[MUSIC PLAYING]
RAJEN SHETH: Hello, everyone.
My name is Rajen Sheth.
And I lead product management
for AI and Industry Solutions
here at Google Cloud.
Thank you for joining us.
This year has been really
hard for all of us.
But it's been really
inspiring to see
how people and organizations
have been coming together
to help each other
during these times.
Now, as businesses start
to refocus on the future
and things start to improve, we
believe that many organizations
and industries will be able
to apply many of the lessons
that they learned
during these times
as they build their businesses.
We're already working
with many of you
and are looking forward to
working with all of you,
across industries, to reimagine
how we can successfully
use AI to generate
value for your business.
And we're in a very exciting
point in time for AI.
I've said in the past
that, over the next decade,
every organization will
be transformed with AI.
And today we're going to focus
on the big question of how.
From working with top
enterprises and customers
around the world,
we've learned that the
how actually comes
in two flavors.
First, in solving common
problems that businesses face.
These could be problems that
are unique to your industry
that we can solve by
integrating into key workflows.
Or they could be common
problems across industries,
for example, things like
providing great customer
service with your
contact centers
or keeping up with
document-heavy processes.
The second thing is by
equipping your teams
to build the best AI solutions
to solve your unique problems.
And so today I'm going
to share about how
AI is driving solutions
to common problems,
and then I'll introduce one of
our engineering directors, Ting
Liu, to discuss how AI can
solve your unique problems.
And throughout,
we're going to be
sharing example stories
of how customers
are realizing value today.
So for those of you who are
hands-on with the technology,
we're going to show you
some cool improvements
to our products and
ways that you can make
an impact with your companies.
For those of you who are trying
to understand how AI can better
be right for you,
we hope that we
can spark some ideas about ways
that you can be successful.
So before we get
into the how, I want
to talk a little bit about why.
Why should you be
thinking about AI?
And also, why work
with us on this?
So based on the
trends we're seeing,
it's important to build out
those AI solutions sooner
rather than later.
The organizations
that embrace AI will
have a competitive
advantage, and the window
for being ahead of the
pack, rather than getting
left behind, is closing.
McKinsey recently
published a study
saying that companies
that fully absorb
AI in their value-producing
workflows by 2025
will dominate the 2030
world economy with 120%
increase in cash flow growth.
Soon, every organization
will have an AI team,
just like we all built
web teams back in the 90s.
And that makes the
question of how we
equip you even more important.
So to truly unlock
the value of AI,
you first need to really
focus on the business problem,
and not the technology.
And it really helps to work
with companies that have already
engaged with customers
to deploy AI at scale,
so they can help you overcome
the roadblocks you're
going to face in the process.
That's why we're
working so hard on this.
Google Cloud AI can help
you with those roadblocks.
And we can partner
with you to solve those
identified business problems.
There are really three
reasons you should
be thinking about Google here.
The first is because of our
leadership, both in AI research
through Google
Research, and also
in practical applications of
AI through Google products.
Large enterprises
trust us as advisors
to their most critical
transformation projects.
In addition to the
innovations we're
making on the
software level, we're
actually innovating all the
way down to the heart of it.
For example, we just delivered
record-setting performance
in six out of eight
benchmarks in the latest round
of the MLPerf benchmark
released in July.
The second reason is that
we bring our heritage
and experience of
deploying AI in production
across Google, in products
like Google photos, Gmail,
and many more.
To help enterprises
realize the value in AI,
our teams and ecosystem support
the largest brands in the world
to infuse AI in production,
to build new revenue streams,
and to drive
operational efficiency.
The third reason is
trust from an enterprise,
and the trust that they are able
to have with their employees
and customers is a
top priority for us.
We're driving leadership
in responsible AI
through AI principles,
and recommended governance
practices are things that
we are practicing internally
and we're also working
with customers on.
Later, Ting is going to share
some examples of how customers
are using the platform
to identify and resolve
complex issues, things
like [INAUDIBLE] and models
during development and
the evaluation phase.
So as I mentioned,
the first flavor
of how organizations will
be transformed with AI
is through solutions
to common problems.
So let's get started with
our horizontal solutions
and how business
decision makers who
care about how AI can use them
to gain a competitive advantage
and optimize their
business operations.
The first solution that
I'm going to talk about
is Contact Center AI.
