Google Keynote (Google I/O’19)


[MUSIC PLAYING] [APPLAUSE] SUNDAR PICHAI: Good morning. Good morning. Wonderful to be back here at
Shoreline with all of you. It’s been a really busy few
months for us at Google. We just wrapped up Cloud Next in
San Francisco with over 30,000 attendees, as well as
YouTube Brandcast last week in New York. Of course, today’s about you
all, our developer community. And thank you all for
joining us in person, and to the millions around the
world watching on livestream. I would love to say welcome
in all our languages our viewers speak,
but we are going to keep the keynote
under two hours, especially since Barcelona kicks
off against Liverpool at noon. [CHEERING] That should be an amazing game. Every year at I/O,
we learn and try to make things a
little bit better. That’s why we have
lots of sunscreen– hope the sun comes out– plenty of water and shade. But this year, we
want to make it easier for you to get around. So we are using AR to help. To get started, open your I/O
app and choose Explore I/O. And then you can just point
your phone where you want to go. We really hope this
helps you get around and answers the number
one question people have– where the sessions are. Actually, it’s not that. They want to know
where the food is. And we have plenty of it around. We also have a couple
of Easter eggs, and we hope you
enjoy them as well. This is a pretty
compelling use case. And we actually want to
generalize this approach so that you can
explore and navigate the whole world that way. There’s a lot of
hard work ahead. And it’s a hard computer
science problem. But it’s the type of
challenge we love. Tackling these
kinds of problems is what has kept us going
for the past 21 years. And it all begins
with our mission to organize the
world’s information and make it universally
accessible and useful. And today, our mission
feels as relevant as ever. But the way we approach
it is constantly evolving. We are moving from
a company that helps your find answers
to a company that helps you get things done. This morning,
we’ll introduce you to many products built on
a foundation of user trust and privacy. And I’ll talk more
about that later. We want our products
to work harder for you, in the context of your
job, your home, and your life. And they all share
a single goal– to be helpful, so we
can be there for you in moments big and small
over the course of your day– for example, helping you
write your emails faster with automatic solutions
from Smart Reply, and giving you the
chance to take them back if you didn’t get it
right the first time, helping you find
the fastest route home at the end of a long
day, and when you get there, removing distractions so
that you can spend time with the people most
important to you, and when you capture
those perfect moments, backing them up automatically
so you never lose them. Simply put, our goal is to
build a more helpful Google for everyone. And when we say
“helpful,” we mean giving you the tools to increase
your knowledge, success, health, and happiness. We feel so privileged to
be developing products for billions of users. And with that scale comes a
deep sense of responsibility to create things that
improve people’s lives. By focusing on these
fundamental attributes, we can empower individuals and
benefit society as a whole. Of course, building a
more helpful Google for us always starts with search and
the billions of questions users trust Google with everyday. But there is so much more
we can do to help our users. Last year, we launched a
new feature in Google News called Full Coverage. And we have gotten great
feedback on it from our users. We’ll be bringing
Full Coverage directly to search to better
organize results for news-related topics. Let’s take an example. If you search for
“black hole,” we’ll surface the relevant top news. It was in the news recently. We use machine
learning to identify different types of
stories and give you a complete picture of how
a story is being reported from a wide variety of sources. You can click into
Full Coverage. It serves as a
breadth of content, but allows you to drill down
into what interests you. You can check out different
aspects of the story, like how the black
hole got its name. You can even now see
a timeline of events. And we’ll be bringing this
to search later this year. Podcasts are another important
source of information. And we’ll be bringing them
directly to search as well. By indexing podcasts, we can
surface relevant episodes based on their content,
not just the title. And you can tap to listen
right in search results, or you can save an episode
for listening later on your commute or
your Google Home. These are all examples of how
we are making search even more helpful for our users,
surfacing the right information in the right context. And sometimes, what’s most
helpful in understanding the world is being able
to see it visually. To show you how we are bringing
you visual information directly in search, here’s Aparna. [MUSIC PLAYING] [APPLAUSE] APARNA CHENNAPRAGADA:
Whether you’re learning about the
solar system or trying to choose a color
scheme for your home, seeing is often understanding. With computer vision
and augmented reality, that camera in our
hands is turning into a powerful visual
tool to help you understand the world around you. So today, we are excited
to bring the camera to Google search, adding a
new dimension to your search results– well, actually three
dimensions, to be precise. So let’s take a look. Say you’re a student
studying human anatomy. Now, when you search for
something like muscle flexion, you can view a 3D model
built by Visible Body right from the search results. Pretty cool. Not only that, you can also
place it in your own space. [APPLAUSE] Look, it’s one thing to read
about flexion or extension, but seeing it in action
right in front of you while you’re studying
the concept, very handy. OK, let’s take another example. Say, instead of
studying, you’re shopping for a new pair of shoes. That happens. With New Balance,
you can look at shoes up close from different angles,
again, directly from search. That way, you get a much
better sense for things like, what does the grip
look like on the sole, or how they match with
the rest of your clothes. [APPLAUSE] OK, this last example
is a really fun one. So you may have all seen a
great white shark in the movies. “Jaws,” anyone? But what does it actually
look like up close? Let’s find out, shall we? OK. I have Arjuna here with
me to help with the demo. So let’s go ahead and search for
“great white shark” on Google. As you scroll through,
you get information on the knowledge panel facts,
but also see the shark in 3D directly from the
knowledge panel. Why don’t we go
one step further? Why don’t we invite
the shark to the stage? Whoa! [APPLAUSE] There it is. It’s one thing to read a
fact like “a great white can be anywhere between 17
feet to 21 feet long,” but to see it in
front of you at scale, filling up the Shoreline stage
like a rock star, that is truly understanding its scale. OK, let’s take a closer look. It’s an AR shark. It won’t bite. Ooh. Look at those layers of teeth. You know, I don’t
know about you all, but I’d much rather see
these teeth up close in AR than in real life. Thank you, Arjuna. [APPLAUSE] Really excited about bringing
the camera and AR capabilities to Google search. Now, sometimes,
though, the things that you’re
interested in, they’re difficult to describe
in a search box. So that’s why we
created Google Lens, to help you search and
do more with what you see by simply pointing your camera. The built lens has a
capability across products. So you can access it directly
from the Google Assistant. But we’ve also built it into
Google Photos and the Camera app on many Android devices. People have already used Lens
more than a billion times so far. And they’ve used
it to ask questions about what they see, like
what kind of flower that is, or where to get
a lamp like that, or just who the artist is. One way we’ve been
thinking about it is, with Lens, we’re indexing
the physical world, billions of places and products and so
on, much like search indexes the billions of
pages on the web. OK, today, let me
show you some new ways that we’re making Lens
more helpful to you. Say you’re at a
restaurant trying to figure out what to order. Instead of going from the menu
to different apps on the phone and back to the
menu and so on, you can simply point your camera. Lens automatically
highlights the popular dishes at this restaurant
right on the menu. [APPLAUSE] And of course, if you
want to know more, you can tap on any
dish on the menu, and you can see what
it looks like, again, at the restaurant– [APPLAUSE] –and, of course, check
out what other people are saying about it on Google Maps. By the way, when
you’re done eating, Lens can help pay for your meal. Not so fast. It’s not picking up your tab. But it can calculate the tip
and even split the total– again, just by pointing
your camera at the receipt. And voila. [APPLAUSE] So you saw how we connected
the menu with information from Google Maps. But we’re starting to
think of other ways that we can connect
helpful digital information with the things in
the physical world. So I’m going to give
you just one example. So you’re flipping through
a “Bon Appetit” magazine and you see a recipe you like. Soon, you can point your
camera at the recipe and see the page come
alive, showing you how to make the dish. We’re starting to work with
more partners, like museums, magazine publishers,
and retailers, to bring unique visual
experiences like this. There’s one final area where
we think that the camera can be particularly helpful to people. Around the world, there are
more than 800 million adults who are struggling to read the
