CASSIE KOZYRKOV: So, have any of you
had that moment where you realize
everyone is using some word, and you
have no idea what that means but you're too
afraid to ask at this point?
What even is machine learning?
Well, it's an approach to making lots of small decisions
with data, something involving algorithmically finding
patterns in data.
But let me just put it bluntly, so that we can all get it.
It's a thing labeler, essentially.
That's what machine learning is.
It's just a thing labeler.
So why on earth should you be excited about thing labeling?
Well, here's my pet, Huxley, and you've just
taken in some pretty complicated data through your senses,
and you just know that that's a cat.
And if I show you a different image, you're not fooled--
you still know that that label is cat.
Your brain just does this.
You don't even know how your brain does it.
Now, if we wanted to get a computer
to perform this labeling task for us,
if we went the traditional way--
you know this, programmers-- we would
communicate with the universe in some way,
think really deeply about the problem,
and come up with a model.
It's a model.
It's just a recipe.
It's a set of instructions that the computer
has to follow to get from the image to the label.
And we would have to hand-craft that.
Now, think about what this recipe would
have to be for the computer to correctly detect that there
is a cat in an image.
I mean, think about what you, your brain,
actually did with those pixels.
Can you express that?
Do you know what your brain is doing?
Or have you just had the benefit of eons of evolution,
and it just kind of figures it out for you?
Now, that recipe is really hard to hand-craft.
Wouldn't it be better if you could just say to the machine,
here, look at a bunch of examples of cats,
look at a bunch of examples of not cats,
and just figure it out yourself.
That is what machine learning is all about.
It is a completely different programming paradigm.
Now instead of giving explicit instructions, you
program with examples, with data, and let
that machine learning algorithm figure it out
and stitch that code that goes in between for you.
So why should you actually be excited by this thing labeling?
Well, engineers, you know we like
to get computers to do stuff for us?
There's a whole class of tasks where we just cannot express
the instructions.
They're ineffable.
AI and machine learning are about automating the ineffable.
This is a fundamental leap in human progress.
Now you can get computers to do tasks where you cannot possibly
express the instructions.
This is so exciting, and seems futuristic and difficult,
right?
And when you think about machine learning,
the one thing I most want you to think about is microwaves.
Who here in the audience knows how
a microwave oven works well enough to build
a new one from scratch?
Raise hands.
Who can build a new one?
Do I see any takers?
Any takers?
No hands.
Yeah, me neither.
Who here has never used one to reheat food?
You all have.
Even though you have no idea how it works,
you're still happy to use one, right?
And anyone here feel like you would have no idea how
to get hold of one, I don't know, like maybe
at Kohl's, if you needed one?
Now, you just told me you have no idea how
it works, so how could you possibly trust this microwave?
Well, you're not going to trust it by reading the wiring
diagram, are you?
You're going to trust it by checking that it actually
does work, by having a good idea of what you want
it to do for you, and then tasting what comes out
on the other side.
And that is exactly what you'll do with machine learning.
And this is a perfect analogy for machine learning.
Now, what they don't tell you about machine learning--
when we use that term, there's actually
two machine learnings, not one.
Two completely different disciplines
that are as different as building microwaves
from scratch and innovating in the kitchen,
and you need a completely different skill sets for these.
If you want to be a machine learning researcher,
you are building general purpose tools for other people to use.
And so the bad news there is that there is quite
a lot of education that you need to get,
takes a little while to ramp up, because how on earth
are you going to build a better microwave than the one that
exists already--
there's some pretty sophisticated stuff out there--
if you have no idea how the current one works?
So that's the bad news.
It takes a long time to become a machine learning researcher.
The good news, though, is if that is your cup of tea,
we at Google have a large machining research shop
where we make these tools for you that
are all powered by open source.
So we give you those wiring diagrams as a springboard
so that you can go and build a better microwave than what
we have using our blueprints.
And TensorFlow, a really popular machine learning project,
started at Google before we open sourced it
because we believe that the community as a whole
is so much more innovative than just any one of us alone.
But let's face it--
most of you are not here to build general purpose
microwaves.
You just want to get cooking already, right?
You just want to use these things to solve business
problems, and so you are in the completely other discipline--
applied machine learning.
Now, for that, you do not need a PhD.
You don't need to know how back propagation works
in neural networks any more than a chef needs to know
how a microwave is wired.
Instead, what you need is a really good kitchen to play in,
and we at Google provide that for you.
Let me show you some of the things that we have,
and what you're going to need kind of depends
on what you're cooking.
If you're cooking with usual ingredients-- data--
and you're making usual dishes--
labels, predictions.
So say you're making pizza.
Please don't go reinvent the concept of pizza from scratch.
There's already recipes out there,
and you can just grab those recipes and start making pizza.
