9:59:59.000,9:59:59.000 Okay, so, good morning everyone. 9:59:59.000,9:59:59.000 I'll just get started. 9:59:59.000,9:59:59.000 My name is Shailesh and I give these talks 9:59:59.000,9:59:59.000 almost every year so this is a very deja-vu feeling for me. 9:59:59.000,9:59:59.000 The only thing different this time 9:59:59.000,9:59:59.000 is the stage is slightly thinner. 9:59:59.000,9:59:59.000 But great crowd, great list of talks so far. 9:59:59.000,9:59:59.000 So, Daniel called me a couple of weeks ago and said 9:59:59.000,9:59:59.000 "Why don't you give a keynote again?" 9:59:59.000,9:59:59.000 And I said, "You know, I'm running out of things to say now." 9:59:59.000,9:59:59.000 I've given four talks at different forums 9:59:59.000,9:59:59.000 with The Fifth Elephant and I wasn't so sure 9:59:59.000,9:59:59.000 what I want to talk about. 9:59:59.000,9:59:59.000 So, then, one of these days I was talking 9:59:59.000,9:59:59.000 to one of my non-geek friends 9:59:59.000,9:59:59.000 and he was very excited about what I do 9:59:59.000,9:59:59.000 so he said, 'What do you do?' 9:59:59.000,9:59:59.000 and I, you know, it was on the phone 9:59:59.000,9:59:59.000 and I started talking to him about this, that, and the other. 9:59:59.000,9:59:59.000 And for about 45 minutes I was rambling 9:59:59.000,9:59:59.000 and this guy was very quiet. 9:59:59.000,9:59:59.000 I didn't realise he wasn't a techie 9:59:59.000,9:59:59.000 and I was going on and on and after 45 minutes I stopped 9:59:59.000,9:59:59.000 and said, "Are you still there? 9:59:59.000,9:59:59.000 "Are you listening?" 9:59:59.000,9:59:59.000 And he said, "Yeah, I'm listening. 9:59:59.000,9:59:59.000 "Can you tell me what do you do again?"[br](audience laughs) 9:59:59.000,9:59:59.000 And then I realized, how do I summarize this in 2 words? 9:59:59.000,9:59:59.000 So then I told him, "Hey, I'm building thinking machines." 9:59:59.000,9:59:59.000 And that's when he said, "Why didn't you say that before? 9:59:59.000,9:59:59.000 "It was so easy to say that, right?" 9:59:59.000,9:59:59.000 So that's how the title came by 9:59:59.000,9:59:59.000 and obviously we're not building thinking machines 9:59:59.000,9:59:59.000 but what I'm going talk about is towards thinking machines, right? 9:59:59.000,9:59:59.000 So, we have a long way to go. 9:59:59.000,9:59:59.000 So I added the word "towards" later. 9:59:59.000,9:59:59.000 So what I'm gonna talk about is all over the place. 9:59:59.000,9:59:59.000 I'm going to talk about philosophy, science fiction. 9:59:59.000,9:59:59.000 I'm going to talk about algorithms 9:59:59.000,9:59:59.000 and I'm going to talk about, you know, deep learning 9:59:59.000,9:59:59.000 and how to think about things beyond deep learning. 9:59:59.000,9:59:59.000 All right? 9:59:59.000,9:59:59.000 And let me give you a perspective and then we'll start. 9:59:59.000,9:59:59.000 So I'll take questions at the end. 9:59:59.000,9:59:59.000 Start working this. 9:59:59.000,9:59:59.000 All right, so, I ended my last year's talk on this quotation 9:59:59.000,9:59:59.000 So I thought I'll start on this quotation this time. 9:59:59.000,9:59:59.000 So I like this quotation because it puts a lot 9:59:59.000,9:59:59.000 of things into perspective of what we're doing, 9:59:59.000,9:59:59.000 how our civilisation got here, and where we're headed. 9:59:59.000,9:59:59.000 So it says, "Our technology, our machines, is part of our humanity. 9:59:59.000,9:59:59.000 "We created them to extend ourselves 9:59:59.000,9:59:59.000 "and that is what is unique about human beings!" 9:59:59.000,9:59:59.000 And if you look at chairs, and dogs, and animals, and cats 9:59:59.000,9:59:59.000 they don't create machines to extend themselves. 9:59:59.000,9:59:59.000 They just have instincts and they follow their instincts. 9:59:59.000,9:59:59.000 Right, that's very unique about human civilization. 9:59:59.000,9:59:59.000 We've created Taj Mahal, and space flights, and internet 9:59:59.000,9:59:59.000 and so we've come a very long way. 9:59:59.000,9:59:59.000 So if you think about the tools, right? 9:59:59.000,9:59:59.000 The cavemen had tools and now we have 9:59:59.000,9:59:59.000 a completely robotic assembly line with no humans 9:59:59.000,9:59:59.000 and you could turn the lights off and nothing will happen 9:59:59.000,9:59:59.000 the cars will get produced, right? 9:59:59.000,9:59:59.000 We've gone from just on-road, bullock carts, 9:59:59.000,9:59:59.000 to massive amounts of transportation that we can do now. 9:59:59.000,9:59:59.000 If you look at our ability to look further 9:59:59.000,9:59:59.000 into space, again, since Galileo, 9:59:59.000,9:59:59.000 we have made a lot of progress. 