words that they come across in their daily lives– bus schedules, bank
forms, et cetera. And many of them are coming
online for the first time with a smartphone. So to help with that, we’ve
integrated a new camera capability into Google Go. This is our search app
for entry level devices. Take this sign in
English next to an ATM. Now, for someone who does
not understand the language and cannot read the words, this
is important information that they’re not getting access to. And we think that the
camera can help here. So let me show you how. So directly from the
Google search bar, you can use Lens,
open it, point it at the sign to hear the
text read out aloud to you. ASSISTANT: Information
for card holders– all customers using old
proprietary magnetic stripe cards should be advised. APARNA CHENNAPRAGADA:
What is nice here is that it is highlighting
the words as they’re spoken. That way, even if you can’t
read the language well, you can follow along, and you
understand the full context of what you see. You can also translate it into
your own language, like this. [APPLAUSE] Notice that the translated
text is overlaid right on top of the original sign. It almost feels
like the sign was written in your own
language to start with. And again, you can
hit Listen and hear the words read out loud, this
time in your own language. ASSISTANT: [SPEAKING SPANISH] [APPLAUSE] APARNA CHENNAPRAGADA:
What you’re seeing here is text-to-speech, computer
vision, the power of translate, and 20 years of language
understanding from search all coming together. Now, our teams in
India have been working with some early testers
and getting a lot of feedback to make the product better. And I want to now show
you how one of them is using it in her daily life. Take a look. [MUSIC PLAYING] [APPLAUSE] APARNA CHENNAPRAGADA:
Thank you, Urmila, for testing it and giving us
a lot of feedback for the team to make the product better. The power to read is the
power to buy a train ticket, to shop in a store,
to follow the news. It’s the power to
get things done. So we want to make this feature
accessible to as many people as possible. So it already works in more
than a dozen languages. And the teams worked
incredibly hard to compress all of this tech to
just over 100 kilobytes. [APPLAUSE] APARNA CHENNAPRAGADA:
That way, it can work on phones that
cost as little as $35. So we’re super excited about
this and all the other features across search and Lens to
help you throughout the day. You’ll start to see
these updates roll out later this month. Thank you. [APPLAUSE] [MUSIC PLAYING] SUNDAR PICHAI: Thanks Aparna. Helpfulness is also about
saving time and making your day a little bit easier. That’s why, last
year, at I/O, we gave you a first look at
our Duplex technology. Duplex enables Google Assistant
to make restaurant reservations on your behalf by
actually placing a call. It’s now available in
44 states across the US. And we’ve gotten great feedback
not only from our users, but from businesses as well. For us Duplex is the
approach by which we train AI on simple
but familiar tasks to accomplish them
and save you time. Duplex was launched with
restaurant reservations on the phone. But now, we are
moving beyond voice and extending Duplex
to tasks on the web. We again want to focus on
narrow use cases to start. So we are looking at rental
car bookings as well as movie ticketing. Today, when you make a
new reservation online, you have to navigate a
number of pages and steps, filling out
information and making selections along the way. I’m sure you’re all familiar
with this experience. It’s time consuming. And if users leave
during the workflow, businesses lose out as well. We want to make this experience
better for both users and businesses. So let me show you how that
system can do it better. Say you get a calendar reminder
about an upcoming trip. And you want to
book a rental car. You can just ask Google,
book a National car rental for my next trip. The Assistant opens
the National website and automatically starts
filling out your information on your behalf– [APPLAUSE] –including the
dates of the trip. You can confirm the
details with just a tap. And then the Assistant
continues to navigate the site. It even selects
which car you like. It’s acting on your behalf
and helping you save time, but you’re always in
control of the flow. Let’s go ahead and
add a car seat. And once all the
details are in, you can check everything
one last time and just tap to finalize
the reservation. You’ll immediately get
a booking confirmation. It’s amazing to see
the Assistant complete a task online on your behalf
in a personalized way. It understands the
dates of your trip and your car preferences
based on trip confirmations and Gmail. And I also want to point
out that this was not a custom integration. This required no action on part
of the business to implement. What you just saw
is an early preview of what we are calling
Duplex on the Web. We’re going to be
thoughtful and get feedback from both users and businesses
to improve the experience. And we’ll have more details
to share later this year. [APPLAUSE] The Google Assistant
helps people around the world with
all kinds of tasks, whether they are at
home or on the go. But we want to build an
even more helpful assistant. In order to process
speech today, we rely on complex algorithms
that include multiple machine learning models. One model maps incoming sound
bites into phonetic units. Another one takes and
assembles these phonetic units into words. And then a third model
predicts the likelihood of these words in a sequence. They are so complex
that they require 100 gigabytes of storage
and a network connection. Bringing these models
to your phone– think of it as putting
the power of a Google data center in your pocket– is an incredibly challenging
computer science problem. I’m excited to share
we have reached a significant milestone. Further advances
in deep learning have allowed us to combine and
shrink the 100-gigabyte models down to half a gigabyte,
small enough to bring it onto mobile devices. This eliminates network latency
and makes the Assistant so much faster– so fast that tapping to use
your phone would seem slow. I think this is
going to transform the future of the Assistant. And I’m thrilled to
bring Scott to tell you more about our next
generation Assistant. [MUSIC PLAYING] [APPLAUSE] SCOTT HUFFMAN: Thanks, Sundar. Well, what if we could bring
the AI that powers the Assistant right onto your phone? What if the Assistant was so
fast at processing your voice that tapping to operate your
phone would almost seem slow? It opens up many new use cases. And we want to show
you how fast it is. Now, internally, we’ve
been calling this the next generation Assistant. Running on device, it can
process and understand requests in real time,
and deliver the answers up to 10 times faster. Now, Maggie’s here. And she’s going to
help us test it out, starting with some
back-to-back commands to demonstrate its speed. Now this demo is
hot off the press. So please send your
positive energy over in Maggie’s direction. [APPLAUSE] MAGGIE: Hey, Google,
open Calendar. Open Calculator. Open Photos. Set a timer for 10 minutes. What’s the weather today? What about tomorrow? Show me John Legend on Twitter. Get a Lyft ride to my hotel. Turn the flashlight on. Turn it off. Take a selfie. [APPLAUSE] SCOTT HUFFMAN: All right. Now as you can see– [APPLAUSE] Yeah. That was awesome. Maggie was able to open and
navigate apps instantly. Now you might have
also noticed that, with continued
conversation, she was able to make several
requests in a row without having to say
“hey Google” each time. Now, beyond an effortless
way to operate your phone, you can start to imagine how the
Assistant fused into the device could orchestrate
tasks across apps. Let’s look at another
demo where Maggie’s chatting with a friend. He’s going to ask her
about a recent trip. Notice how easy it is for her to
respond with her voice and even share a photo. MAGGIE: Reply. Had a great time with my
family, and it was so beautiful. Show me my photos
from Yellowstone. The ones with animals. Send it to Justin. SCOTT HUFFMAN: All right. [APPLAUSE] Yeah. Now another example is when
a friend asks you a question and you need to look up
the information to respond. Justin wanted to know when
Maggie’s flight arrives. MAGGIE: When’s my flight? When’s my flight? Reply. I should get in around 1:00 PM. SCOTT HUFFMAN: All right. So notice how it
helped Maggie multitask more easily across
different apps, saving her a lot
of back-and-forth. Now you can even imagine this
next generation assistant handling more complex
speech scenarios, like composing and
sending an email. MAGGIE: Hey, Google,
send an email to Jessica. Hi, Jessica. I just got back from Yellowstone
and completely fell in love with it. Set subject to
“Yellowstone Adventures.” Let me know if next
weekend works for dinner so I can tell you all about it. Send it. SCOTT HUFFMAN: Whoa. [APPLAUSE] All right. Now, as you can see, this
required the Assistant to understand when
Maggie was dictating part of the message
versus when she was asking it to complete an action. Thanks, Maggie. MAGGIE: Thanks, Scott. [APPLAUSE] SCOTT HUFFMAN: By moving
these powerful AI models right onto your phone, we’re
envisioning a paradigm shift. This next generation assistant
will let you instantly operate your phone with your
voice, multitask across apps, and complete complex actions,
all with nearly zero latency. And actions like turning
on the flashlight, opening Gmail, or
checking your calendar will even work offline. Now, it’s a very hard
problem we’ve been solving. And I’m really excited to share,
the realization of this vision is not far off. In fact, this next
