[APPLAUSE]
[MUSIC PLAYING]
DEMIS HASSABIS: Thanks, Sundar.
It's so great to be here.
Ever since I was a kid playing chess for the England
junior team, I've been thinking about the nature
of intelligence.
I was captivated by the idea of a computer that
could think like a person.
It's ultimately why I became a programmer
and studied neuroscience.
I co-founded DeepMind in 2010, with the goal
of one day building AGI, Artificial General
Intelligence, a system that has human-level cognitive
capabilities.
I've always believed that if we could build this technology
responsibly, its impact would be truly profound,
and it could benefit humanity in incredible ways.
Last year, we reached a milestone on that path
when we formed Google DeepMind, combining
AI talent from across the company into one super-unit.
Since then, we built AI systems that can do an amazing range
of things, from turning language and vision into action
for robots, navigating complex virtual 3D environments,
solving olympiad-level maths problems,
and even discovering thousands of new materials.
Just last week, we announced our next-generation AlphaFold model.
It can predict the structure and interactions of nearly all
of life's molecules, including how proteins interact
with strands of DNA and RNA.
This will accelerate vitally important biological and medical
research from disease understanding to drug discovery.
And all of this was made possible
with the best infrastructure for the AI era,
including our highly-optimized Tensor Processing Units.
At the center of our efforts is our Gemini model.
It's built up, from the ground up,
to be natively multimodal because that's
how we interact with and understand the world around us.
We've built a variety of models for different use cases.
You've seen how powerful Gemini 1.5 Pro is.
But we also know, from user feedback,
that some applications need lower latency and a lower cost
to serve.
So today, we're introducing Gemini 1.5 Flash.
Flash--
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Flash is a lighter-weight model compared to Pro.
It's designed to be fast and cost
efficient to serve at scale, while still featuring
multimodal reasoning capabilities and breakthrough
long context.
Flash is optimized for tasks where low latency and efficiency
matter most.
Starting today, you can use 1.5 Flash and 1.5 Pro,
with up to 1 million tokens in Google AI Studio and Vertex AI,
and developers can sign up to try 2 million tokens.
We're so excited to see what all of you will create with it.
And you'll hear a little more about Flash later on from Josh.
We're very excited by the progress
we've made so far with our family of Gemini models,
but we're always striving to push the state of the art
even further.
At any one time, we have many different models in training,
and we use our very large and powerful ones
to help teach and train our production-ready models.
Together, with user feedback, this cutting-edge research
will help us to build amazing new products
for billions of people.
For example, in December, we shared a glimpse
into the future of how people would interact
with multimodal AI and how this would end up
powering a new set of transformative experiences.
Today, we have some exciting new progress
to share about the future of AI assistants
that we're calling Project Astra.
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For a long time, we've wanted to build a universal AI
agent that can be truly helpful in everyday life.
Our work making this vision a reality goes back many years.
It's why we made Gemini multimodal
from the very beginning.
An agent like this has to understand and respond
to our complex and dynamic world, just like we do.
It would need to take in and remember
what it sees so it can understand context and take
action.
And it would have to be proactive, teachable,
and personal so you can talk to it naturally without lag
or delay.
While we've made some great strides
in developing AI systems that can understand
multimodal information, getting response time down
to something conversational is a difficult engineering challenge.
Building on our Gemini model, we developed
agents that can process information faster
by continuously encoding video frames,
combining the video and speech input into a timeline of events,
and caching this for efficient recall.
We've also enhanced how they sound, with a wider range
of intonations.
These agents better understand the context you're in
and can respond quickly in conversation,
making the pace and quality of interaction
feel much more natural.
Here's a video of our prototype, which you'll see has two parts.
Each part was captured in a single take in real time.
[VIDEO PLAYBACK]
- OK.
Let's do some tests.
That's right.
Ha.
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What can I add here to make this system faster?
[CHEERING]
What does this remind you of?
[LAUGHTER]
All right.
Give me a band name for this duo.
Nice.
Thanks, Gemini.
[END PLAYBACK]
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DEMIS HASSABIS: I think you'll agree,
it's amazing to see how far AI has come,
especially when it comes to spatial understanding,
video processing, and memory.
It's easy to envisage a future where you can have an expert
assistant by your side through your phone or new exciting form
factors, like glasses.
Some of these agent capabilities will
come to Google products, like Gemini app, later this year.
For those of you on site today, you
can try out a live demo version of this experience in the AI
Sandbox area.
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Next, let's take a look at how our innovations
are helping people bring new creative ideas to life.
Today, we're introducing a series
of updates across our generative media tools,
with new models covering image, music, and video.
Over the past year, we've been enhancing quality, improving
safety, and increasing access.
To help tell this story, here's Doug.
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DOUG ECK: Thanks, Demis.
Over the past few months, we've been working hard to build a new
image-generation model from the ground up,
with stronger evaluations, extensive red teaming,
and state-of-the-art watermarking with SynthID.
Today, I'm so excited to introduce Imagin 3.
It's our most capable image generation model yet.
Imagin 3 is more photorealistic--
you can literally count the whiskers on its snout--
with richer details, like this incredible sunlight in the shot,
and fewer visual artifacts or distorted images.
It understands prompts written the way people write.
The more creative and detailed you are, the better.
And Imagin 3 remembers to incorporate small details,
like the wildflowers or "small blue bird"
in this longer prompt.
Plus, this is our best model yet for rendering text,
which has been a challenge for image-generation models.