We've taken our world-class
conversational AI--
speech and natural
language-- and we've
applied it to the
contact center space
so that you can improve
customer experience
and operational efficiency
at the same time.
You can try it for yourself
with the multiple CCAI demos,
now available in our
Next OnAir demo area.
It first easily plugs into
your existing workflows.
And we have full integrations
with telephony providers
like Avaya, Cisco,
Genesys, Twilio, and Five9,
so that you can take advantage
of your existing investments
and relationships.
CCAI also helps expedite
customer requests
through virtual agents.
It helps assist live agents.
And it also offers
a layer of insights.
We're continuing
to invest in CCAI,
and today we're launching
Agent Assist for chat,
in addition to
voice interactions.
And we're also
announcing the ability
to create a custom voice that
represents your unique brand.
Now, Dialogflow
is the technology
that's the core of CCAI.
Today, I'm excited to
announce Dialogflow CX.
This new version of
Dialogflow offers customers
the best virtual agent
tool on the market.
It's optimized for enterprises
with large contact centers,
designed to support complex
conversational architectures,
and it's truly omnichannel.
So you can build this
once and deploy anywhere,
both in your contact
centers as well
as digital and social channels.
Dialogflow CX is truly
state-of-the-art.
And it'll make it
possible to give your end
customers an improved
experience with more
intuitive conversations.
So one customer that's really
redefined the possibilities
of AI-powered conversation using
Contact Center AI is Verizon.
I had a chance to sit down--
virtually, of course--
with Sai Vivek, who's
the executive director
of Customer Service
Technology from Verizon.
And I'm excited to share the
conversation with you now.
Thanks for being here today
to share your experience.
I'm looking forward
to our conversation.
So first of all, can
you tell me a little bit
about why you're considering
this transition of using
AI in your contact centers?
SAI VIVEK: Happy to be
here, and thanks, Rajen.
We at Verizon have
consistently focused
on improving
customer experience.
And to that end, I've
been leading the charge
on digital transformation
for a few years now.
We consider this
transition as a way
to improve our
customers' experience.
But we also realized that we
had to find an efficient way
to do that.
There are two areas
that we focused on--
the customer-facing
experiences on a digital chat
board and IVR, and the
agent-facing experience
with assist and
guidance capabilities.
We were looking to
Cloud AI to deliver
a more consistent conversational
experience for our customers
and better equip our agents
with the tools and information
to support our customers.
RAJEN SHETH: So what were
some of the key factors
in your decision to select
Google in particular
and Contact Center AI?
SAI VIVEK: Yeah.
We acted on the two
areas that I mentioned--
the customer-facing digital
experience and the agent
experiences.
On the customer-facing
experiences,
our journey started
several years ago
as we looked to enhance the
support for our customers
with the introduction
of a chat board.
As the board evolved, we
saw a huge opportunity
to take that experience
to the advice portal
and make it a great experience.
And also, for our agents,
we saw Agent Assist
was able to help our agents
help the customer's questions.
We evaluated several
solutions over the span of two
to three years, and
ultimately picked
Google for our Contact
Center transformation program
for a few key areas.
That was NLP,
synthetic voice that
can help in the advice portal,
and ability to automatically
create dialogues.
And lastly, we also
wanted to make sure
that, from a
scalability standpoint,
the partner that we chose is
able to scale to our needs.
And that's where we thought
Google can come in and help us.
We've started the journey and
we're seeing good results.
RAJEN SHETH: That's great.
And so what was the
implementation like?
SAI VIVEK: We had to
look at several factors.
And we actually came
up with, I would say,
a rather meticulous
plan around it.
Given the breadth of
our customers' needs,
we had to pick and choose
the right use cases.
And on the technology
side of the house,
we had to decide which
experiences have systems
already tied to it, like do we
need new APIs to be created,
or if we can use existing APIs.
So that was a big decision,
and complex decisions
had to be made.
When it came to measurements,
we had very stringent
analytic requirements.
And lastly, business
continued to evolve,
which meant we had to
continuously adjust the plan.
It was almost, like they
say, building the engine
while running it as well.
RAJEN SHETH: That makes sense.
So what kind of results
have you seen so far
after implementing CCAI?
SAI VIVEK: We're in the
process of rolling it out
to a wider scale.
But we onboarded a
few use cases already,
and early results are promising.
On the digital bot, the
auto-dialogue creation engine
is able to create five times
the depths of the conversation.
So it's a lot more human-like.
