[MUSIC PLAYING]
JOSH WOODWARD: Thanks, Dave.
It's amazing to see Gemini Nano do all of that
directly on an Android phone.
That was our plan all along, to create a natively multimodal
Gemini in a range of sizes so you all, as developers, can
choose the one that works best for you.
Throughout the morning, you've heard a lot about our Gemini 1.5
series.
And I want to talk about the two models you can access today--
1.5 Pro, which is getting a series of quality improvements
that go out right about now, and the brand-new 1.5 Flash.
Both are available today globally in over 200 countries
and territories.
[APPLAUSE]
You can go over to AI Studio or Vertex AI
if you're a Google Cloud customer to give them a try.
Now, both of these models are natively multimodal.
That means you can interleave text images, audio, and video
as inputs and pack that massive 1 million token context window.
And if you go to ai.google.dev today,
you can sign up to try the 2 million
token context window for 1.5 Pro.
Now, we're also adding a bunch of new developer features,
starting with video frame extraction--
that's going to be in the Gemini API--
parallel function calling so you can
return more than one function call at a time,
and my favorite, context caching so you
can send all of your files to the model
once and not have to re send them over and over again.
That should make the long context even more
useful and more affordable.
It ships next month.
[APPLAUSE]
Now, we're using Google's infrastructure
to serve these models so developers, like all of you,
can get great prices.
1.5 Pro is $7 per 1 million tokens.
And I'm excited to share that for prompts up to 128K,
it'll be 50% less, for $3.50.
And 1.5 Flash will start at $0.35 per 1 million tokens.
[APPLAUSE]
Now, one thing you might be wondering
is which model is best for your use case.
Here's how we've been thinking about it on the team.
We use 1.5 Pro for complex tasks where you really
want the highest quality response
and it's OK if it takes a little bit longer to come back.
We're using 1.5 Flash for quick tasks,
where the speed of the model is what matters the most.
And as a developer, you can go try them both out today
and see what works best for you.
Now, I'm going to show you how it works here
in AI Studio, the fastest way to build with Gemini.
And we'll pull it up here.
And you can see this is AI Studio.
It's free to use.
You don't have to configure anything to get going.
You just go to aistudio.google.com, log in
with your Google account, and you can just pick the model here
in the right that works best for you.
So one of the ways we've been using 1.5 Flash
is to actually learn from customer feedback
about some of our Labs products.
Flash makes this possible with its low latency.
So what we did here is we just took
a bunch of different feedback from our customer forums.
You can put it into Flash, load up a prompt, and hit Run.
Now, in the background, what it's going to do is it's going
to go through that 93,000-token pile of information.
And you can see here, it starts streaming it back.
Now, this is really helpful, because it pulls out
the themes for us.
It gives us all the right places where we can start to look.
And you can see this is from some
of the benefits from NotebookLM, like we showed earlier.
Now, what's great about this is that you can take something
like this in AI Studio, prototype here in 10 seconds
and, with one click in the upper left, get an API key.
Or over here in the upper right, just tap Get Code,
and you've got all of the model configurations,
the safety settings ready to go, straight into your IDE.
Now, over time, if you find that you
need more enterprise-grade features,
you can use the same Gemini 1.5 models
and the same configurations right in Vertex AI.
That way you can scale up with Google Cloud
as your enterprise needs grow.
So that's our newly updated Gemini 1.5 Pro
and the new 1.5 Flash, both of which are available today
globally.
And you'll hear a lot more about them in the developer keynote
later today.
[APPLAUSE]
Now, let's shift gears and talk about Gemma,
our family of open models, which are
crucial for driving AI innovation and responsibility.
Gemma is built from the same research and technology
as Gemini.
It offers top performance and comes in lightweight 7B and 2B
sizes.
Now, since it launched less than three months ago,
it's been downloaded millions of times
across all the major model hubs.
Developers and researchers have been using it and customizing
the base Gemma model and using some
of our pre-trained variants, like RecurrentGemma
and CodeGemma and today's newest member, PaliGemma,
our first vision language open model.
And it's available right now.
[APPLAUSE]
It's optimized for a range of image captioning, visual Q&A,
and other image-labeling tasks.
So go give it a try.
I'm also too excited to announce that we have Gemma 2 coming.
It's the next generation of Gemma,
and it will be available in June.
One of the top requests we've heard from developers
is for a bigger Gemma model, but it's still
got to fit in a size that's easy for all of you to use.
So in a few weeks, we'll be adding a new 27-billion
parameter model to Gemma 2.
And here's what's great about it.
This size is optimized by NVIDIA to run on next-gen GPUs
and can run efficiently on a single TPU host in Vertex AI.
So this quality-to-size ratio is amazing
because it'll outperform models more than twice its size.
We can't wait to see what you're going to build with it.
[APPLAUSE]
To wrap up, I want to share this inspiring story from India,
where developers have been using Gemma
and its unique tokenization to create Navarasa,
a set of instruction-tuned models
to expand access to 15 Indic languages.
This builds on our efforts to make information accessible
in more than 7,000 languages around the world.
Take a look.
[VIDEO PLAYBACK]
[MUSIC PLAYING]
SPEAKER: Language is a very interesting problem
to solve, actually, given India has a huge variety of languages
and it changes every five kilometers.
[CHATTER]
HARSH DHAND: When technology is developed
for a particular culture, it won't
be able to solve and understand the nuances of a country
like India.
One of Gemma's features is an incredibly powerful tokenizer,
which enables the model to use hundreds of thousands
of words, symbols, and characters
across so many alphabets and language systems.
This large vocabulary is critical to adapting Gemma
to power projects like Navarasa.
RAMSRI G: Navarasa is a model that's
trained for indic languages.
It's a fine-tuned model based on Google's Gemma.
We built Navarasa to make large language models culturally
rooted, where people can talk in their native language
and get the responses in their native language.
Our biggest dream is to build a model to include everyone
from all corners of India.
HARSH DHAND: Today, the language that you speak in
could be the tool and the technology
that you use for solving your real-world problems.
And that's the power of generative AI
that we want to bring to every corner of India
and the entire world.
[MUSIC PLAYING]
[APPLAUSE]
[END PLAYBACK]