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RAJEN SHETH: So it turns out that 90% of data
within a typical enterprise is unstructured information,
so things like documents, or images, or videos.
Over the last five years, deep learning
has advanced tremendously to make it
so that it's possible to be able to make sense out of that data.
But deep learning is quite hard to use.
There are only tens of thousands of people
in the world that are able to build a deep learning
model from scratch, but there are 21 million developers.
So what can we do to bring access
to those 21 million developers for all of this technology?
Well, we have products in three different areas
that make this possible.
So, first of all, we have our AI platform,
which makes it easy for you to build a model
and then scale it across Google Cloud.
Second, we have what we call our AI Building Blocks.
And this is bringing the best of Google's AI
to you, so areas like speech recognition, image recognition,
text and translate.
Our best models are now things that you
can embed within your products.
And then, third, we have our AI solutions
to solve very specific business problems.
Now, AI is a mass customization problem.
And what I mean by that is two customers that
have the same problem will likely
require different models trained on different data,
and that's not easy to do.
So this is why AutoML is so crucial, because AutoML
lets you easily create some of the most
advanced models in the world without machine learning
expertise.
Now we're happy that over the course of the last couple
of years we now have over 14,000 paying
customers of our AI products.
On top of that, we've had 18,000 customers
sign up for interest for AutoML since last January,
so when we first launched our alpha.
In the 24 hours since yesterday's keynote,
we've had 1,400 additional customers
that are actively using AutoML just in that time period.
And so we're very happy about that.
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And our customer base includes all of these big customers
that you see here and more.
So to show you, though, how this can impact the customer,
I'd like to welcome up Laura from Chevron who's
going to tell you more about how Chevron
has been able to use AutoML.
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[APPLAUSE]
LAURA BANDURA: Hello, everyone.
I'm excited to be here today to talk about how we've been
using Google's AI technologies.
At Chevron, we were challenged with something
I imagine many of you can relate to,
finding the information that you need when you need it.
What I'm going to show you is how
we used AutoML Vision to address this challenge.
You're looking at a map showing some
of the millions of documents that we've
created over a period of decades,
mapped to the geographic areas that they describe.
These documents contain a mix of text,
tables, and images, which are scattered across many file
systems.
As you can see on the map, we've invested in the knowledge
in our documents.
When we're exploring an area, accessing information
can be a needle in a haystack type of challenge so much so
that sometimes we're forced to consider redoing the work
instead of finding the information.
As an example, I'll show you how we used AutoML Vision to make
it possible for an explorationist
to evaluate a new opportunity.
Let's say we're looking at a new opportunity
in the Gulf of Mexico.
Clearly, there are too many documents here
for us to search manually.
We can narrow the search by adding a search
term for geologic map.
As we do this, this reduces the number of hits
very dramatically.
The first hit is a document that describes
the geologic providence of the Gulf of Mexico.
Looking into this stack, we see images on the right
here that represent geologic maps,
so we know that we're headed in the right direction.
What happened here is that we ran the images
from the documents against an AutoML Vision
model that was trained to recognize over
60 geologic labels.
We then used the Natural Language APIs
to classify the information and make it possible for users
to search for them.
We can also view the document type on the right-hand side
and confirm that it's a document that we
can use for this project.
As you can see, using AutoML, we were
able to create a solution that enabled
us to do a custom search and find information
that would otherwise be very difficult for us to access.
AutoML Vision met our unique requirements
in a very short time frame.
We needed a custom solution.
We can now find our documents in seconds instead of weeks,
giving us the freedom to make well-informed and timely
decisions and transforming the way business decisions are
made at Chevron.
Thank you.
[APPLAUSE]
RAJEN SHETH: Thank you, Laura.
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