25:40 today, our translation systems, which 25:42 are not perfect by any means, are getting better. 25:45 They do do some of that. 25:46 They do do some paraphrasing. 25:48 They do do some re-ordering. 25:49 They do do a remarkable amount of language understanding. 25:52 So I'm hopeful researchers around the world 25:54 will get there. 25:55 And it's very important to us that our natural language 25:57 APIs become part of our cloud platform, 25:59 and that people can experiment with it, and help. 26:02 JEFF DEAN: One thing I would say is, 26:04 I don't think there's going to be 26:05 this abrupt line between spring and summer, right? 26:08 There's going to be developments that push the state of the art 26:11 forward in lots of different areas in kind 26:13 of this smooth gradient of capabilities. 26:16 And at some point, something becomes 26:18 possible that didn't used to be possible, 26:21 and people kind of move the goalposts 26:23 of what they think of as really, truly hard problems. 26:28 APARNA CHENNAPRAGADA: The classic joke, right? 26:30 It's only AI until it starts working, 26:32 and then it's computer science. 26:34 JEFF DEAN: Like, if you'd asked me four years ago, 26:36 could a computer write a sentence 26:38 given an image as input? 26:40 And I would have said, I don't think they 26:42 can do that for a little while. 26:43 And they can actually do that today, 26:44 and that's kind of a good example of something 26:46 that has made a lot of progress in the last few years. 26:48 And now you sort of say, OK, that's in our tool 26:51 chest of capabilities. 26:53 TOM SIMONITE: OK. 26:53 But if we're not that great at predicting 26:56 how the progress goes, does that mean we can't see winter, 27:00 if it comes back? 27:04 JOHN GIANNANDREA: If we stop seeing progress, 27:06 then I think we could question what the future's going 27:09 to look like. 27:10 But today, the rate of-- I think researchers in the field 27:14 are excited about this, and maybe the field 27:16 is a little bit over-hyped because of the rate of progress 27:18 we're seeing. 27:19 Because something like speech recognition, 27:21 which didn't work for my wife five years ago, 27:23 and now works flawlessly, because image identification 27:29 is now working better than human raters for many fields. 27:32 So there's these narrow fields for which algorithms are not 27:36 superhuman in their capabilities. 27:37 So we're seeing tremendous progress. 27:39 And so it's very exciting for people working in this field. 27:42 TOM SIMONITE: OK. 27:43 Great. 27:44 I should just note that, in a couple of minutes, 27:46 we will open up the floor for questions. 27:48 There are microphones here and here in the main seating area, 27:52 and there's one microphone up in the press area, which 27:55 I can't see right now, but hopefully you 27:57 can figure out where it is. 28:01 Sundar Pichai, CEO of Google, has spoken a lot recently 28:04 about how he thinks we're moving from a world which 28:06 is mobile-first to AI-first. 28:11 I'm interested to hear what you think that means. 28:13 Maybe, Aparna, you could speak to that. 28:16 APARNA CHENNAPRAGADA: I interpret 28:18 it a couple different ways. 28:19 One is, if you look at how mobile's changed, 28:21 how you experience computing, it's 28:25 not happened at one level of the stack, right? 28:28 It's at the interface level, it's 28:29 at the information level, and infrastructure. 28:31 And I think that's the same thing that's 28:33 going to happen with AI and any of these machine learning 28:36 techniques, which is, you'll have infrastructure layer 28:39 improvements. 28:39 You saw the announcement about TPU. 28:41 You'll have a bunch of algorithms and models 28:44 improvements at the intelligence and information layer, 28:47 and there will be interface changes. 28:48 So the best UI is probably no UI. 28:51 TOM SIMONITE: Right. 28:52 OK. 28:53 John, what does AI-first mean to you? 28:57 JOHN GIANNANDREA: I think it means 28:58 that this assistant kind of layer is available to you 29:01 wherever you are. 29:02 Whether you're in your car, or whether it's 29:05 ambient in your house, or whether you're 29:07 using your mobile device or laptop, 29:10 that there is this smart assistance 29:12 that you find very quietly useful to you all the time. 29:17 Kind of how Google search is for most people today. 