[MUSIC PLAYING]
SUNDAR PICHAI: Good morning, everyone.
Welcome to Google I/O.
[CHEERING]
It's great to see so many of you here at Shoreline,
so many developers.
And a huge thanks to the millions
joining from around the world, from Bangladesh, to Brazil,
to our new Bayview campus right next door.
It's so great to have you as always.
As you may have heard, AI is having a very busy year.
So we've got lots to talk about.
Let's get started.
Seven years into our journey, as an AI first company,
we are at an exciting inflection point.
We have an opportunity to make AI even more helpful
for people, for businesses, for communities, for everyone.
We have been applying AI to make our products radically more
helpful for a while.
With generative AI, we are taking the next step.
With a bold and responsible approach,
we are reimagining all our core products, including search.
You will hear more later in the keynote.
Let me start with few examples of how
generative AI is helping to evolve our products,
starting with Gmail.
In 2017, we launched Smart Reply,
short responses you could select with just one click.
Next came Smart Compose, which offered writing suggestions
as you type.
Smart Compose led to more advanced writing features
powered by AI.
They've been used in Workspace over 180 billion times
in the past year alone.
And now with a much more powerful generative model,
we are taking the next step in Gmail with Help Me Write.
Let's say you got this email that your flight was canceled.
The airline had sent a voucher.
But what you really want is a full refund.
You could reply and use Help Me Write.
Just type in the prompt of what you
want, an email to ask for a full refund, hit Create,
and a full draft appears.
As you can see, it conveniently pulled in flight details
from the previous email.
And it looks pretty close to what you want to send.
Maybe you want to refine it further.
In this case, a more elaborate email
might increase the chances of getting the refund.
[APPLAUSE]
And there you go.
I think it's ready to send.
Help Me Write we'll start rolling out as part
of our Workspace updates.
And just like with Smart Compose,
you will see it get better over time.
The next example is Maps.
Since the early days of Street View,
AI has stitched together billions of panoramic images,
so people can explore the world from their device.
At last year's I/O we introduced immersive view,
which uses AI to create a high-fidelity
representation of a place, so you can experience it
before you visit.
Now we are expanding that same technology
to do what Maps does best, help you get where you want to go.
Google Maps provides 20 billion kilometers
of directions every day.
That's a lot of trips.
Imagine if you could see your whole trip in advance.
With Immersive View for Routes now
you can, whether you're walking, cycling, or driving.
Let me show you what I mean.
Say, I'm in New York City, and I want to go on a bike ride.
Maps has given me a couple of options close to where I am.
I like the one on the waterfront,
so let's go with that.
Looks scenic.
And I want to get a feel for it first.
Click on Immersive View for Routes.
And it's an entirely new way to look at my journey.
I can zoom in to get an incredible bird's eye
view of the ride.
And as we turn, we get onto a great bike path.
[APPLAUSE]
It looks like it's going to be a beautiful ride.
You can also check today's air quality.
Looks like AQI is 43, pretty good.
And if I want to check traffic and weather,
and see how they might change over the next few hours,
I can do that.
Looks like it's going to pour later.
So maybe I want to get going now.
Immersive View for Routes will begin
to roll out over the summer and launch in 15 cities
by the end of the year, including London, New
York, Tokyo, and San Francisco.
[CHEERING]
Another product made better by AI is Google Photos.
We introduced it at I/O in 2015.
It was one of our first AI-native products.
Breakthroughs in machine-learning
made it possible to search your photos for things like people,
sunsets, or waterfalls.
Of course, we want you to do more than just search photos.
We also want to help you make them better.
In fact, every month 1.7 billion images
are edited in Google Photos.
AI advancements give us more powerful ways to do this.
For example, Magic Eraser, launched first on Pixel,
uses AI-powered computational photography
to remove unwanted distractions.
And later this year, using a combination
of semantic understanding and generative
AI, you can do much more with a new experience
called Magic Editor.
Let's have a look.
Say, you are on a hike, and you stop
to take a photo in front of a waterfall.
You wish you had taken your bag off for the photo.
So let's go ahead and remove that back strap.
The photo feels a bit dark, so you can improve the lighting.
And maybe you want to even get rid of some clouds
to make it feel as sunny as you remember it.
Looking even closer, you wish you
had posed so it looks like you're really catching
the water in your hand.
No problem.
You can adjust at.
[APPLAUSE]
There you go.
Let's look at one more photo.
This is a great photo.
As a parent, you always want your kid
at the center of it all.
And it looks like the balloons got cut off in this one.
So you can go ahead and reposition the birthday boy.
Magic Editor automatically recreates
parts of the bench and balloons that were not
captured in the original shot.
As a finishing touch, you can punch up the sky.
It changes the lighting in the rest of the photo,
so the edit feels consistent.
It's truly magical.
We are excited to roll out Magic Editor in Google Photos
later this year.
[APPLAUSE]
From Gmail and photos to maps, these
are just a few examples of how AI can help you in moments
that matter.
And there is so much more we can do
to deliver the full potential of AI
across the products you know and love.