And as a result
of that, customers
like engaging with
our bot a lot better.
On the Agent Assist side,
about 30% of the agents
have started using the tool.
And we're seeing almost 75%
of those agents giving us
very high rating
for the experience.
I mentioned these
are early days,
but we're looking forward
to more improvement
in all our KPIs.
RAJEN SHETH: So now, as we
look to the future, what's
your overall vision for
customer experience?
SAI VIVEK: We want to be
sure that the customers have
the best possible experience.
We can continue to
improve and iterate
on how customers and agents
can reach the information they
need quickly and easily.
To that end, we are
exploring additional ways
our assistant can enhance
customer-agent interactions
and bring the convergence of
physical and digital channels
together.
Ultimately, we
want our customers
to have the best
experience and what
they call ambient experiences.
And that's our goal.
And that's what we're
working on at Verizon.
RAJEN SHETH: Well, thanks so
much for your time today, Sai.
I really appreciate
the conversation.
SAI VIVEK: Thanks for
the opportunity, Rajen.
Appreciate it.
RAJEN SHETH: It's so great
to hear about the value
that Verizon is seeing.
Earlier this year,
we had a chance
to use Contact Center
AI to help organizations
that are overwhelmed
with the influx of demand
to their contact centers.
When we launched our Rapid
Response Virtual Agent,
we made it possible to
quickly build and implement
a customized Contact
Center AI Virtual
Agent to respond to
the common questions
that people had due to
COVID-19 over chat, voice,
and social channels.
So to learn more about
CCAI, check out the CCAI
State of the Union breakout.
The next horizontal solution
I'm going to talk about
is Document AI.
Document AI makes it
possible to unlock insights
from your documents
with machine learning
and use these to
their fullest degree.
By extracting structured
data from these unstructured
documents, customers are able to
make better business decisions,
whether it's things like
processing invoices more
quickly and accurately,
knowing each of your customers,
or reducing things like
mortgage processing
time and many, many
other use cases.
One company that has reimagined
their document strategy
by leveraging Document AI is
the industry-leading mortgage
services provider, Mr. Cooper.
They're challenged to manage
domain-specific mortgage
document types, and
they've classified
over 100 million pages.
They've trained over 130
critical mortgage document
labels, and they've
achieved over 92% accuracy
for these classifications.
Using Doc AI, they
can trust the content
will be accurately classified.
Earlier this year, we
used the power of Doc AI
to help the response
to COVID-19.
We developed the PPP
Lending AI Solution
to help lenders
accelerate and automate
processing loan applications
for the Paycheck Protection
Program.
The Document AI PPP Parser API
made it possible for lenders
to help with the large intake
of volume of PPP loan requests
by using AI to extract critical
information from loan documents
submitted by applicants.
We also released some exciting
updates for Document AI
this year, ranging from Form
Parser, which is a new way
to extract text as well
as spatial structures
from documents, to the
Invoice Parser, which
is a specialized API that's
highly accurate in processing
invoices.
To learn more about Document AI,
take a look at the Document AI
overview breakout, or try
the "Unlocking Insights
from Document AI" demo
on the Next OnAir site,
and also the demo for
Document AI on our website.
These horizontal solutions
are solving problems
that are common
across industries,
but sometimes needs can differ.
And we need to consider
industry-specific solutions.
As Thomas mentioned in
his opening keynote,
we're investing in specialized
solutions across industries
and have worked with
customers like Macy's, Fox,
and Cardinal Health.
In addition, we expect that
the movement to e-commerce
will continue to accelerate,
with a consistent omnichannel
experience moving from a
nice-to-have to a must-have.
Our customers are struggling
to optimize the merchandising
supply chain, as
well as the product
discovery-to-drive conversion.
We're building out solutions to
address these needs, including
things like demand
forecasting, product
search, and Recommendations AI.
In July, we announced that
Recommendations AI is now
available in public beta.
It's already being
used by companies
like Sephora and Hanes.
In health care, COVID-19
has forced institutions
to rethink how they can provide
high-quality, patient-centered
care outside of
traditional care settings.
We've seen, and we
expect to continue
to see, a huge uptake in the
use of remote collaboration
and telemedicine tools.
COVID-19 has further
highlighted the need
for better interoperability
of health care data
to enable remote care,
as well as the ability
to plan forward how healthcare
organizations need to respond.