29:19 I think most people would not want search engines taken away 29:23 from them, right? 29:24 So I think that being that useful to people, 29:26 so that people take it for granted, 29:27 and then it's ambient across all your devices, 29:29 is what AI-first means to me. 29:31 TOM SIMONITE: And we're in the early stages of this, 29:33 do you think? 29:34 JOHN GIANNANDREA: Yeah. 29:35 It's a journey, I think. 29:36 It's a multi-year journey 29:37 TOM SIMONITE: OK. 29:38 Great. 29:39 So thanks for a fascinating conversation. 29:41 Now, we'll let someone else ask the questions for a little bit. 29:44 I will alternate between the press mic and the mics 29:49 down here at the front. 29:51 Please keep your questions short, 29:53 so we can get through more of them, 29:54 and make sure they're questions, not statements. 29:58 We will start with the press mic, wherever it is. 30:13 MALE SPEAKER: There's nobody there. 30:14 TOM SIMONITE: I really doubt the press has no questions. 30:18 What's happening? 30:18 Why don't we start with the developer mic 30:20 right here on the right? 30:23 AUDIENCE: I have a philosophical question about prejudice. 30:28 People tend to have prejudice. 30:31 Do you think this is a step stone 30:33 that we need to take in artificial intelligence, 30:36 and how would society accept that? 30:40 JOHN GIANNANDREA: I'm not sure I understand the question. 30:43 Some people have prejudice, and? 30:46 AUDIENCE: Some people have the tendency 30:49 to have prejudice, which might lead to behaviors 30:53 such as discrimination. 30:56 TOM SIMONITE: So the question is, 30:57 will the systems that the people build have biases? 31:00 JOHN GIANNANDREA: Oh, I see. 31:01 I see. 31:02 Will people's prejudices creep into machine learning systems? 31:05 I think that is a risk. 31:07 I think it all depends on the training data that we choose. 31:10 We've already seen some issues with this kind of problem. 31:13 So I think it all depends on carefully 31:14 selecting training data, particularly 31:16 for supervised systems. 31:19 TOM SIMONITE: OK. 31:21 Is the press mic working, at this point? 31:23 SEAN HOLLISTER: Hi. 31:24 I'm Sean Hollister, up here in the press mic. 31:26 TOM SIMONITE: Great. 31:27 Go for it. 31:28 SEAN HOLLISTER: Hi, there. 31:29 I wanted to ask about the role of privacy in machine learning. 31:33 You need a lot of data to make these observations 31:38 and to help people with machine learning. 31:41 I give all my photos to Google Photos, 31:44 and I wonder what happens to them afterwards. 31:47 What allows Google to see what they 31:49 are, and is that ever shared in any way with anyone else? 31:53 Personally, I don't care very much about that. 31:55 I'm not worried my photos are going 31:57 to get out to other folks, but where do they go? 32:00 What do you do with them? 32:01 And to what degree are they protected? 32:04 JEFF DEAN: Do you want to take that one? 32:06 APARNA CHENNAPRAGADA: I think this 32:07 is one of the most important things 32:09 that we look at across products. 32:12 So even with photos, or Google Now, 32:14 or voice, and all of these things. 32:16 There's actually two principles we codify into building this. 32:20 One is, there's a very explicit-- 32:22 it's a very transparent contract between the user 32:25 and the product that is, you basically know what benefits 32:29 you're getting with the data, and the data 32:31 is there to help you. 32:32 That's one principle. 32:34 But the second is, by default, it's an opt-in experience. 32:39 You're in the driver's seat. 32:40 In some sense, let's say, you're saying, 32:42 hey, I do want to get traffic information when 32:45 I'm on Shoreline, because it's clogged up to Shoreline 32:48 Amphitheater, you, of course, need the system 32:50 to know where your location is. 32:51 Because you don't want to know how the traffic is in Napa. 32:55 So having that contract be transparent, but also 32:58 an opt-in, I think it really addresses the equation. 33:04 But I think the other thing to add in here 33:06 is also that, by definition, all of these are for your eyes 33:11 only, right? 33:12 In terms of, like, all your data is yours, and that's an axiom. 