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  • DAMIEN HENRY: Hello everyone.

  • 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."

  • Again, I don't know why they have

  • so many sad girls in there.

  • But of course, again, that makes you question what

  • was happening at that time?

  • So it actually motivates you to search back

  • and, well, what's the story behind this?

  • But this all I started kind of on my own.

  • And this was not-- I wouldn't say it wasn't

  • deep learning what I was doing.

  • It was more classical