We formed a strategic
partnership with Mayo Clinic,
combining our Cloud
and AI capabilities
with Mayo's clinical expertise
to jointly develop solutions
to transform healthcare.
In financial services,
we see a strong need
to streamline processes,
particularly those
with complex document
management processes,
such as lending, for example.
Today, we're excited to announce
two new solutions designed
to make this easier
for financial services
organizations.
The first is Lending DocAI,
a solution built specifically
to help financial institutions
process mortgages more quickly
and efficiently.
It automates many of
the routine document
reviews so people can focus
on the more complex decisions.
The second is Procure2Pay DocAI.
DocAI for Procure2Pay
helps enterprises
automate one of their
highest-volume and
highest-value business process,
which is the procurement cycle.
We provide a group of
AI-powered parsers,
starting with invoices
and receipts, that
take documents in a
variety of formats
and return cleanly
structured data.
Another area that
companies like HSBC
are starting to look at
to transform and modernize
is their end
customers' experience,
and also helping
better manage risk.
And we're working
with them on solutions
like AML, Anti-Money Laundering,
and KYC, Know Your Customer.
In media and
entertainment and gaming,
we see customers with
a large amount of data,
and especially things like
video and media files.
Companies like Fox
Sports are starting
to use and make use of
massive amounts of content.
And with their
video content, they
want to make it
possible to search
and also do interesting
work with that content.
In manufacturing,
we expect to see
more distributed
manufacturing lines,
and to make it
safer for workers,
and also more efficient
for organizations.
Companies like [INAUDIBLE]
are using our AI in defect
detection, and we're
creating offerings,
like adaptive controls,
to drive energy efficiency
and optimization in
buildings and factories.
Now that I've covered
how AI can solve
common problems that your
business is faced with,
both problems that we
see across verticals
and those that are
unique to your industry,
I'd like to pass it
over to Ting Liu, one
of our amazing
engineering directors,
to talk about equipping
with your teams
to build the best AI
solutions and to solve
your unique problems.
TING LIU: Thanks, Rajen.
As Rajen shared, we recognize
that there are unique business
problems that you need to solve.
And we are invested in bringing
you the best of Google AI
technology so you can
build solutions to solve
those problems as well.
Customers tell us that
their teams working on AI
are composed of various skill
sets, from ML engineers,
to data scientists,
to developers.
So we have new tools for
everyone on your team.
I will highlight
a few ways that we
enable these different groups to
build out unique AI solutions.
First, ML engineers.
Developing ML into production
involves more than just
building the model.
It involves data collection,
feature extraction,
resource management, model
versioning, monitoring,
and much more.
The set of operations that are
required to deploy and manage
production models is
referred to as "ML Ops,"
and it can easily become the
bottleneck for an organization
as they build more models.
We enable organizations
to scale and streamline
their ML development in a
repeatable and reproducible
manner with our AI platform.
Today, we are announcing new
capabilities and services
within our AI platform
that will simplify ML Ops.
Starting with AI
Platform Pipelines--
we announced a hosted
offering for building,
managing ML pipelines on AI
platform earlier this year.
We are pleased to now share that
a fully managed service for ML
pipelines will be available
by October this year.
With the new managed
service, users
can be with ML pipelines
using PFX pre-built components
and templates that significantly
reduce the effort required
to deploy models.
Today, we offer a continuous
evaluation service
in our platform that samples
prediction input and output
from deployed ML models.
And it helps users assign
human reviewers to provide
[? ground-use ?] labels.
We are also pleased to announce
a continuous monitoring
service that will monitor the
model performance in production
to let you know if
it is going stale
or if there are any outliers,
skews, or concept drifts.
This will simplify the
management of models at scale.
And it will will be
available to our users
by the end of this year.
The next announcement
is the foundation
of all these new services,
our new ML metadata management
service.
This lets AI teams track
all the important artifacts
and experiments
they run, providing
a curated ladder of actions
and detailed model lineage.
This will enable users to
determine model provenance
for any model trained
on AI platform
for debugging, audits,
or collaboration.
Our ML metadata service
will be available in preview
by the end of September.
Our customers have told us how
important ML Ops is to them,
and we will continue to
invest in ML Ops capabilities,
such making it easier for
customers to organize, share,
and serve ML features at scale.
Second, we are improving
agility of AI data scientists
and developers,
giving them one place
to go with the AI platform.
Today, we are
excited to announce
that, by the end of September,
the AI platform will
include AutoML as an integrated
function in the workflow.