Today we have 15 products that each
serve more than half a billion people and businesses.
And six of those products, serve over two billion users each.
This gives us so many opportunities
to deliver on our mission to organize the world's
information and make it universally
accessible and useful.
It's a timeless mission that feels
more relevant with each passing year.
And looking ahead, making AI helpful for everyone
is the most profound way we will advance our mission.
And we are doing this in four important ways.
First, by improving your knowledge
and learning and deepening your understanding of the world.
Second, by boosting creativity and productivity.
So you can express yourself and get things done.
Third, by enabling developers and businesses
to build their own transformative products
and services.
And finally, by building and deploying AI responsibly,
so that everyone can benefit equally.
We are so excited by the opportunities ahead.
Our ability to make AI helpful for everyone
relies on continuously advancing our foundation models.
So I want to take a moment to share
how we are approaching them.
Last year you heard us talk about PaLM,
which led to many improvements across our products.
Today we are ready to announce our latest PaLM model
in production, PaLM 2.
[APPLAUSE]
PaLM 2 builds on our fundamental research and our latest
infrastructure.
It's highly capable at a wide range of tasks
and easy to deploy.
We are announcing over 25 products and features powered
by PaLM 2 today.
PaLM 2 models deliver excellent foundational capabilities
across a wide range of sizes.
We have affectionately named them Gecko, Otter,
Bison, and Unicorn.
Gecko so lightweight that it can work on mobile devices,
fast enough for great interactive applications
on device, even when offline.
PaLM 2 models are stronger in logic and reasoning thanks
to broad training on scientific and mathematical topics.
It's also trained on multilingual text,
spanning over 100 languages.
So it understands and generates nuanced results.
Combined with powerful coding capabilities,
PaLM 2 can also help developers collaborating around the world.
Let's look at this example.
Let's say you're working with a colleague in Seoul,
and you're debugging code.
You can ask it to fix a bug and help out
your teammate by adding comments in Korean to the code.
It first recognizes the code is recursive, suggests a fix,
and even explains the reasoning behind the fix.
And as you can see, it added comments in Korean,
just like you asked.
[APPLAUSE]
While PaLM 2 is highly capable, it really
shines when fine-tuned on domain-specific knowledge.
We recently released Sec-PaLM, a version of PaLM
to fine tune for security-use cases.
It uses AI to better detect malicious scripts
and can help security experts understand and resolve threats.
Another example is Med-PaLM 2.
In this case, it's fine-tuned on medical knowledge.
This fine-tuning achieved a 9x reduction
in inaccurate reasoning, when compared to the model,
approaching the performance of clinician experts
who answered the same set of questions.
In fact, Med-PaLM was the first language model
to perform at expert level on medical licensing exam-style
questions and is currently the state of the art.
We are also working to add capabilities to Med-PaLM 2,
so that it can synthesize information
from medical imaging, like plain films and mammograms.
You can imagine an AI collaborator
that helps radiologists interpret images
and communicate the results.
These are some examples of PaLM 2
being used in specialized domains.
We can't wait to see it used in more.
That's why I'm pleased to announce that it is now
available in Preview.
And I'll let Thomas share more.
[APPLAUSE]
PaLM 2 is the latest step in our decade-long journey
to bring AI in responsible ways to billions of people.
It builds on progress made by two world class teams,
the Brain Team and DeepMind.
Looking back at the defining AI breakthroughs
over the last decade, these teams
have contributed to a significant number of them,
AlphaGo, Transformers, sequence to sequence models, and so on.
All this helps set the stage for the inflection point
we are at today.
We recently brought these two teams together
into a single unit, Google DeepMind.
Using the computational resources of Google,
they have focused on building more capable systems safely
and responsibly.
This includes our next generation foundation model,
Gemini, which is still in training.
Gemini was created from the ground up
to be multi-modal, highly efficient tool in API
integrations, and built to enable future innovation,
like memory and planning.
While still early, we are already
seeing impressive multi-modal capabilities not seen
in prior models.
Once fine-tuned and rigorously tested for safety,
Gemini will be available at various sizes and capabilities,
just like PaLM 2.
As we invest in more advanced models,
we are also deeply investing in AI responsibility.
This includes having the tools to identify synthetically
generated content whenever you encounter it.
Two important approaches are watermarking and metadata.
Watermarking embeds information directly into content
in ways that are maintained even through modest image editing.
Moving forward, we are building our models
to include watermarking and other techniques
from the start.
If you look at a synthetic image,
it's impressive how real it looks.
So you can imagine how important this
is going to be in the future.
Metadata allows content creators to associate additional context
with original files, giving you more information whenever
you encounter an image.
We will ensure every one of our AI-generated images
has that metadata.
James will talk about our responsible approach
to AI later.
As models get better and more capable,
one of the most exciting opportunities
is making them available for people to engage with directly.
That's the opportunity we have at Bard, our experiment
for conversational AI.
We are rapidly evolving Bard.
It now supports a wide range of programming capabilities.
And it's gotten much smarter at reasoning and math problems.
And as of today, it is now fully running on PaLM 2.