My name is Damien.

And I feel very lucky today, because two great artists,

Cyril Diagne and Mario Klingemann

will join me on stage in a few minutes

so you can see what they do when they use machine learning.

So if you want to go to the bathroom or text,

please do it while I'm doing the intro.

But when they are there, this is a really [INAUDIBLE].

So I'm working for the Cultural Institute in Paris.

And the mission of the Cultural Institute

is to help to a museum, an institution,

to digitalize and to shared their culture, their assets

online.

We are working with more than 1,000 museums.

And it means that if you want to discover

a new museum every week, it will take you 20 years to do so.

What we do in the Cultural Institute is this app.

It's named the Google Arts and Culture App.

It's really a beautiful app.

And if you have not downloaded yet, you should do.

There are a ton of really incredible features.

And one of my favorites is named Gigapixel.

Gigapixel is done using the art camera.

And the art camera is able to catch

every detail in a painting-- so every crack, every blue stuff,

you can see them in the app.

You can zoom in very deeply in the app, to see,

for instance, "Starry Night."

But I'm not working on this app.

I'm working in a space named The Lab.

It's in the middle of Paris.

It's a beautiful space.

I feel lucky every day when I go there.

And it's a space dedicated for creativity.

And just as a fun fact, it's where the Cardboard is born.

David Coz and I use a laser cutter

there to create the very first Cardboard-- the one that was

unveiled at I/O two years ago.

That's what I have to show today.

And last year, we also worked at the lab,

for instance, on this picture.

You can see some early prototype of the Cardboard

that was unveiled at I/O Google last year.

But here for the Cardboard today,

even if I have still a strange relationship with the VR team,

I also have a small team in Paris that's is named CILEx.

It stands for Cultural Institute Experiment Team.

And what we do, we do experiments

with creative [INAUDIBLE] and artists.

We are very passionate about three different axes.

We try to engage more people to enjoy culture.

So we try to find fun ways for people to watch more paintings.

We try to find also a new way to organize information.

So our user can have a journey in our database,

and [INAUDIBLE] can learn something out of it.

And obviously, because we have seven million assets

in the database, we try to analyze

them to discover new insights.

So this talk is about machine learning, obviously.

And just take 30 seconds to remind of the definition.

Just to make things simple, let's imagine

that you are writing an algorithm

to check if a picture is a cat picture.

You can do it yourself, by trying

to analyze all the pixels one by one, but obviously,

it's difficult. Or what you can do is, using machine learning,

having an algorithm that will learn by itself

what are the good features to check

if a picture is a cat picture.

So the key, I think, is this is happening now.

Machine learning is not the future.

Machine learning is something that everybody in this audience

can do, can try.

If you know how to code, you can try machine learning.

For example, did you know that you

can create some "Mario Brothers" levels just using a [INAUDIBLE]

networks.

Or you can make a color movie from a black and white.

Or you can make a 3D movie from a 2D movie.

So things that seem difficult or impossible

are something that you can do now using machine learning

and neural networks.

And as an example, this one is "Inside the Brother."

It's David R from Japan, designer and artist.

And he just decided to make these simple games

with two volley players.

And in fact, they play extremely well just

using a very, very simple neural network

that he displays on the screen.

So because machine learning is so useful and widespread now,

there is no doubt that it will have a huge impact

on art and on artists.

So that's why we decide something like one year

ago to create a machine learning residency in the lab.

So we asked artists to join us and to create great experience.

So now I will leave to Mario Klingemann with our latest

artist in residence.

MARIO KLINGEMANN: Thank you, Damien.

[APPLAUSE]

Hi, everybody.

My name's Mario Klingemann.

And I'm a code artists.

And might sound like I'm kind of really good at indentation

or write beautiful code.

But that's not really what it is.

It just says that I'm using code and algorithms to produce

things that look interesting.

And some of them might even be called art.

I don't know-- I'm not the one to decide that.

Like any other artist, I have this problem.

You look around you, and it looks like everything

has already been done.

I mean, in times of Google, you come up with a great idea,

you Google it, and think, oh, well, OK, done already.

So it seems there are no empty spaces anymore--

no white spaces where you can make your mark,

where you can kind of be original.

On the other hand, if you look at it,

there are no real-- humans are incapable of having

original ideas.