33:16 JOHN GIANNANDREA: And to answer his question, 33:18 we would never share his photos. 33:19 We train models based on other photos that are not yours, 33:24 and then the machine looks at your photos, 33:26 and it can label it, but we would never 33:27 share your private photo there. 33:29 SEAN HOLLISTER: To what degree is advertising 33:31 anonymously-targeted at folks like me, 33:34 based on the contents of things I upload, 33:37 little inferences you make in the meta data? 33:40 Is any of that going to advertisers in any way, 33:44 even in aggregate, hey, this is a person who 33:47 seems to like dogs? 33:50 JOHN GIANNANDREA: For your photos? 33:51 No. 33:52 Absolutely not. 33:52 APARNA CHENNAPRAGADA: No. 33:53 TOM SIMONITE: OK. 33:53 Let's go to this mic right here. 33:55 AUDIENCE: My questions is for Aparna, about, 33:58 what is the thought process behind creating a new product? 34:02 Because there are so many things that these guys are creating. 34:05 So how do you go from-- because it's kind of obvious right 34:08 now to see if you have my emails, 34:10 and you know that I'm traveling tomorrow to New York, 34:14 it's kind of simple to do that on my calendar 34:16 and create an event. 34:17 How do you go from robotic arms, trying 34:21 to understand how to get things, to an actual product? 34:25 The question is, what is the thought process behind it? 34:27 APARNA CHENNAPRAGADA: Yeah. 34:27 I'll give you the short version of it. 34:29 And, obviously, there's a longer version of it. 34:32 Wait for the medium post. 34:33 But I think the short version of it 34:35 is, to echo one thing JG said, you 34:38 want to pick problems that are easy for machines 34:41 and hard for humans. 34:42 So AI plus machine learning is not 34:45 going to turn a non-problem into a real problem 34:47 that people need solving. 34:49 It's like, you can take Christopher Nolan and Ben 34:53 Affleck, and you can still end up with Batman Versus Superman. 34:56 So you want to make sure that the problem you're solving 34:59 is a real one. 35:00 Many of our failures, even internally 35:02 and external, like frenzy around bots and AI, 35:06 is when you kid yourself that the problem needs solving. 35:09 And the second one, the second quick insight there, 35:12 is that you also want to build an iterative model. 35:15 That is, you want to kind of start small, and say, hey, 35:18 travel needs some assistance. 35:19 What are the top five things that people need help with? 35:22 And see which of these things can scale. 35:25 JEFF DEAN: I would add one thing to that, 35:26 which is, often, we're doing research 35:29 on a particular kind of problem. 35:31 And then, when we have something we think is useful, 35:34 we'll share that internally, as presentations or whatever, 35:37 and maybe highlight a few places where 35:39 we think this kind of technology could be used. 35:42 And that's sort of a good way to inform the product designers 35:45 about what kinds of things are now possible that 35:49 didn't used to be possible. 35:50 TOM SIMONITE: OK. 35:51 Let's have another question from the press section up there. 35:54 AUDIENCE: Yeah. 35:54 There's a lot of talk, lately, about sort of a fear of AI. 35:59 Elon Musk likened it to summoning the demon. 36:04 Whether that's overblown or not, whether it's 36:07 perception versus reality, there seems 36:10 to be a lot of mistrust or fear of going 36:13 too far in this direction. 36:15 How much stock you put into that? 36:18 And how do you win the trust of the public, when 36:22 you show experiments like the robot arm thing 36:24 on the keynote, which was really cool, but sort 36:26 of simultaneously creepy at the same time? 36:29 JOHN GIANNANDREA: So I get this question a lot. 36:31 I think there's this notion that's 36:34 been in the press for the last couple of years 36:36 about so-called super intelligence, 36:38 that somehow AI will beget more AI, 36:40 and then it will be exponential. 36:42 I think researchers in the field don't put much stock in that. 36:46 I don't think we think it's a real concern yet. 36:48 In fact, I think we're a long way away 36:49 from it being a concern. 36:51 There are some researchers who actually 36:53 think about these ethical problems, 36:55 and think about AI safety, and we 36:56 think that's really important. 