And that is what our cloud APIs are all about.
These are recipes that we've already built, that you can
just pick up and start using.
You can just plug them into your apps
to make those intelligent applications.
But say you're cooking with a little twist.
Now it's gluten free, vegan pizza.
Well, you still don't need to-- yeah.
You still don't need to start entirely from scratch.
It's still pizza.
You can just adjust what already exists out there.
And so what we suggest for you here is try Cloud AutoML.
Sure, maybe we haven't trained our vision system
on the clothing type that your Kohl's customers are wearing.
But no problem-- just feed in your images with your labels
into Cloud AutoML and you'll be good to go.
Only a small adjustment necessary.
Now, say you're truly innovating.
You have your own completely unique ingredients.
You're doing something pretty different in the kitchen.
I don't know, maybe now you're making an edible sock
that tastes like pizza.
Well, machine learning and innovation
are in Google's core DNA.
In fact, I can't think of a single one of our consumer
products that doesn't have machine
learning in it somewhere.
And so if you are innovating in the kitchen,
if you're building truly new recipes, what we have for you
is Cloud Machine Learning Engine.
This is access to exactly the same infrastructure that Google
uses to train our models.
Incredible.
So you could, if you wanted to, after this session,
grab a laptop, open it up, and test
drive our shiny, gleaming kitchen
full of those appliances ready to go.
You could feel the thrill of a data center
and all those cutting edge algorithms rising up
to do your bidding.
So solving business problems with machine learning--
applied machine learning-- is far easier
than what most people think.
What you don't realize is that all
those courses in university--
those are all about how to build the microwave, not how
to just use it to get cooking.
And when I hear people say, oh, I couldn't possibly
get started with machine learning until I take
a course in it, or, goodness, a whole degree,
I can't help but imagine that same person saying,
I couldn't possibly use a microwave
until I've built one myself.
You can just get cooking.
It's not the lack of degree, or course,
or knowledge that's holding you back.
What's much, much more important than that,
what you need to get started is creativity.
Figure out what you're going to cook because all those tools
are already there waiting for you to use them.
Imagine gleaming kitchens just waiting
for you to come play in them.
But since this is a thing labeler,
you need to start by thinking, what
on earth do you want labeled?
And see how creative you can get with that.
You might imagine the label being maybe the wave form that
sounds like a voice that could make a hairdresser's
appointment for you.
Or you could imagine a few other cases,
like maybe you are a loving son, and your aging parents sort
vegetables by hand, and you want to save them
the trouble of having to do that on their farm.
So maybe you're thinking about building a system that
will label the vegetables and sort it automatically
so your parents don't have to do it.
Or maybe you're worried about the ingredients that
go into baby food, and you think it would be great
if you could automatically check whether the ingredient is safe
or spoiled before it goes in.
Or maybe you're into the idea of a huge bank
with trillions of dollars in assets countering fraud
and money laundering automatically
across millions of accounts.
If you imagined that world, you imagined this world.
Well, these are all real examples
of things that have been done on Google Cloud Platform.
The first is a real family of cucumber farmers,
the second is a Japanese manufacturer of high quality
baby food products, Kewpie, and the third
is HSBC, who uses Google Cloud to run a better business
and catch the bad guys across 27 million customers.
Pretty different chefs, completely different
applications.
Why is Google Cloud Platform the right choice for all of them?
Well, think about what you might want in a kitchen.
You'd want good engineering.
So you probably want to think about getting a partner whose
quality you trust.
But think more long-term than that.
If you believe in your own infinite creativity,
if you believe in the future, you
would be crazy to let yourself get locked into a kitchen
where the purveyor sets it up one time for you
and then controls what you can and can't have in there,
and every appliance has its own proprietary little socket
that you are not allowed to tamper with.
And if your business changes, then you
have to rebuild and rewire the whole thing.
Imagine, instead, the dream kitchen that
becomes what you need it to be instantly.
Complete, universal wiring, open interfaces,
the best of everything, all the modern appliances
always available, and you can plug them in with no fuss
because that wiring's universal.
That, my friends, is the power of open source,
and that is what Google and Red Hat together believe in.
We don't want to see your creativity squashed by what
seemed like a good idea yesterday.
So we have built for you an incredible future-proof machine
learning kitchen.
This kitchen can expand, contract as you need it.
You can plug in whatever you like.
All the most modern appliances are there and compatible
as you need them, fully customizable.
Because none of us can tell what innovations
the future will bring, and we don't
want you to have to bet on the direction of progress and lock
yourselves in.
We want you to be free to be as creative as all of us
can be together.
So I hope that you're excited to dive in.
This machine learning kitchen is there,
ready, waiting for you to get started, start playing in it.
Your machine learning adventure awaits.
Thanks so much.