Ideas are always just recombination of something

some other people have done before.

You take concept A and concept B,

and the idea is finding a new connection between them.

And so this is where, for me, the computer can help

me finding these connections.

So in theory, all I have to do is

go through every possible permutation.

And the computer will offer me new combinations that,

hopefully, not have been done.

And all I have to do is sit back,

and let the whatever it has created pass by,

and decide if I like it or not.

So in a way, I'm becoming more of a curator than a creator.

I'll show you a short example.

So this is a tool I call Ernst.

It's a kind of an homage to Max Ernst, an artist

famous for his surreal collages back in the early 20th century.

And what he did, he created these collages from things

he found in papers, and catalogs, et cetera.

So I decided, well, maybe I'll build my own collage tool.

And in this case, I'm using assets

found in the vast collection of public domain images

by the Internet Archive.

And I wrote me a tool that helps me to automatically

cut them out.

And then I say, OK, if I give you these five elements, what

can you do with them?

And then it produces me stuff like these.

And unlike Max Ernst, I have the possibility

to also scale material.

And then you get these.

If you have like pipes, you get fractal structures,

things that look like plants.

And the process is very like this.

I have this tool with all the library elements,

and then it just starts combining them in random ways.

And sometimes, I see something that I like.

Very often I see things that are just horrible,

or just total chaos.

But yet, sometimes there's something that looks like this.

I call that, for example, "Run, Hipster, Run."

And I must say, coming up with funny titles

or very interesting titles is, of course, a nice perk

of this way of working.

But of course, there's still this problem

that I still have to look through a lot of images which

are just noise, just chaos.

So wouldn't it be nice if the machine could

learn what I like, what my tastes are, or, even better,

what other people like.

And then I can sell them better.

So I realized I have to first understand

what do humans find interesting in images?

What is it that makes one image more artful than another one?

And this directed my view to this growing amount

of digital archives.

And those are now-- there are lots of museums and libraries

out there that start digitizing all their old books

and paintings, just like the Cultural Institute

helps museums doing that.

And so I first stumbled upon this about two years ago,

when the British Library uploaded one million images

that were automatically extracted from books spanning

from 1500 to 1899.

There was only a little kind of a problem with it,

because all these illustrations and photos

were cut out automatically.

So they had OCR scans, and then they

knew there would be an image in a certain area.

But unless you didn't look yourself at the image,

you wouldn't know what's actually on it.

So if you were looking for, let's

say, a portrait of Shakespeare, you

would have to manually go through every image

until you maybe struck upon it.

So I thought that's a bit tricky.

Maybe I can help them with classifying their material,

and training the computer.

OK, this is a portrait.

This is a map.

So I started in a way figuring out ways how I could do that.

And eventually, I was able to tag

about 400,000 images for them.

I mean, this was a kind of group effort.

Everybody could join in.

But working with this material, I realized,

is such a joyful experience, because in the beginning,

I was just interested in the machine learning.

But actually, you suddenly realize

there's this goldmine, this huge mine of material.

And sometimes, really, you go through lots

of things that seem to be boring or you're not interested in.

And then you strike upon a beautiful illustration

or something.

And I realized that is actually a huge part

of the fun of the process.

So for example, take this rock or stone axe.

Well, once you go through this material,

you start recognizing patterns.

And so sometimes there comes this rock by.

And you say, OK, well, I don't care.

But then the second one, and you say, oh, maybe I

should start at a rock collection or rock category.

And then what happens is, suddenly

you are happy when every time you come another one of those.

And then, well, what I do is I start

arranging them, and putting them kind of in a new context.

And then you start actually starting

to appreciate the craftsmanship that went into this.

And also you can-- once you put lots of very similar things

together, you can much better distinguish

between the slight differences in there.

Yes, so I start doing this.

For example, here on the left side,

you see a piece called to 36 anonymous profiles.

There's all these 100s, 1,000s of geological profiles, which,

again, you probably, if you're not interested or are

a geologist, you wouldn't care.

But like this, it becomes a really interesting field

or just a way to bring these things that maybe sometimes

have been hidden for 100 years in a book,

and nobody has watched them.

And now you can bring them back to life.

Or on the right side, a piece I call "16 Very Sad Girls."