36:57 And we work on this stuff with them, 37:00 and we support that kind of work. 37:01 But I think it's a concern that is decades and decades away. 37:06 It's also conflated with the fact 37:08 that people look at things like robots learning 37:10 to pick things up, and that's somehow 37:12 inherently scary to people. 37:14 I think it's our job, when we bring products 37:16 to market, to do it in a thoughtful way 37:19 that people find genuinely useful. 37:21 So a good example I would give you is, in Google products, 37:26 when you're looking for a place, like a coffee shop 37:28 or something, we'll show you when it's busy. 37:30 And that's the product of fairly advanced machine learning 37:34 that takes aggregate signals in a privacy-preserving way 37:36 and says, yeah, this coffee shop is really 37:38 busy on a Saturday morning. 37:39 That doesn't seem scary to me, right? 37:41 That doesn't seem anything like a bad thing 37:46 to bring into the world. 37:47 So I think there's a bit of a disconnect between the somewhat 37:50 extended hype, and the actual use of this technology 37:52 in everyday products. 37:54 TOM SIMONITE: OK. 37:54 Next question. 37:55 AUDIENCE: Thank you. 37:56 So given Google's source of revenue 37:58 and the high use of ad blockers, is there 38:02 any possibility of using machine learning 38:04 to maybe ensure that the appropriate ads are served? 38:07 Or if there's multiple versions of the same ad, 38:10 that the ad that would apply most to me 38:12 would be served to me, and to a different user, 38:14 a different version, and things like that? 38:16 Is that on the roadmap? 38:17 JEFF DEAN: Yeah. 38:18 I think, in general, there's a lot 38:20 of potential applications of machine 38:21 learning to advertising. 38:24 Google has actually been using machine 38:25 learning in our advertising system for more than a decade. 38:29 And I think one of the things about deciding 38:34 what ads to show to users is, you 38:35 want them to be relevant and useful to that user. 38:38 And it's better to not show an ad at all, 38:40 if you don't have something that seems plausibly relevant. 38:44 And that's always been Google's advertising philosophy. 38:47 And other websites on the web don't necessarily quite 38:51 have the same balance, in that respect. 38:53 But I do think there's plenty of opportunity to continue 38:56 to improve advertising systems and make them better, 38:59 so that you see less ads, but they're actually more useful. 39:03 TOM SIMONITE: OK. 39:03 Next question from at the top. 39:05 JACK CLARK: Jack Clark with Bloomberg News. 39:08 So how do you differentiate to the user 39:12 between a sponsored advert, and one that is provided by your AI 39:17 naturally? 39:18 How do I know that the burger joint you're suggesting 39:21 is like a paid-for link, or is it a genuine link? 39:26 JEFF DEAN: So in our user interfaces, 39:27 we always clearly delimit advertisements. 39:30 And in general, all ads that we show 39:33 are selected algorithmically by our systems. 39:36 They're not like, you can just give us an ad, 39:38 and we will always show it to someone. 39:40 We always decide what is the likelihood 39:43 that this ad is going to be useful to someone, 39:45 before we decide to show that advertiser's ad. 39:48 JACK CLARK: Does this extend to stuff like Google Home, where 39:51 it will say, this is a sponsored restaurant 39:53 we're going to send you to. 39:57 JEFF DEAN: I don't know that product. 39:58 JOHN GIANNANDREA: I mean, we haven't 40:00 launched Google Home yet. 40:01 So a lot of these product decisions are still to be made. 40:05 I think we do, as a general rule, 40:08 clearly identify when something is sponsored 40:10 versus when it's organic. 40:13 TOM SIMONITE: OK. 40:13 Next question here. 40:15 AUDIENCE: Hi. 40:15 This is a question for Jeff Dean. 40:19 I'm very much intrigued by the Google Brain project 40:22 that you're doing. 40:22 Very cool t-shirt. 40:25 The question is, what is the road map of that, 40:28 and how does it relate to the point of singularity? 