generation assistant is coming to the new Pixel
phones later this year. [APPLAUSE] All right. Now our mission is to make
the Assistant the best way to get things done. You just saw how we’re
making it much faster. But it also has to be personal
enough to really help you. Now, personalized help is
especially important in areas where people’s
preferences completely differ, like choosing
what to listen to, what to do on the weekend,
or even what to eat. So let’s look at
a recipe example. Hey, Google, what should
I cook for dinner? ASSISTANT: Here are some
recipe picks for you. SCOTT HUFFMAN: Now
as you can see, the Assistant picked
recipes tailored to me. For example, it suggested
a bourbon chicken recipe, because it’s helped me with
barbecue recipes in the past. Now what I really love is
that different people get completely different results. We call this feature
Picks for You. And it will be launching
on smart displays later this summer, starting with
recipes, podcasts, and events. Now, beyond your preferences,
becoming more personal means the Assistant will better
understand the people, places, and events that are
important to you. Now, one important
person in my life is my mom, who I’m going
to visit right after I/O. So let’s say I asked
my Assistant, how’s the traffic to Mom’s house? Now, we all understand what
I mean by Mom’s house, right? Well, if I’m in
Toledo, Mom’s House might have meant this place,
a nonprofit childcare center. In other cities, Mom’s House
can be a restaurant or a grocery store. In fact, there’s lots of
things in the world called Mom’s House. Now, in linguistics, the
process of figuring out which thing a phrase refers to
is called reference resolution. And it’s fundamental to
understanding human language. At Google, we
approached this problem using our knowledge
graph of things in the world and
their relationships. It’s what allows us to
understand something like the Starbucks near
the Golden Gate Bridge. Today, we’re expanding
the Assistant’s ability to understand you better by
applying those same techniques to the things in your world. We call it Personal References. So if I shared my mom’s contact
info with the Assistant, I can ask, hey, Google, what’s
the weather like at Mom’s house this weekend? ASSISTANT: Friday and
Saturday in Carmichael will be partly cloudy. SCOTT HUFFMAN: How long
will it take to get there? ASSISTANT: With
light traffic, it will take you two
hours and 14 minutes to get to 123 Main
Street by car. SCOTT HUFFMAN: Remind
me to order flowers a week before Mom’s birthday. ASSISTANT: All right. I’ll remind you on July 3. SCOTT HUFFMAN: And
it goes beyond Mom. If you’ve shared important
people, places, and events with the Assistant, you’ll be
able to ask for things more naturally. Like, show me photos of my son,
or directions to the restaurant reservation, or
reminding me to pick up chocolates on my anniversary. And rest assured, you’re
always in control. You can edit or delete
this information at any time in the updated
You tab in Assistant settings. Now, one place
where the Assistant can be especially
helpful is in the car, offering a safer hands-free
way to get everything you need while you’re on the road. Now, we’ve been focused
on the main things that we all want
when we’re driving– to get where we’re going safely,
to catch up with friends, and listen to something
interesting along the way. Last year, we brought the
Assistant to Android Auto. And earlier this year,
we added it to Navigation in Google Maps. I’m happy to share,
the Assistant is also coming to Waze
in the next few weeks. Now, I’d like to
show you the future of how we’re improving your
mobile driving experience even more. Introducing the Assistant’s
new driving mode. Just put your phone
in the car and say, hey, Google, let’s drive. Driving mode has a
thoughtfully designed dashboard that brings your most relevant
activities front and center while you’re driving,
and includes suggestions personalized for you. For example, if you have
a dinner reservation on your calendar, you’ll
see a convenient shortcut to navigate to the restaurant. Or if you started a podcast
at home in the morning, once you get in
your car, it will display a shortcut
to resume the episode right where you left off. Now it also highlights
top contacts, making it easy to call
them or message them, and recommendations for
other things to listen to. Now, once you’re navigating,
phone calls and music appear in a low
profile way, so you can get things done without
leaving your navigation screen. Hey, Google, play some jazz. ASSISTANT: Sure. Check out this jazz music
station on YouTube Music. [JAZZ MUSIC PLAYING] SCOTT HUFFMAN: Now,
everything is voice-enabled. So if a call comes
in, the Assistant will tell you who’s
calling and ask if you want to
answer without having to take your eyes off the road. [PHONE RINGING] ASSISTANT: Call from Mom. Do you want to pick it up? SCOTT HUFFMAN: No thanks. But thanks for your
help with the demo, Mom. All right, so best of
all, with the Assistant already on your phone, there’s
no need to download an app. Just start driving. Driving mode will be available
this summer on any Android phone with the Assistant. [APPLAUSE] Now, today, the
Google Assistant is available on over 1 billion
devices in over 30 languages across 80 countries. And with Duplex on the Web,
the next generation Assistant, personalized help, and
assistance in the car, we’re continuing to
build on our mission to be the fastest, most
personal way to help you get things done. Now, before I go, I want
to share a little something that a lot of you
have been asking for. Check this out. [ALARM SOUNDING] Stop. [ALARM STOPS] [APPLAUSE] Now you can stop your timers
and alarms just by saying stop. No “hey, Google” needed. And it’s rolling out on smart
displays and Google Homes in English-speaking
locales starting today. Thanks very much. [APPLAUSE] [MUSIC PLAYING] SPEAKER 1: Hey, Google,
open the pod bay doors. SPEAKER 2: Hey, Google. SPEAKER 3: Hey, Google. SPEAKER 4: Hey, Google. Turn on the lights. SPEAKER 5: Turn on two mode. SPEAKER 6: I love that. SPEAKER 7: Hey, Google. [HORN HONKING] SPEAKER 8: Call Mom. [SCREAMING] ASSISTANT: I found a few
restaurants near you. SPEAKER 9: Order my
usual from Starbucks. ASSISTANT: Ordering you a grande
vanilla latte from Starbucks. SPEAKER 9: I love you. I love you! SPEAKER 10: OK, Google. ASSISTANT: This is a cat. [MEOWING] SPEAKER 11: The forecast
is 72 and sunny. SPEAKER 12: Take a selfie. [ANIMAL BARKS] SPEAKER 13: Hey, Google. SPEAKER 14: What’s on
my calendar for today? SPEAKER 15: Make me laugh. SPEAKER 16: How do
I slice a mango? SPEAKER 17: Turn on
the Christmas spirit. MACAULAY CULKIN:
Begin Operation Kevin. ASSISTANT: Operation
Kevin underway. SPEAKER 18: Show me how to make
an octopus costume on YouTube. [APPLAUSE] SUNDAR PICHAI: Thanks, Scott. It’s great to see the
momentum of Google Assistant and how it’s able to help
users get things done. So far, we’ve talked about
building a more helpful Google. It’s equally important to us
that we do this for everyone. “For everyone” is a core
philosophy for us at Google. That’s why, from
the earliest days, search was the same,
whether you you were a professor at Stanford or
a student in rural Indonesia. It’s why we build affordable
laptops for classrooms everywhere. And it’s why we care about
the experience on low cost phones in countries
where users are just starting to come online
with the same passion as we do with premium phones. And it goes beyond our
products and services. It’s why we offer free
training and tools through Grow with
Google, helping people grow their skills,
find jobs, and build their businesses. And it’s how we
develop our technology, ensuring the responsible
development of AI, privacy and security that
works for everyone, and products that are
accessible at their core. Let’s start with
building AI for everyone. Bias has been a
concern in science long before machine
learning came along. But the stakes are
clearly higher with AI. It’s not enough to
know if a model works. We need to know how it works. We want to ensure that our AI
models don’t reinforce bias that exists in the real world. It’s a hard problem,
which is why we are doing fundamental
computer science research to improve the
transparency of machine learning models and reduce bias. Let me show you what I mean. When computer scientists
deploy machine learning models, it can sometimes be
difficult to understand why they make a certain prediction. That’s because most
machine learning models appear to operate
on low level features– edges and lines in a picture,
color of a single pixel. That’s very different
than the higher level concepts more
familiar to humans, like stripes on a zebra. To tackle this problem,
Google AI researchers are working on a new
methodology called TCAV, or testing with concept
activation vectors. Let me give you an example. If it’s a machine
learning model, trained to detect zebras, you would
want to know which variables were being used to decide if the
image contained a zebra or not. TCAV can help you understand
if the concept of stripes was important to the
model’s prediction. In this particular
case, it makes sense. Stripes are an important
predictor for the model. Now suppose a classifier was
trained on pictures of doctors. If the training data
was mostly males wearing coats and
stethoscopes, then the model could inaccurately
assume that being male was an important
prediction factor. There are other important
examples as well. Now imagine an AI
system that could help with detecting skin cancer. To be effective, it
would need to recognize a wide variety of skin
tones representative of the entire population. There’s a lot more to