9:59:59.000,9:59:59.000 Recently we saw the news of Pluto flyby 9:59:59.000,9:59:59.000 so now we're able to send satellites into space. 9:59:59.000,9:59:59.000 If you look at the first computer we built 9:59:59.000,9:59:59.000 and where we are today, right? 9:59:59.000,9:59:59.000 We have a huge data center, and really 9:59:59.000,9:59:59.000 if you look at the whole thing in perspective 9:59:59.000,9:59:59.000 we have made an enormous amount of progress 9:59:59.000,9:59:59.000 in the last so many centuries, right? 9:59:59.000,9:59:59.000 So if you look just at the technical part 9:59:59.000,9:59:59.000 the IT kind of intelligent machines 9:59:59.000,9:59:59.000 we're not talking about mixies 9:59:59.000,9:59:59.000 and other things, just look at what AI 9:59:59.000,9:59:59.000 and deep learning, this stuff, has produced. 9:59:59.000,9:59:59.000 Today's machines can play chess. 9:59:59.000,9:59:59.000 And there's no human on the planet 9:59:59.000,9:59:59.000 who can play chess better than the machine. 9:59:59.000,9:59:59.000 I want to take a pause and think about where we are. 9:59:59.000,9:59:59.000 There's no human on the planet 9:59:59.000,9:59:59.000 who can play chess better than the machine. 9:59:59.000,9:59:59.000 There's no human on the planet 9:59:59.000,9:59:59.000 who can play Jeopardy better than the machine. 9:59:59.000,9:59:59.000 And recently, Google came out with automatic cars 9:59:59.000,9:59:59.000 so the machines can drive cars and record show 9:59:59.000,9:59:59.000 that these cars are better than humans under ideal conditions 9:59:59.000,9:59:59.000 And they have much less accident rates 9:59:59.000,9:59:59.000 and all the accidents happened because of other humans drivers. 9:59:59.000,9:59:59.000 They're not because of cars. 9:59:59.000,9:59:59.000 And recently also saw how machines 9:59:59.000,9:59:59.000 are able to create pictures, right? 9:59:59.000,9:59:59.000 So this is one of the things 9:59:59.000,9:59:59.000 that deep learning is internally doing. 9:59:59.000,9:59:59.000 And now think about all this. 9:59:59.000,9:59:59.000 Just think about where machines have gone today. 9:59:59.000,9:59:59.000 How many things they can do 9:59:59.000,9:59:59.000 which are way beyond our imagination 9:59:59.000,9:59:59.000 that machines could have done. 9:59:59.000,9:59:59.000 So obviously there's a lot they've done. 9:59:59.000,9:59:59.000 But can they do the following? 9:59:59.000,9:59:59.000 So obviously there's a lot they've done. 9:59:59.000,9:59:59.000 So one of the holy grails of AI is to have a machine 9:59:59.000,9:59:59.000 have a conversation with a human being. 9:59:59.000,9:59:59.000 We all know the Turing test and the 9:59:59.000,9:59:59.000 repercussions of this will be huge. 9:59:59.000,9:59:59.000 We could think about how we talk to the internet today. 9:59:59.000,9:59:59.000 We carefully craft word-for-word queries, right? 9:59:59.000,9:59:59.000 And you know, we allow the internet to make mistakes. 9:59:59.000,9:59:59.000 We craft queries again, and we take the suggestions or not. 9:59:59.000,9:59:59.000 We talk to the internet like we're talking to a three-year-old. 9:59:59.000,9:59:59.000 Now in the daily needs of massive data computers, NLP 9:59:59.000,9:59:59.000 and all its deploying staff, imagine how shameful it is 9:59:59.000,9:59:59.000 to talk to a computer like a 3-year-old. 9:59:59.000,9:59:59.000 So it's got the capacity of thousands of people 9:59:59.000,9:59:59.000 but it can't understand language. 9:59:59.000,9:59:59.000 So we need to change that. 9:59:59.000,9:59:59.000 Now imagine beyond keywords what can happen. 9:59:59.000,9:59:59.000 We can do question answering 9:59:59.000,9:59:59.000 but how do we do question answering today? 9:59:59.000,9:59:59.000 We've created Yahoo Answers, we've created Quora 9:59:59.000,9:59:59.000 where people can type questions 9:59:59.000,9:59:59.000 We do a match between the questions and the answers 9:59:59.000,9:59:59.000 and then we again do retrieval. 9:59:59.000,9:59:59.000 So not answering questions. 9:59:59.000,9:59:59.000 Now think about conversations.[br]Conversation is an even more complex thing 9:59:59.000,9:59:59.000 If it works out, what are the[br]repercussions? I don't want to study 9:59:59.000,9:59:59.000 physics from my physics teacher. I want to[br]study from Einstein or Feynman. 9:59:59.000,9:59:59.000 We already know all the language and[br]knowledge of these people. 