Hello, everyone.

Hey.

Good morning.

Thanks for coming here.

It's probably been a long week.

The last couple of days have been crazy here,

and the traffic's not been fun either.

So hopefully it's been working all well for you guys.

Some of you have made your way from a different parts

of the country, many different areas.

But I hope it's been worth it.

And there's a lot of interesting things that we've been doing.

So we want to talk a little bit about some of them.

You've heard our keynotes and the great products we have,

et cetera.

Today I'm going to talk a little bit about machine learning,

right?

The title says machine learning is not

the future, which is kind of weird

given that we're talking about machine learning in everything

we're doing here.

But the point that I wanted to bring across here

is it's not really the future.

It's part of everything we have today

as you're seeing without products as all the things

that we're talking about.

We're using machine learning today

in everything we're doing.

And I want to talk about some of that

and tell you how you can do that too.

But first, let's start with this all started.

So and this goes back to 1997.

I don't know how many of you remember this Tik-Tok

from "The Wizard of Oz."

He's one of the early versions of robots in modern times,

really.

Although there are references to automatons going back

to the 5th Century B.C. And stuff,

even the Egyptians and others way back when.

But in the modern times, this is probably

one of the earliest references to something

that was close to a robot.

Any of you familiar with Tik-Tok?

How many of you are familiar with Tik-Tok?

Not too many.

Let me tell you a little bit about him.

So this one is-- in fact, the term robot

wasn't even coined back when Tik-Tok

was created by Frank Baum.

And what this robot would do is it had a winding thing.

And you needed to wind the robot.

And it would run.

And it could do pretty much what a human could do but not

really be alive.

And it was a great reference to things

that we've always wanted to do.

And so a lot of this AI and robotics

and a lot of these things have been part of science fiction

for a very, very long time.

In fact, a lot of what we do today in science

has been driven by people, literally authors, et cetera,

are able to really build up and think about what

the future might be like.

This is one example, but there are many, many more.

For example, if some of you have read Asimov,

he has a bunch of books.

And he has this whole robotics cities,

again way back in the '30s where he talks about robots

and how they might be like and the kind

of things that come with it.

And that was very interesting.

A lot of people over the years have

been inspired by these science fiction books and movies

to do a lot of interesting things.

Another example of that is back in the '70s

from "The Hitchhikers Guide to the Galaxy"

there is this robot called Marvin,

which is this depressed-- it's always depressing.

He's always talking about these things he's not happy about.

He's just too smart for all the things happening it.

And it's really a great example of what

people have been thinking about what's

going to happen in the future.

And now going to another example,

"Star Trek," another thing.

The example that I put here, you can

go to Data in "Star Trek" who talks about-- this

is a poem he composes, which I thought was pretty funny,

and how people have been thinking about AIs

and what they would do and what they

would be doing in the future.

In this case, again, it's like a robot,

like a humanoid, somebody who can do a lot of things,

but is not quite human.

Then coming to more recent times, more closer to 2001,

if you've seen Steven Spielberg's movie "AI" called

"AI" itself, they have these mechanical robots

that can really do-- that look like humans, that

can do all kinds of things.

But they don't feel.

And then they build this little kid David,

who can actually feel and love as well,

and really changes how the perception is

and how they think about AI or what it means

to the people around them.

And then much more recently there's

this movie called "Her" in 2013, which

talks about AI without the shape, without the robotics,

and all of that.

But AI is just that live within your computer as an OS,

essentially, that can interact with you.

That's your assistant, but much more than that.

It actually feels as well.

It understands things, et cetera.

So all these are great.

There are so many different things

that people have been talking about,

people have been thinking about.

But these are still science fiction.

This is not really what machine learning is about today.

This has always been the future.

It's still the future.

Maybe at some point it will be a reality.

But that's not where we are today.

That's not what we're talking about.

But there are some real things that we can do today.

And those are some of the things that I'm

going to talk about today.

So we've actually made over the last few years

real progress in terms of all the different things that we

can do with AI with machine learning,

use all the products that you see around you.

And I'm going to talk a little bit about those.

There's so much in there that has

benefited from machine learning from what we call AI as well.

And so this is some of the smallest of products, really,

at Google that we made that used machine learning in some ways.

But at Google, anytime we think of a product

there's, of course, programming.

And you build it, and you do all sorts of things with it.

But machine learning is an integral part of everything

we do in building that, because we want these products to be

really smart to give you the right things,

to not just follow your actions, but really

give you the right things when you want them as you want them.

And I'll go over some examples of these in later slides

as well.

So before I go into other things,

let's just go a little bit into what deep learning

and machine learning is about.

And I'm just quickly going to give you some examples

on a website that we have.

So part of this slide is going to talk about TensorFlow.

And recently we put the site up called

playground.tensorflow.org that allows

you to play with neural nets that allows you to really do

different kinds of things, and allows you to really understand

how these networks work, how machine learning works,

and be able to play with some of those problems.

So I'm going to start with a very, very basic problem

classification.

The goal in this case is there are two kinds of clients.

I just want to classify that it's ARB, in this case

the orange or the blue ones.

And I'm going to use the very simplest case.

It's a very simple linear classification,

if any of you guys know what that is.

But the idea is you have some inputs.

In this case, the x-axis and the y-axis are inputs.

And based on those two inputs you

want to decide if it's a blue one or an orange one.

And so what this model is going to learn

is some parameters to figure out-- OK.

If I get some point, I get the an x1 value and a x2 value,

how do I decide that?

It's some mathematical computation

that it needs to do.

So in this case, it does that iteratively.

Let's just quickly run this.

I've just set it up as a linear model.

So lets just run it very quick.

And as you see here, its looking at x1 and x2.

And very, very quickly it figures out

how to separate those out.

It just draw a line in the middle.

And its just optimizing what that line looks

like, how to separate these out very simply.

So this was a very easy problem.

This is typically not how the real problems are,

but it's a great place to start.

So let's go to a slightly more complex problem now.

So let's go to this one here where

you still have those two kinds of points,

but now they're clustered differently.

So there's one in the center-- is a circle,

and the rest are around it.

So can we actually solve this using the same kind of methods?

Can we actually classify using the same thing?

If I run this, do you think it would work?

Any guesses?

Let's try it out.

So it's trying to separate this out.

It's basically trying to draw a line.

But it's lost.

It's really not making a progress

if you see what's happening here.

So there are a few things you can do about this.

One, in terms of classification, again,

going a little bit into machine learning into the details,

you can add more features, more kinds of things about the input

data that helped the model understand

or how to separate them.

So if you guys remember your math back from high school

or college, in this particular case,

given how these clusters are structured,

if you looked at a couple more features

from based on the inputs, like in this case,

x1 squared and x2 squared, you can probably

use those to separate this out better.

So let's try this.

Let's add these two and see what happens.

And taking those two values, now it

knows how to separate those and clearly

separate it out into two separate, different classes.

So this works.

This great great, right?

This is what happens or used to happen

in machine learning very often for every single thing

that you do.

If you take a bunch of inputs, you want to solve some problem.

It's typically the inputs themselves

that are not in the same kind of mode

that you need to solve the problem.

And what you do is called feature generation

where you are combining or crossing

those features in different ways to really solve your problem.

But there's a lot of work you need

to do in figuring out the right features that make sense.

In this case, it was a simple problem.

So we knew x squared, x1 squared or x2 squared would work.

But in some cases, the problem is not as simple

and you really need to do a little bit more.

So for those kind of cases, we have

something called deep learning which

seems to work really well.