do, but we are committed to building AI in a way that’s
fair and works for everyone, including identifying and
addressing bias in our own ML models and sharing
tools and open data sets to help you as well. Another way we
build for everyone is by ensuring that our
products are safe and private, and that people have
clear, meaningful choices around their data. We strongly believe that
privacy and security are for everyone, not just a few. This is why powerful privacy
features and controls have always been built
into Google services. We launched incognito mode
in Chrome over a decade ago. We pioneered Google Takeout,
which gives you easy controls to export your data, from
email, contacts, photos– all of our products– any time you choose to. But we know our work on privacy
and security is never done. And we want to do more to
stay ahead of constantly evolving user expectations. We have been working
on a significant set of enhancements. And I want to talk
you through a few. Today, you can already
find all your privacy and security settings in one
place in your Google account. To make sure your Google account
is always at your fingertips, we are making it easily
accessible from your profile photo. If you’re in search, you
can tap on your photo, and you can quickly access
the most relevant privacy controls for search, in the
case of your data in search. Here, you can view and
manage your recent activity. And you can easily change
your privacy settings. Last week, we announced
auto-delete controls, which you’ll also be able to
access right from the app. Data helps make search
work better for you. And with auto-delete, you can
choose how long you want it to be saved– for example, three or 18 months,
after which any old data will be automatically
and continuously deleted from your account. This is launching today
for web and app activity. We’ll be rolling it
out to location history in the coming weeks. And we’ll continue to
bring features such as this to more controls over time. In addition, one-tap access
to your Google account will be coming to
our major products, including Chrome, search,
Assistant, YouTube, Google News, and Maps. And speaking of Maps, if you
tap on your profile photo, in addition to
finding easy access to your privacy controls, you’ll
find a new feature, incognito mode. Incognito mode has been a
popular feature in Chrome since it launched. And we are bringing
this to Maps. While in Incognito in
Maps, your activity, like the places you
search and navigate to, won’t be linked to your account. We want to make it easy to
enter in and out of Incognito. And maps Will soon
join Chrome and YouTube with support for Incognito. And we’ll be bringing it to
search as well this year. [APPLAUSE] Another way we
ensure your privacy is by working hard to
keep your data secure, from Safe Browsing, which now
protects over 4 billion devices everyday, to using TensorFlow
to significantly reduce phishing attacks
in Gmail, we also encourage users to use
two-step verification, because an additional layer of
protection is always helpful. Today, we are making
two-step verification even more convenient
for everyone by bringing the protection
of security keys directly into your
Android phone. So now, you can confirm a
sign-in with just a tap. And today, it will be
available to over 1 billion compatible devices. [APPLAUSE] We always want to
do more for users, but do it with less
data over time. So we are applying the
same cutting edge AI research that makes
our products better and applying it to
enhance user privacy. Federated Learning– this
is a new approach to machine learning developed by Google– is one example. It allows Google’s AI products
to work better for you and work better for
everyone without collecting raw data from your devices. Instead of sending data to the
cloud, we flipped the model. We ship machine learning
models directly to your device. Each phone computes an
update to the global model. And only those
updates, not the data, is securely uploaded and
aggregated in large batches to improve the global model. And then the
updated global model is sent back to
everyone’s device. Let me explain it with
a concrete example. Take Gboard, Google’s keyboard. Using on-device learning alone,
when new words become popular, Gboard would not be able to
suggest them until you’ve typed them many times. Federated Learning,
however, allows Gboard to learn new
words like BTS or YOLO after thousands of people start
using them without Google ever seeing anything you type. Actually, with BTS, it’s
probably millions of people. This is not just research. In fact, Gboard is already
using Federated Learning to improve next word prediction,
as well as emoji prediction, across tens of
millions of devices. It’s still very
early, but we are excited about the progress
and the potential of Federated Learning across many
more of our products. [APPLAUSE] Privacy and security
are the foundation for all the work we do. And we’ll continue to push
the boundaries of technology to make it even
better for our users. Building for everyone also
means ensuring that everyone can access our products. The World Health Organization
estimates that 15% of the world’s population– over 1 billion people– has a disability. We believe technology can
help us be more inclusive. And AI is providing
us with new tools to dramatically
improve experience for people with disabilities. For example, there are
almost 500 million people in the world who are
deaf or hard of hearing. Think of how many
conversations are challenging, from in-person discussions
and phone calls, to even experiencing
videos online. A few months ago we
launched Live Transcribe, powered by Google’s
Cloud Speech API, to caption conversations
in real time. You can leave your
phone open with the app. And when someone speaks to you,
it transcribes their speech into text. Those who cannot– or prefer
not to– speak can also respond by typing. I was really inspired by
how the product came about. Two of our Google
researchers, Dimitri and Chet, saw an opportunity
to help people and collaborated
to develop the app. Together, with a small team
of engineers and people who volunteered their 20% time,
they built Live Transcribe. And it is now available in
over 70 languages and dialects on Android devices. [APPLAUSE] Today, we are going further
in extending this technology. We are announcing a new
feature called Live Caption. Live Caption makes all
content, no matter its origin, more accessible to everyone. The incredible thing is that
it works completely on device. So there’s no delay. With one click, you
can turn on captions for a web video,
podcast, or even on a moment you capture at home. SPEAKER 19: You like
the blueberries? Blueberries. Delicious? Here comes more. Yum! Show Daddy. Ah. [APPLAUSE] It’s only possible due to our
recent breakthroughs in speech recognition technology. We recently tested Live
Caption with some users. Let’s take a look. [APPLAUSE] You can imagine all the use
cases for the broader community too– for example, the ability to
watch any video if you’re in a meeting or on the subway
without disturbing the people around you. The Android team is going to
talk a little bit later today about what made Live
Caption possible. We’re also exploring
how this technology can caption phone calls. But we want to go one step
further and actually allow more people to
respond and accomplish tasks over their phones. As you’ll see in
this example, Nicole, who’s deaf and
prefers not to speak, can receive a call
from her hairstylist. With Smart Compose
and Smart Reply, she can answer the
call and interact. Let’s take a look. [PHONE RINGING] ASSISTANT: Hi. This is Nicole’s Assistive Chat. She’ll see what you
say, and her responses will be read back to
you, starting now. JAMIE: Hi, Nicole. It’s Jamie. How are you? ASSISTANT: Hey, Jamie. I’m good. And you? JAMIE: Great. Are we still on for your
1:00 PM haircut tomorrow? ASSISTANT: Sorry,
can you do 3:00 PM? JAMIE: Yes, I can do 3:00 PM. We have a lot to catch up on. I want to hear all
about your trip. ASSISTANT: Perfect. Thumbs up. JAMIE: Great. See you tomorrow. Bye. SUNDAR PICHAI: Thumbs up indeed. [APPLAUSE] We call this new
technology Live Relay. While there’s still
more work to do, we are excited to see how it
can help people like Nicole get things done more easily. Just like with Live Caption,
this runs completely on device, and these conversations
remain private to you. We also want to help those
with speech disorders, or people whose speech has been
affected by a stroke or ALS. Researchers from Google
AI are exploring the idea of personalized communication
models that can better understand different
types of speech, as well as how AI can help
even those who cannot speak to communicate. We call this research
Project Euphonia. Let’s take a look. [MUSIC PLAYING] [APPLAUSE] SUNDAR PICHAI: We
are working hard to provide these
voice recognition models through the Google
Assistant in the future. But as you saw in
Dimitri’s case, this will only be possible
with many more speech samples to train our models on. If you or someone
you know has slurred or hard to understand
speech, we’d like to invite you to
submit voice samples to help accelerate this effort. Fundamentally AI research
which enables new products for people with disabilities
is an important way we drive our mission forward. Live Transcribe, Live
Caption, Live Relay, and Project Euphonia will
ultimately result in products that work better for all of us. It’s a perfect
example of what we mean by building a more
helpful Google for everyone. One of the most powerful ways
we deliver help to our users is through our open source
platforms like Android. To tell you more, I’d like to
invite Steph onto the stage. [MUSIC PLAYING] [APPLAUSE] STEPHANIE CUTHBERTSON:
It’s amazing we’re here to talk about
Android’s version 10. [APPLAUSE] And we get to celebrate
a milestone together. Today, there are over 2.5
billion active Android devices. [APPLAUSE] And today, we want to
walk you through what’s coming next in the Android
Q. Innovation, security, and privacy– the central theme
of the Q release, and digital well-being. A lot has changed since 1.0. Smartphones have evolved
from an early vision to this integral
tool in our lives. And they are incredibly helpful. Looking ahead, we
see another big wave of innovation coming to
make them even more helpful. Q shows Android shaping
the leading edge of mobile innovation,
with over 180 device makers around the world. Driven by this
powerful ecosystem, many innovations have been first
on Android, from large screens to the first OLED display. And this year,
display technology will take an even bigger
leap with foldables coming from multiple Android OEMs. These devices open up a
completely new category, which, though early,
just might change the future of mobile computing. Foldables take advantage
of completely new display technology. They literally bend
and fold from phone to tablet-sized screen. And Q maximizes what’s
possible on these screens. For instance, foldables
are great for multitasking. So I can watch some funny
videos my sister sent me while we chat about
what we’re going to do for my mom on Mother’s Day. But the feature I’m most excited
about is screen continuity. So let’s say we finish chatting. It’s time to head out. And I’m standing around,
waiting for my ride. So I start playing a game on
the folded smaller screen. When I sit down and
unfold, the game seamlessly transfers to the larger screen. It is so cool. And I can pick up exactly
where I was playing. Now, multiple OEMs will
launch foldables this year, all running Android. [APPLAUSE] Another exciting
innovation is 5G. 5G networks mean
consistently faster speeds with lower latency. So apps and especially
games can target rich, immersive experiences
to these 5G-connected phones. And Android Q
supports 5G natively. This year, more than 20
carriers will launch networks. And our OEMs have over a
dozen 5G-ready phones all launching this year. And they’ll all be
running Android. Now, in addition to
hardware innovation, we’re also seeing huge firsts
in software, driven by advances in on-device machine learning. Sundar showed Live Caption. Now, I would really like
you to see it in action and then take you
under the hood. Please welcome Tristan. [APPLAUSE] TRISTAN: Like many people,
I watch videos without sound when I’m on the go. With captions, I
can still keep up, even if I’m in a crowded space
or I’m sitting in a meeting. So for me, they’re
super helpful. But for almost 500 million
people who are deaf or hard of hearing, captions
are critical. Today, loads of mobile content
embeds audio, from video to voice messages and
everything in between. Without captions, this content
is nowhere near as accessible. Live Caption in Q takes
audio and instantly turns it into text. Let’s take a look at this
video my friend Heather sent me yesterday. To turn it on, I open
the volume rocker and tap the Live Caption button. HEATHER: Hey, cutie. Do you want to give
your puppy a hug? Oh. Oh, I guess not. Puppy is walking away. TRISTAN: So as you can
see, these captions appear in real time, over
a video that would normally never have captions. You can expand them, contract
them, move them up and down. It’s a lot of fun. But what makes this
feature so incredible is that it’s entirely
done on device. In fact, it doesn’t
need to be connected to the internet at all. If we take a look,
this entire demo I’ve done in airplane mode. [APPLAUSE] Thank you. STEPHANIE CUTHBERTSON:
Thank you, Tristan. OK. So how is this possible? It’s because of a
huge breakthrough in speech recognition that
we made earlier this year. This once required
streaming audio to the cloud to run a two-gigabyte
model for processing. Now we can do that same
processing on device, using a recurrent neural
net, in just 80 megabytes. The live speech model
is running on the phone. And no audio stream
ever leaves it. All this protects user privacy. And this is OS-wide,
which means you get those captions in all your
apps and in web content too. Now, the same on-device
machine learning powers another useful Q
feature, which is Smart Reply. With Smart Reply,
the OS helpfully suggests what you’ll type next. It’ll predict the
text you’ll type– even emoji. And it’s a huge time-saver. What’s really cool
is, this works now for all messaging
apps in Android. Like in Signal, you can
see the OS providing these helpful suggestions. And Smart Reply can now
even predict the actions that you’ll take. So say a friend
sends you an address. And normally, you copy
and paste that into Maps. That’s kind of a hassle. With Smart Reply, you just
tap and it will open for you. Now, all this is
saving you time. On-device machine
learning powers everything from these incredible
breakthroughs like Live Caption to helpful everyday
features like Smart Reply. And it does this
with no user input ever leaving the phone, all of
which protects user privacy. Now there’s one more addition
to Android Q that’s small, but you’ve been asking
us about for a while, and that is dark theme. And we’re launching it in Q. [APPLAUSE] So you can activate it
by using the quick tile or by turning on Battery Saver. And in fact, it will
help you save battery. Your OLED display is one of the
most power-hungry components in your phone. So by lighting up less pixels,
it will save you battery. So that’s innovation. But we feel all
innovation must happen within a frame of
security and privacy. People now carry
phones constantly. And we trust them with a
lot of personal information. You should always be in
control of what you share and who you share it with. And that’s why the
second area we’ll cover in the central
focus of the release is security and privacy. Now, over the years,
Android’s built out a huge set of protections already– file-based encryption,
SSL by default, secure DNS, work profiles. And many of these
were first on Android. Android has the most
widely deployed security and anti-malware service of any
OS, with Google Play Protect. It runs on every device. And it scans over 50
billion apps a day. In fact, in Gartner’s
2019 security report, which was published
this week, Android scored the highest
possible rating in 26 out of 30 categories. It’s ahead on multiple
points, from authentication, to network security, to
malware protection, and more. At the same time, we
wanted to go much further. And that’s why Android Q
includes almost 50 features focused on security
and privacy, all providing more protection,
transparency, and control. So first, in Q, we brought
privacy to the top level in settings. And there, you’ll find a
number of important controls all in one place– activity data, location
history, ad settings. And you decide what’s on or off. Now, location is
another place we’ve created tools for more
transparency and control. Now, location can be really
helpful, especially when you’re lost in a new place. But it’s also some of your
most personal information. And you should, again,
always be in control of who you share it with
and how they can use it. So first, if you’re
wondering which apps can be accessing
your location, we make it easy for you to know. With Q, your device
will give you helpful reminders
whenever an app accesses location when you’re not
actively using that app. So you can review
and decide, do you want to continue sharing or not? Second, Q will give you
more control over how you share location data with apps. For example, say you want
to get pizza delivered. You can choose to
share your location only while the app is in use. And as soon as you close,
you’ll stop sharing location. Finally, what if
you’re wondering, what kind of location
do all my apps have? In Q, we’ve brought
location controls to the forefront in settings. So you can quickly review every
app and change location access with simple controls. Now, there are many, many
more enhancements to security and privacy throughout
the OS, like TLC v3, encryption for low-end devices,
randomizing your Mac address by default, and many more. And you can read about all
of these in our blog post this week. But there’s one more really
big thing for security. Now, your Android device
gets regular security updates already. But you still have to
wait for the release. And you have to
reboot when they come. We want you to
get these faster– even faster. And that’s why, in Q, we’re
making a set of OS modules updatable, directly
over the air. So now, these can be
updated individually as soon as they are available, and
without a reboot of the device. [APPLAUSE] Now, this was a huge
technical challenge. We’re updating these in
the background the same way we’re updating Google Apps. It’s easier for our
partners, with whom we’re working closely. But more importantly,
it’s much better for you. You can learn more about
this at the session “What’s New in Android?” Now, there’s one
more thing that’s changed since the
early days of Android. Now, people carry
smartphones everywhere, because they’re really helpful. But we’re also spending
a lot of time on phones. And people tell
us sometimes they wish they’d spent more
time on other things. We want to help people find
balance and digital well-being. And yes, sometimes, this
means making it easier to put your device away entirely
and focus on the times that really matter. That’s why, last year, we
launched digital well-being tools with dashboards,
app timers, Flip to Shush, and Wind Down to help you
set the phone down and get to sleep at night. And these tools
are really helping. App timers help users