In side-by-side comparisons, independent evaluators
preferred Imagin 3 over other popular image-generation models.
In sum, Imagin 3 is our highest-quality image-generation
model so far.
You can sign up today to try Imagin
3 in ImageFX, part of our suite of AI tools at labs.google.
And it will be coming soon to developers and enterprise
customers in Vertex AI.
Another area full of creative possibility is generative music.
I've been working in this space for over 20 years,
and this is by far the most exciting year of my career.
We're exploring ways of working with artists to expand
their creativity with AI.
Together with YouTube, we've been building Music AI Sandbox,
a suite of professional music AI tools
that can create new instrumental sections from scratch,
transfer styles between tracks, and more.
To help us design and test them, we've
been working closely with incredible musicians,
songwriters, and producers.
Some of them made even entirely new songs
in ways that would have not been possible without these tools.
Let's hear from some of the artists we've been working with.
[VIDEO PLAYBACK]
[GUITAR SOLO]
- I'm going to put this right back
into the music AI tool, this same boom, boom, boom, boom,
boom.
What happens if Haiti meets Brazil?
Dude, I have no clue what's about to be spat out.
And this is what excites me.
[SYNTHESIZED MUSIC]
[VOCALIZING]
As a hip-hop producer, we dug in the crates.
We playing these vinyls.
And the part where there's no vocal, we pull it, we sample it,
and we create an entire song around that.
So right now, we digging in the infinite crate.
It's endless.
- Where I found the AI really useful for me,
this way to fill in the sparser elements of my loops.
OK.
Let's try bongos.
We're going to put viola.
We're going to put rhythmic clapping.
And we're going to see what happens there.
Ooh.
And it makes it sound, ironically,
at the end of the day, a little more human.
So then this is entirely Google's loops right here.
These are "Gloops."
[SYNTHESIZED MUSIC]
It's like having this weird friend that's just
like, try this, try that.
And then you're like, oh, OK.
Yeah.
No, that's pretty dope.
[VOCALIZING]
[MUSIC PLAYING]
- The tools are capable of speeding up
the process of what's in my head, getting it out.
You're able to move light speed with your creativity.
This is amazing.
That right there?
[END PLAYBACK]
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DEMIS HASSIBIS: I think this really
shows what's possible when we work with the artist community
on the future of music.
You can find some brand-new songs
from these acclaimed artists and songwriters on their YouTube
channels now.
There's one more area I'm really excited to share with you.
Our teams have made some incredible progress
in generative video.
Today, I'm excited to announce our newest,
most capable, generative video model called Veo.
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Veo creates high-quality 1080p videos from text, image,
and video prompts.
It can capture the details of your instructions
in different visual and cinematic styles.
You can prompt for things like aerial shots of a landscape
or time lapse and further edit your videos
using additional prompts.
You can use Veo in our new experimental tool called
VideoFX.
We're exploring features like storyboarding and generating
longer scenes.
Veo gives you unprecedented creative control.
Techniques for generating static images have come a long way,
but generating video is a different challenge altogether.
Not only is it important to understand where an object
or subject should be in space, it needs
to maintain this consistency over time, just
like the car in this video.
Veo builds upon years of our pioneering generative video
model work, including GQN, Phenaki, Walt, VideoPoet,
Lumiere, and much more.
We combine the best of these architectures and techniques
to improve consistency, quality, and output resolution.
To see what Veo can do, we put it
in the hands of an amazing filmmaker.
Let's take a look.
[VIDEO PLAYBACK]
[MUSIC PLAYING]
- Well, I've been interested in AI for a couple of years now.
We got in contact with some of the people at Google,
and they had been working on something of their own.
So we're all meeting here at Gilga Farms
to make a short film.
- The core technology is Google DeepMind's generative video
model that has been trained to convert input text into output
video.
[LAUGHTER]
- It looks good.
- We are able to bring ideas to life that were otherwise
not possible.
We can visualize things on a time scale that's 10 or 100
times faster than before.
- When you're shooting, you can't really
iterate as much as you wish.
And so we've been hearing that feedback
that it allows for more optionality, more
iteration, more improvisation.
- But that's what's cool about it is
you can make a mistake faster.
That's all you really want, at the end of the day,
at least in art, is just to make mistakes fast.
- So using Gemini's multimodal capabilities to optimize
the model-training process, Veo is able to better capture
the nuance from prompts.
So this includes cinematic techniques and visual effects,
giving you total creative control.
- Everybody's going to become a director,
and everybody should be a director
because at the heart of all of this is just storytelling.
The closer we are to being able to tell each other our stories,
the more we'll understand each other.
- These models are really enabling us to be more creative
and to share that creativity with each other.
[MUSIC PLAYING]
[END PLAYBACK]
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DEMIS HASSABIS: Over the coming weeks, some of these features
will be available to select creators
through VideoFX at labs.google.
And the waitlist is open now.
Of course, these advances in generative video
go beyond the beautiful visuals you've seen today.
By teaching future AI models how to solve problems creatively
or, in effect, simulate the physics of our world,
we can build more useful systems that
can help people communicate in new ways
and thereby advance the frontiers of AI.
When we first began this journey to build AI
more than 15 years ago, we knew that, one day,
it would change everything.
Now that time is here.
And we continue to be amazed by the progress we see
and inspired by the advances still to come on the path
to AGI.
Thanks, and back to you, Sundar.
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