9:59:59.000,9:59:59.000 Can we not have a persona or a person[br]Feynman or Einstein, 9:59:59.000,9:59:59.000 and have a conversation with that person,[br]right? So just imagine the future 9:59:59.000,9:59:59.000 of what will happen if we're just able to[br]have conversations with the machines. 9:59:59.000,9:59:59.000 So there's a long way to go between[br]keyword search and conversations. 9:59:59.000,9:59:59.000 Can we discover a cure for cancer?[br]There are a lot of diseases out there. 9:59:59.000,9:59:59.000 Now obviously there is a lot of research[br]pharma companies are doing. 9:59:59.000,9:59:59.000 There's a lot of new initiatives on how[br]to use the high end machine learning 9:59:59.000,9:59:59.000 in pharma research. But my contention is[br]I believe that the cure for a lot of 9:59:59.000,9:59:59.000 diseases is already out there. In all the[br]medical literature, if somebody could 9:59:59.000,9:59:59.000 actually read them, hold that knowledge in[br]the brain, in RAM, and do interconnections 9:59:59.000,9:59:59.000 we should be able to find a lot of things.[br]But what is the problem? 9:59:59.000,9:59:59.000 A single human expert, even in one field[br]cannot keep up with that quest of 9:59:59.000,9:59:59.000 knowledge, right. We forget some things,[br]we want to read certain papers. 9:59:59.000,9:59:59.000 And therefore, it's the other problem. [br]We have too much knowledge 9:59:59.000,9:59:59.000 and our individual brains are not[br]capable of forming those connections 9:59:59.000,9:59:59.000 in the - because we can't even read that[br]many docs, right? 9:59:59.000,9:59:59.000 But machines could do it, the way, and[br]then there's progress. 9:59:59.000,9:59:59.000 Can we not find cures or new medicine[br]too. 9:59:59.000,9:59:59.000 Can I crack the next IIT Entrance Exam?[br]You laughing today, but you never know. 9:59:59.000,9:59:59.000 Five years from now, what will happen?[br]We should hope that if Watson is 9:59:59.000,9:59:59.000 a test of intelligence, if Igloo is a test[br]of intelligence, could this not be 9:59:59.000,9:59:59.000 a test of intelligence.[br]The ability of AI system to be able to 9:59:59.000,9:59:59.000 actually solve an IIT paper and get a[br]rank 1. 9:59:59.000,9:59:59.000 What about, can I search all the video[br]scenes, which only have a goal shot 9:59:59.000,9:59:59.000 in the football videos and nothing else.[br]I don't want to watch the rest of it. 9:59:59.000,9:59:59.000 A lot of balls going here and there.[br]I just want to see the goal shots. 9:59:59.000,9:59:59.000 Today I cannot do that.[br]Can my machines be intelligent enough 9:59:59.000,9:59:59.000 to vision part, to actually find, this is[br]a goal, this is a goal, this is a goal - 9:59:59.000,9:59:59.000 the rest of it is something else.[br]So we can imagine the applications now. 9:59:59.000,9:59:59.000 We were talking about sarcasm a lot and we[br]all understand sarcasm is a very hard 9:59:59.000,9:59:59.000 thing to do. And imagine if you could[br]detect sarcasm, what else can you do? 9:59:59.000,9:59:59.000 You writing an email to your boss[br]You're angry, you've written 9:59:59.000,9:59:59.000 a sarcastic comment, and ? says,[br]'Hey are you sure about this?' 9:59:59.000,9:59:59.000 In the heat of the moment, can[br]I put it this way? 9:59:59.000,9:59:59.000 So, like, today we do attachments. Can you[br]detect sarcasm and things like that. 9:59:59.000,9:59:59.000 And to me the holy grail of AI is not[br]really all these big things, 9:59:59.000,9:59:59.000 but a really simple thing. Can a machine[br]find a joke funny? 9:59:59.000,9:59:59.000 Now there are a lot of - don't know if you[br]guys watch Star Trek - but data entry 9:59:59.000,9:59:59.000 300, 400 years from now, is an android who[br]is capable of all these other things. 9:59:59.000,9:59:59.000 He's a great supercomputer in human form[br]but he's still struggling with humans. 9:59:59.000,9:59:59.000 That's how hard the problem is.[br]So obviously we have a long way to go. 9:59:59.000,9:59:59.000 We've come a long way and we have a long[br]way to go. 9:59:59.000,9:59:59.000 So this talk is really about the way[br]forward. 9:59:59.000,9:59:59.000 So, what do we imagine the future to be?[br]We want something like this. 9:59:59.000,9:59:59.000 Golden ?[br]We all want a Jarvis, right? 9:59:59.000,9:59:59.000 Who takes care of the chores and gets rid[br]of the whatever and then we all want 9:59:59.000,9:59:59.000 a Jarvis right? So if you watch these[br]movies again, after watching this talk, 9:59:59.000,9:59:59.000 you'll have a very different perspective[br]on what we need to do to get here. 9:59:59.000,9:59:59.000 It's not going to happen just because[br]we're going to make more and more 9:59:59.000,9:59:59.000 Hollywood movies like this.