stick to their goals over 90% of the time. And users of Wind Down had a
27% drop in nighttime usage. If you’re not using
these already, I would really recommend them. But this year, we want to help
even more with distraction. A lot of times, I just
want to sit down and focus to get something done. And when I’m trying to
do this– like, working. Maybe it’s studying for you– I don’t want email or
anything else to distract me. And that’s why we’ve created
a new mode for Android. It’s called Focus mode. When I enter Focus mode,
I can select the apps that I find distracting. For me, that’s email, news. So now they’re turned off
and I can really get to work. Those apps that distract
me are disabled. But I can still keep text. Because it’s important to
me that my family can always get a hold of me, until
I come out of Focus mode. And then everything is back. Focus mode is coming to
devices on P and Q this fall. [APPLAUSE] Now, finally, I want
to talk about families. For 84% of us parents,
technology use by our kids is a top concern. In the US, the average
age of kids getting phones is now eight. In Q, Family Link
parental controls will be built right into
the settings of the device. So when you set up a device
for someone in your family, Family Link will help
connect it to a parent. And you can review any apps that
your child wants to install. After that, you can set
daily screen time limits. You can check, where
are the apps where my kids are spending time? And you can set a device
bedtime so your kids can disconnect and get to sleep. And now on Android Q,
you can set time limits on specific apps. And when your child hits
that device bedtime, if you want to give them
just five more minutes, now we have bonus time. [APPLAUSE] Now, there’s a ton
more in Q that we don’t have time to cover– a ton– everything
from streaming media, to hearing aids, to
better connectivity, to new gesture UI, and more. So today, I’m excited
to announce that Q Beta 3 is available on 21 devices. That is 12 OEMS plus all Pixels. [APPLAUSE] And that is more than
double last year. We hope you head over to the
link to get it on your phone, because we would love
to have you try it out. And now I will hand
it over to Rick. Thank you very much. [MUSIC PLAYING] [APPLAUSE] RICK OSTERLOH: Thanks, Steph. Well, we’ve heard about some
terrific innovations today in Android, AI,
and the Assistant, and real breakthroughs in how
we’re able to help our users. I’d like to spend a
few minutes and talk about how some of those come
to life in our made by Google products. Now, we continue to believe that
the biggest breakthroughs are happening at the intersection
of AI, software, and hardware, whether that’s a tensor
processing unit, an entire data center, the phone in your hand,
or a helpful smart display in your home. Let’s start there. The smart home of today is
fragmented and frustrating. To deliver real
help in the home, you can’t start with technology. You have to start with people. And we’ve always worked to
put people first and build technology around their needs. There’s no more important
place to get this right than in the home. Let’s take a look. [MUSIC – PHILLIP PHILLIPS,
“HOME”] SPEAKER 20: No, no, no, sweetie. [DOORBELL RINGS] SPEAKER 21: It’s
[INAUDIBLE] me and Daddy. [GIGGLES] [BABY CRYING] [LAUGHTER] [CHEERING] [BABY CRYING] [APPLAUSE] RICK OSTERLOH: Your home
is the most special place in your life. So we need to be thoughtful
about the technology we create for it. By putting people
first, we’re going beyond the idea of a smart home
to create a truly helpful home. Over the past year, we’ve
brought the Nest and Google teams together to deliver on
our vision of the helpful home. And today, we’re further
simplifying things, bringing all of these products
together under the Nest name. As a single team and a
single product family, we’re following a set of
guiding principles that reflect our commitment
to putting people first. Now, to start, we
believe technology should be easy for everyone
in the home to use, whether they’re five or 95. The helpful home should also
be personal for everyone. With Google Assistant
at the core, we can provide a
personalized experience for the entire household,
even in communal spaces. And the tech in your
home should work together for a single seamless experience
across rooms and devices. Most importantly,
the helpful home needs to respect your privacy. And today, we’re publishing
privacy commitments for our home
products that clearly explain how they work,
the data we’re storing, and how it’s used. [APPLAUSE] Our vision for the helpful home
is anchored in the Assistant. And as you heard
from Scott, we’re continuing to get more
helpful over time. We want to make sure that
you can get the help you need where you need it. Google Home Hub, which
we’re renaming Nest Hub, was designed
specifically to bring the helpfulness of the Assistant
to any room in your house. Now, we’ve also been working
on a new display that builds on the things that
people love about Hub, but is designed
for communal spaces in the home where
the family gathers. Introducing Nest Hub Max. [APPLAUSE] It’s a new product that
has a camera and a larger 10-inch display, which
is perfect for the center of your helpful home. Hub Max pulls together
your connected devices into a home view dashboard,
where you can see your Nest cams, you can switch on
lights, control your music, and adjust your thermostat. Hub Max also supports Thread. So just like Nest Connect,
it communicates directly with Thread-supported
devices that need a low power connection,
like door locks or motion sensors. And we’ve designed Hub Max width
an incredibly helpful camera. If you want to know what’s
going on in your home, you can choose to use
it like a Nest Cam. You can turn it on when
you’re away from home. You can check on things
right from the Nest app in your phone. And just like a Nest Cam, it’s
easy to see your event history, enable home and away assist,
and you also get a notification if the camera detects any
motion or sees someone it doesn’t recognize in your home. Now, video calling is
easy too with Google Duo. The camera has a
wide angle lens. And it automatically adjusts to
keep you centered in the frame. You can chat with any
iOS or Android device, or a PC with a Chrome browser. You can also use Duo
to leave video messages for members of your household. Hub Max is designed to give you
full control over the camera. Nothing is streamed or recorded
unless you intentionally enable it. And you’ll always know
when the camera is on, with a green indicator light. You have multiple controls
to disable camera features. And a physical
switch on the back electrically disconnects the
camera and the microphones. And you can see all
these controls clearly on the display. [APPLAUSE] Thank you. Hub Max is designed to be used
by multiple people in your home and provide everyone
with the help they need in a personalized way. Now to help with that, we’ve
offered users the choice to enable voice match,
so the Assistant can recognize your voice
and respond directly to you. But today, we’re also
extending the options to personalize using the
camera, with a feature we call Face Match. For each person in your family
that chooses to turn it on, the Assistant guides
you through a process of creating a face model, which
is then encrypted and stored on the device. Then, whenever you walk
in front of the camera, Hub Max recognizes you and shows
just your information and not anyone else’s. Face Match’s facial
recognition technology is processed locally on the
device using on-device machine learning, so the camera data
never leaves the device. And in the morning, I
can walk into the kitchen and the Assistant
knows to greet me with my calendar, my commuting
details, the weather, and any other information
I need to start my day. And when I get home,
Hub Max welcomes me home with any reminders that
might be waiting for me. And the Assistant offers
personalized recommendations for music and TV shows. And I can even see if anyone’s
left me a video message. One of my favorite
things about Hub Max is that it’s a great
digital photo frame. No matter what kind
of day I’m having, nothing makes me
feel better than seeing some of my
favorite memories on this beautiful screen. And the Google
Photos integration makes this whole
process really simple. I can select my
family and friends. And Hub Max displays the best
photos of them from years ago or from earlier today. And now, with a
simple voice command, sharing my favorite shots
is easier than ever. The big screen
also makes Hub Max the kitchen TV you’ve
always wanted to. Tell it what you want to watch. Or if you need
help deciding, just ask the Assistant to pull
up our new onscreen guide. Hub Max can stream your
favorite live shows and sports on YouTube TV. But unlike your
kitchen TV, it can also teach you how to cook, see
who’s at the front door, and play your music. You’re also getting
full stereo sound– [MUSIC PLAYING] –with a powerful
rear-facing woofer. And now, when the
volume’s up, instead of yelling at the Assistant to
turn it down or pause the game, with the camera, it’s
as simple as a gesture. [PHONE RINGING] You just raise your hand. [APPLAUSE] And Hub Max uses
on-device machine learning to instantly
identify your gesture and pause your media. Hub Max is a Google
Assistant smart display that’s also a smart
home controller, a TV for your kitchen,
a great digital photo frame, an indoor camera, and
it’s perfect for video calling. All this will be available on
Nest Hub Max later this summer for just $229. [APPLAUSE] And today, we’re lowering the
price of the original Nest Hub from $149 to $129. [APPLAUSE] And we’re expanding its
availability to 12 new markets and supporting
