Google CEO Sundar Pichai’s I/O 2017 keynote

[Music] [Applause] [Music] [Applause] good morning welcome to Google i/o I [Applause] love you guys do can't believe it's one year already a beautiful day we've been joined by over 7,000 people and we are live-streaming this as always to over 400 events in 85 countries last year was a tenth year since Google i/o started and so we moved it closer to home at Shoreline back where it all began seems to have gone well I checked the Wikipedia entry from last year there were some mentions of sunburn so we have plenty of sunscreen all around it's on us use it liberally it's been a very busy year since last year no different from my 13 years at Google that's because we've been focused evermore on our core mission of organizing the world's information and we are doing it for everyone and we approach it by applying deep computer science and technical insights to solve problems at scale that approach is served us very very well this is what has allowed us to scale up seven of our most important products and platforms to over a billion monthly active users each and it's not the not just the scale at which these products are working users engage with them very heavily YouTube's not just as over a billion users but every single day uses watch over 1 billion hours of videos on YouTube Google Maps every single day users navigate over 1 billion kilometers with Google Maps so the scale is inspiring to see and there are other products approaching the scale we launched Google Drive five years ago and today it is over 800 million monthly active users every single week that are over three billion objects uploaded to Google Drive two years ago at Google i/o we launched photos as a way to organize users photos using machine learning and today we are over 500 million active users and every single day users upload 1.2 billion photos to Google so the scale of these products are amazing but they are all still working up their way towards Android which I'm excited as of this week we crossed over 2 billion active devices of Android as you can see that the robot is pretty happy - behind me so it's a privilege to serve users of this scale and this is all because of the growth of mobile and smartphones but computing is evolving again we spoke last year about this important shift in computing from a mobile first to AI first approach mobile made us reimagine every product we were working on we had to take into account that the user interaction model it's fundamentally changed with multi-touch location identity payments and so on similarly in AI first world we are rethinking all our products and applying machine learning and AI to solve user problems and we are doing this across every one of our products so today if you use Google search we rank differently using machine learning or if you're using Google Maps Street View automatically recognizes restaurant signs street signs using machine learning duo with video calling uses machine learning for low bandwidth situations and smart reply in a low last year had great reception and so today we are excited that we are rolling out smart reply to over 1 billion users of Gmail it works really well here's a sample email if you get a email like this the machine learning systems learn to be conversational and it can reply and find what Saturday or so it's really nice to see just like with every platform shift how users interact with computing changes mobile brought multi-touch we evolved beyond keyboard and mouse similarly we now have voice and vision as new to new important modalities for computing humans are interacting with computing in more natural and immersive ways let's start with voice we've been using voice as an input across many of our products that's because computers are getting much better at understanding speech we have had significant breakthroughs but the pace and even since last year has been pretty amazing to see our word error rate continuously improve even in very noisy environments this is why if you speak to Google on your phone or Google home we can pick up your voice accurately even in noisy environments when we were shipping Google home we had originally planned to include 8 microphones so that we could accurately locate the source of read where the user was speaking from but thanks to deep learning use a technique called neural beamforming we were able to ship it with just two microphones and achieve the same quality deep learning is what allowed us about two weeks ago to announce support for multiple users in Google home so that we can recognize up to six people in your house and personalize the experience for each and everyone so voice is becoming an important modality in our products the same thing is happening with vision similar to speech we are seeing great improvements in computer vision so when we look at a picture like this we are able to understand the attributes behind the picture we realize it's your boy in a birthday party there was cake and family in wall and your boy was happy so we can understand all that better now and our computer vision systems now for the task of image recognition are even better than humans so it's pounding progress and be using it across a product so if you use the Google pixel it has the best-in-class camera and we do do a lot of work with computer vision you can take a low-light picture like this which is noisy and we automatically make it much clearer for you or coming or coming very soon if you take a picture of your daughter at a baseball game and there is something obstructing it we can do the hard work remove the obstruction and have the picture of what matters you in front of you we are clearly at an inflection point with vision and so today we are announcing a new initiative called Google is Google AM is a set of vision based computing capabilities that can understand what you're looking at and help you take action based on that information we will ship it first in Google assistant and photos and it will come to other products so how does it work so for example if you run into something and you want to know what it is say a flower you can invoke google lens from your assistant point your phone at it and we can tell you what floor it is it's great for someone like me with allergies or if you've ever been at a friend's place and you've crawled under a desk just to get the username and password from a Wi-Fi router you can point your phone at and we can automatically do the hard work for you or if you're walking in a street downtown and you see a set of restaurants across you you can point your phone because we know where you are and we have our knowledge graph and we know what you're looking at we can give you the right information in a meaningful way as you can see we are beginning to understand images and videos all of Google was built because we started understanding text and webpages so the fact that computers can understand images and videos has profound implications for our core mission when we started working on search we wanted to do it at scale this is why we rethought our computational architecture we designed our data centers from the ground up and we put a lot of effort in them now that we are evolving for this machine learning and AI world we are rethinking our computational architecture again we are building what we think of as AI first data centers this is why last year we launched the tensor processing units they are custom hardware for machine learning they were about 15 to 30 times faster on 30 to 80 times more power efficient than CPUs and GPUs at that time we use DP use across all our products every time you do a search every time you speak to Google in fact DP user what powered alphago in its historic match against laser at all as you know machine learning has two components training that is how we build a neural @v we you know training is very computationally intensive and inferences what we do at real time so that when you show it a picture we recognize whether it's a dog or a cat and so on last year's TPU software optimized for inference training is computationally very intensive to give you a sense each one of our machine translation models takes a training of over three billion words for a week on about 100 GPUs so we've been working hard and I'm really excited to announce our next generation of TP use cloud TP use which are optimized for both training and inference what you see behind me is one cloud TPU board it has four chips in it and each board is capable of 180 trillion floating point operations per second and you know we have designed it for our data center so you can easily stack them you can put 64 of these into one big supercomputer we call these TPU parts and each part is capable of 11.5 peda flops it is the important advance in technical infrastructure for the AI era the reason we enabled named it cloud TPU is because we are bringing it through the Google cloud platform so cloud GPUs are coming to Google compute engine as of today we want Google cloud to be the best cloud for machine learning and so we want to provide our customers with a wide range of hardware beats CPUs GPUs including the great GPS Nvidia announced last week and now cloud TPS so this lays the foundation for significant progress

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Keywords: google, google i/o, sundar pichai, keynote, conference, google i/o 2017

Id: vWLcyFtni6U

Length: 12min 4sec (724 seconds)

Published: Wed May 17 2017