[MUSIC PLAYING]
[APPLAUSE]
JAMES MANYIKA: Hi, everyone.
I'm James.
In addition to research, I lead a new area at Google
called Technology and Society.
Growing up in Zimbabwe, I could not
have imagined all the amazing and groundbreaking innovations
that have been presented on this stage today.
And while I feel it's important to celebrate
the incredible progress in AI and the immense potential
that it has for people in society
everywhere, we must also acknowledge
that it's an emerging technology that is still being developed,
and there's still so much more to do.
Earlier, you heard Sundar say that our approach to AI
must be both bold and responsible.
While there's a natural tension between the two,
we believe it's not only possible, but in fact, critical
to embrace that tension productively.
The only way to be truly bold in the long-term
is to be responsible from the start.
Our field-defining research is helping scientists
make bold advances in many scientific fields,
including medical breakthroughs.
Take for example, Google DeepMind's AlphaFold,
which can accurately predict the 3D
shapes of 200 million proteins that's
nearly all the cataloged proteins known to science.
AlphaFold gave us the equivalent of nearly 400 million years
of progress in just weeks.
[APPLAUSE]
So far, more than one million researchers around the world
have used AlphaFold's predictions,
including Feng Zhang's pioneering lab at the Broad
Institute of MIT and Harvard.
AUDIENCE: Whoo!
JAMES MANYIKA: Yeah.
In fact, in March this year, Zhang and his colleagues at MIT
announced that they'd used AlphaFold
to develop a novel molecular syringe, which could deliver
drugs to help improve the effectiveness of treatments
for diseases like cancer.
[APPLAUSE]
And while it's exhilarating to see
such bold and beneficial breakthroughs,
AI also has the potential to worsen existing societal
challenges like unfair bias, as well as pose new challenges
as it becomes more advanced and new users emerge.
That's why we believe it's imperative to take
a responsible approach to AI.
This work centers around our AI principles
that we first established in 2018.
These principles guide product development
and they help us assess every AI application.
They prompt questions like, will it be socially beneficial,
or, could it lead to harm in any way?
One area that is top of mind for us is misinformation.
Generative AI makes it easier than ever
to create new content, but it also
raises additional questions about its trustworthiness.
That's why we're developing and providing people with tools
to evaluate online information.
For example, have you come across a photo on a website
or one shared by a friend with very little context,
like this one at the moon landing,
and found yourself wondering, is this reliable?
I have, and I'm sure many of you have as well.
In the coming months, we're adding two new ways for people
to evaluate images, first, with our About This Image tool
in Google Search, you'll be able to see important information,
such as when and where similar images may have first appeared,
where else the image has been seen online,
including news, fact checking, and social sites,
all this providing you with helpful context
to determine if it's reliable.
Later this year, you'll also be able to use it
if you search for an image or screenshot using Google Lens,
or on your own websites in Chrome.
As we begin to roll out the generative image capabilities
like Sundar mentioned, we will ensure that every one of our AI
generated images has metadata, a markup in the original file
to give you context if you come across it
outside of our platforms.
Not only that, creators and publishers
will be able to add similar metadata,
so you'll be able to see a label in Images
in Google Search marking them as AI generated.
[APPLAUSE]
As we apply our AI principles, we also
start to see potential tensions when it comes
to being bold and responsible.
Here's an example, Universal Translator
is an experimental AI video dubbing service
that helps experts translate the speaker's voice while also
matching their lip movements.
Let me show you how it works with an online college
course created in partnership with Arizona State University.
[VIDEO PLAYBACK]
- What many college students don't realize
is that knowing when to ask for help
and then following through on using helpful resources
is actually a hallmark of becoming a productive adult.
[SPEAKING SPANISH]
JAMES MANYIKA: We use--
[APPLAUSE]
It's cool.
We use next generation translation models
to translate what the speaker is saying,
models to replicate the style and the tone,
and then match the speaker's lip movements,
then we bring it all together.
This is an enormous step forward for learning comprehension.
And we're seeing promising results of course completion
rates.
But there's an inherent tension here.
You can see how this can be incredibly beneficial,
but some of the same underlying technology
could be misused by bad actors to create deepfakes.
So we've built the service of guardrails
to help prevent misuse and to make it accessible only
to authorized partners.
[APPLAUSE]
And as Sundar mentioned, soon we'll
be integrating new innovations in watermarking into our latest
generative models to also help with the challenge
of misinformation.
Our AI principles also help guide us on what not to do.
For instance, years ago, we were the first major company
to decide not to make a general purpose facial recognition
API commercially available.
We felt there weren't adequate safeguards in place.
Another way we live up to our AI principles
is with innovations to tackle challenges
as they emerge, like reducing the risk of problematic outputs
that may be generated by our models.
We are one of the first in the industry to develop and launch
automated adversarial testing using large language
model technology.
We do this for queries like this,
to help uncover and reduce inaccurate outputs like the one
on the left and make them better like the one on the right.
We're doing this at a scale that's never been done before
at Google, significantly improving the speed, quality,
and coverage of testing, allowing
safety experts to focus on the most difficult cases.
And we're sharing these innovations with others.
For example, our perspective API originally created
to help publishers mitigate toxicity,
is now being used in large language models.
Academic researchers have used our perspective API
to create an industry evaluation standard.
And today, all significant large language models,
including those from OpenAI and Anthropic,
incorporate this standard to evaluate toxicity
generated by their own models.
Building AI--
[APPLAUSE]
Building AI responsibly must be a collective effort, involving
researchers, social scientists, industry experts, governments,
and everyday people, as well as creators and publishers.
Everyone benefits from a vibrant content ecosystem today
and in the future.
That's why we're getting feedback
and will be working with the web community on ways
to give publishers choice and control over their web content.
It's such an exciting time.
There's so much we can accomplish,
and so much we must get right together.