nine new languages. So whether you prefer a Hub
with a camera or without one, we have a device that’ll
help you in your home. As I said earlier, there
is a fundamental difference between a smart home
and a helpful home. And we’re excited to unify all
our products under the Nest brand to make the helpful home
more real for more people. All right. Next, I want to
talk about Pixel. [APPLAUSE] Yeah, thank you. And I love talking about Pixel. I want to talk about our work to
bring a more hopeful smartphone experience to more people. A core element of
Google’s mission is to make technology more
available and accessible for everyone. And Sundar said it earlier. We need to ensure that
technology benefits the many, not just the few. But there’s been a
really troubling trend in the smartphone industry. To support the
latest technologies, everyone’s high-end
phones are getting more and more expensive. So we challenged
ourselves to see if we could optimize
our software and AI to work great on
more affordable hardware, so we can deliver these
high-end experiences at a more accessible price point. I want to introduce you to the
newest members of the Pixel family, Google
Pixel 3a and 3a XL, designed to deliver premium
features at a price people will love. [APPLAUSE] We didn’t compromise on the
capabilities and performance you’d expect from a
premium device, which is why we branded them Pixel. They start at just $399. [APPLAUSE] That’s about half the price– half the price of
typical flagship phones. And I want to introduce
Sabrina to tell you more about how we did it. [MUSIC PLAYING] [APPLAUSE] SABRINA ELLIS: Thanks, Rick. Delivering premium features
with high performance on a phone at this price point– it’s been a huge
engineering challenge. And I’m really proud
of what our team has been able to accomplish
with Pixel 3a. So let’s start with the design. Pixel 3a follows the design
language of the Pixel family– the familiar two-tone
look, smooth finish, and ergonomic unibody design. It feels good in your hand
and it looks beautiful. Pixel 3a comes in three colors,
Just Black, Clearly White, and a new color, Purple-ish. Everything looks amazing on
the vibrant OLED display. And your music, your
podcasts, they sound great in premium stereo sound. Pixel 3a supports Bluetooth
5.0 and USB-C digital audio. And we’ve also included a
3.5-millimeter audio jack. [APPLAUSE] Because we’ve heard some people
want more headphone options. But what Pixel is really known
for is its incredible camera. And with software
optimizations, we found a way to bring
our exclusive camera features and our
industry-leading image quality into Pixel 3a, so photos
look stunning in any light. What other smartphone
cameras try to do with expensive
hardware, we can deliver with
software and AI, including high-end
computational photography. So here’s what that means. Pixel 3a can take amazing
photos in low lights– [APPLAUSE] –with Night Sight. It’s one of Pixel’s
most popular features. We’ve also enabled
Pixel’s portrait mode on both the front
and rear cameras. And our Super Res Zoom applies
computational photography, so you can get closer
to your subject while still maintaining a
high degree of resolution. And all of your beautiful
photos are backed up for free in high quality
with Google Photos. [APPLAUSE] Pixel 3a also has
the helpful features you’d expect in a Pixel. Just squeeze the
sides of your phone to bring up the
Google Assistant. We’re using the AI in Pixel
3a to help manage your phone calls too. I’m pretty sure we all
hate getting robocalls. And Call Screen uses
Google speech recognition and natural language processing
to help you filter out those unwanted calls. It’s already screening
millions of them. Now, you might remember, last
year, we shared our vision for using AR in Google Maps. Starting today on
Pixel phones, when you use walking
directions, instead of staring at that blue
dot on your phone, you’re going to see
arrows in the real world to tell you where to turn next. [APPLAUSE] We’re just beginning our
journey with AR and Maps. And we’re really
excited for Pixel users to experience this
early preview. Battery life– it’s one of
the most important features on a smartphone. It makes sense. People need to know that
their phone won’t quit on them before the end of their day. Pixel 3a has adaptive battery. It uses machine
learning to optimize based on how you use your
phone, so you can get up to 30 hours on a single charge. [APPLAUSE] And with the included
18-watt charger, you’ll get up to seven
hours of battery life with just 15
minutes of charging. Pixel 3a doesn’t compromise
on security either. It’s got the same comprehensive
approach as Pixel 3. On the hardware side,
our Titan M security chip protects your sensitive data
on the device, like login credentials, disk encryption,
app data, and OS integrity. On the software side, you get
the latest Google security patches and updates
for three years, including Android Q this summer. So instead of getting slower
and less secure over time, your Pixel gets better
with every update. We think this hybrid approach
provides the strongest data protection. And in a recent
Gartner report, Pixel scored the highest for built-in
security among smartphones. [APPLAUSE] Pixel 3a offers the
complete Pixel experience. And we’re proud to make it
available and affordable to more people around the world. Verizon’s been a great partner
over the past two and a half years in the US. And we’re excited to be
partnering with them again for the launch of Pixel 3a. And for the first time, we’re
expanding our US carrier partnerships. So the entire Pixel family
is now available for sale at T-Mobile, Sprint,
and US Cellular. [APPLAUSE] You can also get Pixel
3a from the Google Store and use it on any US carrier,
including Google Fi and AT&T. Pixel 3a and 3a XL are available
in 13 markets, starting today. [APPLAUSE] You can find more details
online at the Google Store. We’re really excited
to have you try it out. Next, Jeff will tell you about
our efforts in Google AI. But first, here’s a quick
look at our new Pixel. [MUSIC PLAYING] SPEAKER 22: Hey, Google,
show me donut shops nearby. [APPLAUSE] [MUSIC PLAYING] JEFF DEAN: Hi, everyone. [APPLAUSE] Everything from building
a low cost premium device like the one you just
saw without compromising on capabilities to developing
a truly helpful Assistant were all built on
a tremendous amount of research and innovation
under the covers. And they’re examples of
what we do at Google AI. Google AI is a
collection of teams focused on making progress
in artificial intelligence research across a wide
range of different domains. We focus on solving fundamental
computer science challenges in order to solve
problems for people. That includes things like
improving speech recognition models to answer
questions faster and let you interact with
your device quickly, or pushing the boundaries
of computer vision to help people interact with
their world in new ways, as you’ve seen today. We publish papers, , release
open source software, and apply our research
to Google products. The goal is really to
solve problems everyday that touch billions of people. One of the things I’m
most excited about is progress in
language understanding. As Scott mentioned earlier,
so much of our daily life depends on actually
understanding language– reading traffic signs
and shopping lists, writing emails, communicating
with the people around us. We’d really want computers
to have the same fluency with language that we do– not just understand
surface forms of the words, but actually understand
what sentences mean. Unlocking that
would get us closer to our mission of organizing
the world’s information and making it universally
accessible and useful. In the past few years,
we’ve made major strides. Take teaching a machine
to answer questions like this one about Carlsbad
Caverns, a national park in New Mexico. Only recently, the
state-of-the-art architecture for language understanding was
something called a recurrent neural network, or RNN. RNNs process words
sequentially, one after another. They work well for
modeling short sequences, like sentences,
but they struggle to make abstract
associations, like knowing that stalactites and stalagmites
are natural formations, and that cement pathways,
for example, are not. In 2017, we made a leap
forward with our research on transformers, models that
process words in parallel. One year later, we used
it as the foundation for a technique we called
bi-directional encoder representations
from transformers. It’s a bit of a mouthful,
so we just call it BERT. BERT models can consider
the full context of a word by looking at the words that
come before and after it. They’re pre-trained
using plain text from the web and
other textual sources. To do that, we use a
process to train it that’s a little like the
word game of Mad Libs. We hired about 20%
of the input words. And we train the model to
guess those missing words. You can actually try this
at home with a bit of text that you have. Hide a few words and see if
you can guess what they are. That’s effectively
what we’re doing. This approach is
much more effective for understanding language. When we published the research,
BERT obtained state-of-the-art results on 11 different
language processing tasks. Fast forward to today. And we’re excited
to see how BERT can help us answer more
complex questions that are relevant to
you, whether that’s getting the flight time
from Indiana to Honolulu, learning a new
weightlifting exercise, or translating
between languages. Research like this gets us
closer to technology that can truly understand language. We’re now working with product
teams all across Google to see how we can use BERT to
solve more problems in more places. We’re excited to bring this
