October 4th – Google Event

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[music]. good morning everyone thank you for . joining us as we were preparing for this . event we were all devastated by the news . coming out of las vegas as i'm sure all . of you were and that's coming off a . challenging past few weeks with . hurricanes harvey hermann maria and . other events around the world it's been . hard to see the suffering but i've been . moved and inspired by everyday heroism . people opening up their homes and the . first responders literally risking their . lives to save other people our hearts . and prayers are with the victims and . families impacted by these terrible . events we are working closely with many . relief agencies in affected areas and we . are committed to doing our part it's a . true privilege to be a dear sub jazz. center it's a great american institution . for jazz performance and education and . it's really good to see familiar faces . in the audience as always i want to give . a shout out to people joining us on the . live stream globally from around the . world since last year and since google . io . we've been working hard continuing our . shift from a mobile first to an ai first . world we are rethinking all our core . products and working hard to solve user . problems by applying machine learning . and ai let me give you an example . recently i visited lagos in nigeria it's . a city of 21 million people it's an . incredibly dynamic vibrant and ever . growing city many people are coming . online for the first time so . it's very exciting unless you happen to . be in the google maps team and you have . to map the city and it is so it is . changing so fast and normally we map a . place by using street view and doing a . lot of stuff automatically but it's . difficult to do that in a place like . legos because the city is changing you . can't always see the signage clearly and . there are variable address conventions . things are in sequential so for example . take that house there if you squint hard. you can see the street number there it . is number three to the left of the gate . that was relatively easy aren't a harder . problem now that house you know that's . what we see from street view i think as . humans it's probably pretty hard maybe . one or two of you can spot it out but . our computer vision systems thanks to . machine learning can pick it out . identify the street number and start . mapping mapping the house so we approach . legos completely differently it deployed . machine learning from the ground up and . just in five months the team was able to . map five thousand kilometers of new . roads fifty thousand new addresses and . hundred thousand businesses and it's . something which makes a real difference . for millions of users there as school . maps is popular and we think this . approach is broadly applicable let's . come closer to home in a parking in san . francisco i don't even try it anymore . but for those of you who try it we again . use machine learning we understand . location data we try to understand . patterns our car circling around and the . color shows the density of parking and . we can analyze it throughout the day and . predict parking difficulty and in google. maps give you options a simple example . but it's the kind of everyday use case . for which we are using machine learning . to make a difference the best example i . can think of what we've taught before is . google translation i literally remember . many years ago . adding translation in chrome and making. it automatic so that if you land in a. page different from your language we do . that for you fast forward to today with . the power of machine learning on our . neural machine translation we serve over . 2 billion translations in many many . languages every single day to me shows . the power of staying at a problem . constantly using computer science to . make it better and seeing users respond. to it at scale this is why we are. excited about the shift from a mobile . first to a ai first well it is not just . about applying machine learning in inner . products but it's radically rethinking . how computing should work at a higher. level in an ai first world i believe . computers should adapt to how people. live their lives rather than people . having to adapt to computers and so we . think about four core attributes as part . of this experience first people should . be able to interact with computing in a . natural and seamless way mobile took us . a step in this direction with . multi-touch but increasingly it needs to . be conversational sensory we need to be . able to use her voice gestures and . vision to make the experience much more . seamless second it is going to be . ambient computing is going to evolve . beyond the phone be there in many . screens around you when you need it . working for you third we think it needs . to be thoughtfully contextual mobile . gave us limited context you know with . identity your location we were able to . improve the experience significantly an . ai first world we can have a lot more . context and apply it thoughtfully for . example if you're into fitness and you. land in a new city we can suggest. running routes . maybe gyms nearby and healthy eating. options and in my case being a . vegetarian and having a weakness for . deserts maybe suggest the right . restaurants for me finally and probably . the most important of it all you know . computing needs to learn and adapt . constantly over time. it just doesn't work that way today in . mobile . you know developers write software and . constantly ship updates but you know let . me give a small example i use google . calendar all the time on sundays i try . to get a weekly view of how my week . looks like but once the work week starts. say on a monday or a tuesday i'm trying . to get a view into what the next few . hours looks like i have to constantly. toggle toggle back and forth . google calendar should automatically. understand my context and show me the. right view it's a very simple example . but software needs to fundamentally . change how it works it needs to learn . and adapt and that applies the important . things like security and privacy as well . today a lot of us deal with security and . privacy by putting the onus back on . users we give them many settings and . toggles to improve those but you know ai . first well we can learn and adapt and do . it thoughtfully for our users for . example if it's a notification for your . doctor's appointment we need to treat it . sensitively and differently than just . telling you when you need to start . driving to work so we're really excited . by the shift and that's why we are here . today we've been working on software and . hardware together because that's the . best way to drive the shifts in . computing forward but we think we are at . a unique moment in time where we can . bring the combination of ai and software. and hardware to bring a different . perspective to solving problems for . users and we're very confident about our . approach here because we are at the . forefront of driving the shifts with ai . three months ago at google i/o our . google ai teams announced a new approach . called auto mo auto ml is just our . machines automatically generating. machine learning models today these are . handcrafted by machine learning. scientists and literally only a few . thousands of scientists around the world . can do this design the number of layers . they can connect the neurons . creately it's very hard to do we want to . democratize this we want to bring this. to more people we want to enable . hundreds of thousands of developers to . be able to do it so we've been working . on this technology called auto mo and . just in the past month for a standard . tasks like image classification . understanding images our auto ml models . are now not only more accurate than the . best human generated models but they are . more resource efficient so it's pretty . amazing to see we are now taking it a . step further let me talk about another . use case object direction when we say . object direction just a fancy name for . computers trying to delineate and . understand images being able to draw . bounding boxes and distinguish between. all the vehicles their scooters mopeds . motorcycles and even pick out the bike . in front it has a lot of practical use . cases the street view example for legos . works based on object deduction google . lense which you'll hear about later as . well as our photography in pixel use. this object direction this is really . hard to do the best human generated . models we have only have a 39 percent . accuracy but our auto ml models as of . the past couple of weeks have have . reached around forty three percent . accuracy they're constantly getting . better so the rate at which we are . seeing progress with ai is amazing which . is why we are really excited about . combining it with our software and . hardware to bring it together for our . users let me give you a concrete example . i was recently very inspired by the . street from a journalist who was uh . turning the little league world series . as you can see there are two little . leaguers here one from dominican. republic and the other from south dakota . and they are talking to each other using . google translate it's a great example . but i looked at it and i feel like we . could do a whole lot better computing . needs to evolve where this happens in a . more natural seamless an
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