40:32 JEFF DEAN: Aha. 40:34 So the road map of-- this is sort of the project code name 40:41 for the team that I work on. 40:43 Basically, the team was developed 40:45 to investigate the use of advanced methods 40:49 in machine learning to solve difficult problems in AI. 40:54 And we're continuing to work on pushing the state 40:57 of the art in that area. 40:59 And I think that means working in lots of different areas, 41:01 building the right kinds of hardware with TPUs, 41:04 building the right systems infrastructure with things 41:07 like TensorFlow. 41:08 Solving the right research problems 41:10 that are not connected to products, 41:14 and then figuring out ways in which machine learning can 41:17 be used to advance different kinds of fields, 41:22 as we solve different problems along the road. 41:25 I'm not a big believer in the singularity. 41:27 I think all exponentials look like exponentials 41:30 at the beginning, but then they run out of stuff. 41:34 TOM SIMONITE: OK. 41:35 Thanks for the question. 41:36 Back to the pressbox. 41:38 STEVEN MAX PATTERSON: Hi. 41:39 Steven Max Patterson, IDG. 41:41 I was looking at Google Home and Google Assistant, 41:45 and it looks like it's really a platform. 41:50 And it's a composite of other platforms, 41:53 like the Knowledge Graph, Google Cloud Speech, Google machine 41:58 learning, the Awareness API. 42:00 Is this a feature that other consumer device manufacturers 42:06 could include, and is that the intent and direction of Google, 42:09 is to make this a platform? 42:13 JOHN GIANNANDREA: It's definitely 42:14 the case that most of our machine learning APIs 42:18 are migrating to the cloud platform, which enables people 42:21 to use, for example, our speech capabilities in other products. 42:25 I think the Google Assistant is intended to be, actually, 42:27 a holistic product delivered from Google. 42:29 That makes sense. 42:30 But it may make sense to syndicate 42:32 that to other manufacturers at some point. 42:34 We don't have any plans to do that today. 42:36 But in general, we're trying to be 42:37 as open as we can with the component pieces 42:39 that you just mentioned, and make 42:41 them available as Cloud APIs, and in many cases, 42:43 as open source solutions as well. 42:45 JEFF DEAN: Right. 42:46 I think one of the things about that 42:47 is, making those individual pieces available 42:49 enables everyone in the world to take advantage of some 42:53 of the machine learning research we've done, 42:55 and be able to do things like label images, 42:57 or do speech recognition really well. 42:59 And then they can go off and build 43:00 really cool, amazing things that aren't necessarily 43:03 the kinds of things we're working on. 43:05 JOHN GIANNANDREA: Yeah, and many companies are doing this today. 43:07 They're using our translate APIs. 43:08 They're using our Cloud Speech APIs today. 43:12 TOM SIMONITE: Right. 43:13 We have time for one last quick question from this mic here. 43:15 AUDIENCE: Hi. 43:16 I'm [INAUDIBLE]. 43:18 John, you said that you would declare summer 43:22 if, in language understanding, it 43:25 would be able to translate from one paragraph in English 43:30 to another paragraph in English. 43:32 Don't you think that making that possible requires 43:35 really complete understanding of the world, and everything 43:40 that's going on, just to catch the emotional level that 43:44 is in the paragraph, or even the physical understanding 43:47 of the world around us? 43:50 JOHN GIANNANDREA: Yeah, I do. 43:52 I use that example because it is really, really hard. 43:55 So I don't think we're going to be done for many, many years. 43:58 I think there's a lot of work to do. 44:00 We built the Google Knowledge Graph, in part, 44:02 to answer that question, so that we actually 44:03 had some semantic understanding of at least 44:05 the things in the world, and some of the relationships 44:07 between them. 44:08 But yeah, it's a very hard problem. 44:09 And I used that example because it's 44:11 pretty clear we won't be done for a long time. 44:13 TOM SIMONITE: OK. 44:14 Sorry, there's no time for other questions. 44:16 Thanks for the question. 44:17 A good forward-looking note to end on. 44:20 We'll see how it works out over the coming years. 44:23 Thank you for joining me, all of you on stage, 44:25 and thanks for the questions and coming for the session. 44:29 [MUSIC PLAYING]
Transcripción : Youtube