to people around the world to help them get the
information they need everyday. All this machine
learning momentum, though, wouldn’t be possible
without platform innovation. TensorFlow is the
software infrastructure that underlies our work
in machine learning and artificial intelligence. When we developed TensorFlow,
we wanted everyone to be able to use
machine learning, so we made it an
open source platform. And while it’s been
essential to our work, we’ve been amazed to see what
other people outside of Google have used it for– all
kinds of different things. We’ve seen engineers at
Roma Tre University in Italy parsing handwritten
medieval manuscripts. We’ve seen coders
in France colorizing black and white photography. We’ve even seen companies
developing fitness centers for cows. The work that people are doing
is really inspiring to us. It pushes us to keep
asking ourselves, how can machine
learning crack open previously unsolvable problems
in order to help more people? One example is our work in
the field of health care. We’re really optimistic that our
research can create real world impact in medicine by improving
solutions and establishing new diagnostic procedures. To share more, here’s Dr. Lily
Peng from the Google AI health care team. [MUSIC PLAYING] [APPLAUSE] LILY PENG: Thanks, Jeff. So as a doctor,
what I care about most is improving
patients’ lives. And that means good care
and accurate diagnoses. That’s why I was so excited
two years ago at I/O when we shared our work
in diabetic retinopathy. This is a complication
of diabetes that puts over
400 million people around the world at
risk for vision loss. Since then, we’ve been piloting
this work with patients in clinical settings. Our partners have
fairly recently received European
regulatory approval for the machine learning model. And we have clinical
deployments in Thailand and in India that
are already screening thousands of patients. In addition to diabetes,
one of the other areas we think AI can help
doctors is in oncology. Today, we’d like
to share our work on another project
in cancer screening where AI can help catch
lung cancer earlier. So lung cancer causes more
deaths than any cancer. It’s actually the most
common cause of death globally, accounting for
3% of annual mortality. We know that, when cases
are diagnosed early, patients have a higher
chance of survival. But unfortunately, over
80% of lung cancers are not caught early. Randomized control
trials have shown that screening with low dose
CTs can help reduce mortality. But there’s opportunity to
make them more accurate. So in a paper we are about to
publish in “Nature Medicine,” we describe a deep learning
model that can analyze CT scans and predict lung malignancies. To do it, we trained a neural
network with de-identified lung cancer scans from our
partners at the NCI– the National Cancer Institute– and Northwestern University. By looking at many
examples, the model learns to detect
malignancy with performance that meets or exceeds that
of trained radiologists. So concretely, how
might this help? Very early stage
cancer is minuscule, and can be hard to see, even
for seasoned radiologists, which means that many patients
with late stage lung cancer have subtle signs
on earlier scans. So take this case, where
an asymptomatic patient with no history of cancer
had a CT scan for screening. This scan was
interpreted as normal. One year later, that same
patient had another scan. It picked up a late
stage cancer, one that’s much harder to treat. So we used our AI system to
review that initial scan. So let’s be clear. This is a tough case. We showed this initial
scan to other radiologists, and five out of six
missed this cancer. But our model was able to detect
these early signs one year before the patient was
actually diagnosed– one year. And that year could translate
to an increased survival rate of 40% for patients like this. [APPLAUSE] So clearly, this is a
promising but early result. And we’re very much
looking forward to partnering with
the medical community to use technology
like this to help improve outcomes for patients. Now I’ll hand it back to Jeff. [APPLAUSE] JEFF DEAN: Thanks, Lily. The same technologies that you
just saw driving health care innovation have applications
across almost every field imaginable. Our AI for Social Good program
brings together our efforts to use AI to explore and
address some of the world’s most challenging problems. Last year, we announced the
program and its two pillars– research and
engineering, and building the external ecosystem. Let’s talk first about
research and engineering. One project we’re working
that’s already creating impact is our work on
flood forecasting. Floods are the most common,
deadliest natural disasters on the planet. Every year, they affect
up to 230 million people across the world, more
than storms and earthquakes combined. 20% of flood fatalities
happen in India alone. This is a problem
that we’re even seeing this week with the
impact from Cyclone Fani. Floods prevent kids
from being able to play in their neighborhoods
or parents from protecting and
providing for their families, often because they don’t
have enough advance warning. And without consistent
accurate warning systems, people are prone to ignore
warnings and be unprepared. That’s especially detrimental in
areas hit with annual monsoons. That’s why, last fall,
we shared our work on flood forecasting models
that can more accurately predict flood timing,
location, and severity. Through a partnership
with India’s Central Water Commission, we began
sending early flood warnings to the phones of users
who might be affected. Today, we’re
thrilled to announce the expansion of our
detection and alerting system for the upcoming
monsoon season. The expanded area
will cover millions of people living along the
Ganges and Brahmaputra River areas. Not only are we increasing
the area of coverage, but we’re also better
forecasting where the floods will hit hardest. Through a new version
of our public alerts, people can better understand
whether they’ll be affected, so they can protect
themselves and their families. Our model simulates
water behavior across the flood plain,
showing the exact areas that will be affected. We combine thousands
of satellite images to create high resolution
elevation maps, using a process similar
to stereographic imaging, to figure out the
height of the ground. We then use neural networks
to correct the terrain, so it’s even more accurate. And then we use
physics to simulate how flooding will happen. We also collaborate
with the government to receive up-to-date
stream gauge measurements and send forecasts in real time. We’re excited to continue
working with partners to increase the accuracy and
precision of these models, which we hope will make
people safer from flooding all around the world. Research like this is critical. But we also know that AI will
have the biggest impact when people from many
different backgrounds all come together to develop new
solutions to problems they see. That’s why the second pillar of
our AI for Social Good program is to build the
external ecosystem. We want to empower everyone
to use AI to solve problems they see in their communities. Last year, we partnered
with Google.org to launch the Google
AI Impact Challenge. It was a call for nonprofits,
social enterprises, and universities to share
their ideas for using AI to address societal challenges. We received applications
from 119 countries across six continents,
representing all kinds of sizes and
types of organizations. Today, we’re really excited
to announce the 20 selected. We even have a few of
them with us today. Let’s give them a warm welcome. [APPLAUSE] Here’s the list
of organizations. These organizations are
working on some of the world’s most meaningful issues. La Fondation Medicins
sans Frontieres is using image recognition
to help medical staff analyze anti-microbial images
in order to prescribe the right antibiotics
for bacterial infections. New York University
in partnership with the Fire Department
of New York City is building a model to help
speed up emergency response times. This could really improve
public health and safety. And Makerere
University in Uganda will use AI to create a
high resolution monitoring network to shape public policies
for improving air quality. We’ll be supporting
our 20 grantees and bringing these
ideas to life. We’re providing $25 million
in funding from Google.org, as well as coaching and
resources from teams all across Google. Congratulations to
all our grantees. [APPLAUSE] As we head into the
next decade, I’m really excited about
what’s to come. There are so many
promising avenues for fundamental research. For instance, machine
learning models today, typically, we can get
them to be good at solving individual tasks. But what if they
could generalize across thousands of tasks,
solving new problems faster, and with just a few
examples to learn from? The keys to progress on these
kinds of research problems are those most human
characteristics, perseverance and ingenuity. As you heard Sundar mention
at the start of the day, we’re moving from a company
that helps you find answers to a company that also
helps you get things done. And all the products we showed
you today share a single goal– to be helpful. At the same time,
we want to ensure that the benefits of
technology are felt everywhere, continue to uphold our
foundation of user trust, and build a more helpful
Google for everyone. To everyone joining
us on the livestream, thank you for tuning in. And to everyone here with
us in the audience today, welcome to Google I/O 2019. Thank you. And enjoy the rest of I/O. [MUSIC PLAYING]

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