This combines the best of
non-code and code-based options
to build custom ML models
faster with high quality.
To hear a deep
dive on this, take
a look at the "Creating Value
with AI Platform" breakout.
We also offer some tools
designed for data scientists
to give them the resources to
achieve a competitive edge.
One example is Notebooks,
which is now GA.
And we run the largest
number of Notebook instances
in the world.
Notebooks now supports important
enterprise security features,
like VPC service controls,
Access Transparency,
and the Cloud IAM, and has
a smart analytics framework
to run Spark jobs and manage
Hadoop clusters on Dataproc.
We have also integrated Kaggle
kernels to our Notebooks,
so Kaggle users can now
seamlessly move their Notebook
instance to AI platform for more
compute and storage options.
Third, we continue to hear
from customers and analysts
that it can be a challenge
to hire AI talent.
So we offer developers
with limited AI expertise
great tools through
our platform,
such as pre-trained APIs
and AutoML technology,
to enable an organization's
existing team's non-AI experts
to build AI applications
with high quality
and realize the value of AI.
Both customers and analysts
have highlighted our leadership
in state-of-the-art models,
and we are continuing to invest
in these models.
For instance, we
recently upgraded
our OCR model, which achieves
high-quality recognition,
as well as the AutoML
image classification model.
Customers are doing amazing
things with these developer
tools, from making maintenance
of their wind turbines
safer, to automating the
evaluation of equipment,
to translating highly
domain-specific
financial analysis as
quickly as the markets move.
Many organizations across
regulated industries and those
seeking hybrid and
multi-pronged solutions
have expressed interest in
using our AI technologies
on premises.
Last week, we announced that
our organizations can now
use and host to enable STT
capabilities on premises,
bringing our AI technology to
you in a more flexible way.
As organizations increasingly
serve global users,
it becomes imperative to
overcome language barriers.
And we at Google
have a long history
of empowering users across the
world through our technologies.
[INAUDIBLE] organizations
use our language and speech
services to connect meaningfully
with global users at scale
with ease.
Earlier this year, we announced
the Media Translation API,
which organizations can
use to translate audio
from streaming
input in real time
to provide a seamless
experience for users.
One customer that's
already started
using this API is OnePlus,
a large Chinese smartphone
manufacturer who is using it
to provide real-time streaming
translation for video chat
to ensure their customers
feel effortlessly connected.
One company that has
been pulling all of this
together and using AI to
build a more curated shopping
experience for their
customers is Etsy.
Their marketplace includes
more than 65 million
seller-generated listings.
They are using AI to build
sophisticated workflows
to help buyers find exactly
what they are searching for
and to deliver enriched
recommendations that
better reflect their buyers'
unique styles and tastes.
Lastly, across our
portfolio, we're
committed to our investment
in Explainable AI
and have been releasing things
like feature attributions
on AI platform prediction,
Notebooks, and other ML tables.
In November last year, we
launched Explainable AI
in model cars.
This April, we launched
a new image attribution
method called XRAI.
XRAI is a new way of
displaying attributions
that highlights which
regions of an image
most impacted the model, instead
of just individual pixels.
This means the AI
platform explanations now
offer two methods for getting
attributions of image models--
integrated gradients and XRAI.
We will continue to
invest in Explainable AI
so that you can trust the
platform and the solutions
you build.
We're committed to
continuing to build
the best tools for all our users
to solve your unique problems.
Back to you, Rajen.
RAJEN SHETH: Thank you, Ting.
You know, I remember
10 years ago,
brainstorming with
some of my colleagues
about what computing
would be like in 2020
at the beginning of
this next decade.
And we used to think
about all kinds
of crazy, futuristic ideas.
Like, imagine if a computer
could answer questions for you
and help you find what you need.
Imagine if a
computer can help you
do things that are, right
now, manual and repetitive.
Imagine if a computer can
use all the information
that you have to help you
make a great decision.
At that time, these all
seemed like science fiction.
Now this is a reality.
And we've seen this in action
with many of the interfaces
we work with and that
you've heard about today.
But this is only the beginning
of the transformation.
We're looking forward to helping
you get value out of AI today.
We also want to partner
with you to imagine
what your business
looks like next year,
in five years, and 10 years.
Every organization will
be transformed by AI
in the next 10 years.
And the question that we
all need to answer is, how?
And how can we help?
