Author Archives: Damien Henry

A Creative Summer with Arts & Culture Experiments

With so many artifacts and historic treasures from museums and cultural institutions around the world to explore on Google Arts & Culture, sometimes it can be hard to know where to start. That’s why our Creative Coders like to experiment with playful tools for you to discover the hidden gems curated by our many partner institutions.

Today we’re launching five new experiencesfor culture lovers of all ages to dive into the collections. Reimagine the world’s most famous paintings in your own color palette  with “Color Hunt”. Ready for a jam session with none other than the master of harmonization himself? Check out “Assisted Melody.” which helps you to create music in the style of Bach. If it’s a virtual round-the-world tour you’re after, team up with Hopper, our penguin guide. Want to flex your creative muscles? Doodle around with “Draw to Art” and see your sketches transform into artworks.

Color Hunt visual

Color Hunt

Have you ever studied a painting and wondered how it would look with a different color palette? With Color Hunt, you can use colors in your own environment to recreate existing artworks, giving yourself a new perspective on the work as well as your own surroundings.


Assisted Melody 1.jpeg

Assisted Melody

Have you ever wanted to collaborate with Bach on a composition? Now’s your chance: Assisted Melody allows you to compose your own tune on a virtual sheet of music, and with the click of a button make it sound like Bach. No musical knowledge required—we’ve done that for you, by training our machine learning algorithm on the composer’s choral works.

Draw to Art.jpeg

Draw to Art

Many great works of art started as a sketch, but has a sketch ever been used to search for art? If you’re not sure what that even means, try out Draw to Art. It uses machine learning to match your doodles to paintings, drawings and sculptures with similar shapes. Sketch whatever forms come to mind and see what artworks you discover.
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Hopper, the penguin explorer

Want to discover the pyramids of Giza or visit the Eiffel Tower? Let Hopper the penguin be your guide and show you around some of the most famous places in the world. You can even snap a picture of Hopper and immortalize your favorite virtual trips. And if you’re searching for more fun with our cheeky penguin, here’s a clue: Sometimes he likes to get lost in museums. Follow him on our Family Fun page.

Ocean of Books_map.jpeg

An Ocean of Books

An Ocean of Books is a new way to explore all kinds of literature and learn fun facts. For example, did you know that Sherlock Holmes never actually said “Elementary, my dear Watson”? Or that the original manuscript for John Steinbeck’s Of Mice and Men was eaten by the author’s dog Toby? Let our map guide you through a landscape of authors and books, perhaps leading you to your next reading adventure.

If these experiments have whetted your appetite for fun and games, see what you think of our recent collection Play with Arts & Culture, which offers puzzles and trivia drawn from the cultural treasures of our partner institutions. Try them on your computer via g.co/artgames, or in the Google Arts & Culture app on your mobile.

When fashion and choreography meet artificial intelligence

At the Google Arts & Culture Lab in Paris, we’re all about exploring the relationship between art and technology. Since 2012, we’ve worked with artists and creators from many fields, developing experiments that let you design patterns in augmented reality, co-create poetry, or experience multisensory art installations. Today we’re launching two experiments to test the potential of artificial intelligence in the worlds of contemporary dance and fashion.

For our first experiment, Runway Palette, we came together with The Business of Fashion, whose collection includes 140,000 photos of runway looks from almost 4,000 fashion shows. If you could attend one fashion show per day, it would take you more than ten years to see them all. By extracting the main colors of each look, we used machine learning to organize the images by color palette, resulting in an interactive visualization of four years of fashion by almost 1,000 designers.

Everyone can now use the color palette visualization to explore colors, designers, seasons, and trends that come from Fashion Weeks worldwide.  You can even snap or upload a picture of, let’s say, your closet, or autumn leaves, and discover how designers used a similar color palette in fashion.

  • Various fashion images in visualised cloud.gif

    Explore four years of runway looks organized by color using machine learning.

  • Images of clothing on a mobile screen.gif

    Take a picture or upload a photo to see how designers used a similar color palette in fashion.

  • a visualisation showing fashion looks.gif

    Click on a palette to see which runway looks have similar colors, and compare by collection.

For our second experiment, Living Archive, we continued our collaboration with Wayne McGregor to create an AI-driven choreography tool. Trained on over 100 hours of dance performances from Wayne’s 25-year archive, the experiment uses machine learning to predict and generate movement in the style of Wayne’s dancers. In July of this year, they used the tool in his creative process for a new work that premiered at the LA Music Center. 


Today, we are making this experiment available to everyone. Living Archive lets you explore almost half a million poses from Wayne’s extensive archive, organized by visual similarity. Use the experiment to make connections between poses, or capture  your own movement to create your very own choreography.
The Living Archive experiment

You can try our new experiments on the Google Arts & Culture experiments page or via our free app for iOS and Android.

Machine learning meets culture

Whether helpingphysicians identify disease orfinding photos of “hugs,” AI is behind a lot of the work we do at Google. And at our Arts & Culture Lab in Paris, we’ve been experimenting with how AI can be used for the benefit of culture. Today, we’re sharing our latest experiments—prototypes that build on seven years of work in partnership the 1,500 cultural institutions around the world. Each of these experimental applications runs AI algorithms in the background to let you unearth cultural connections hidden in archives—and even find artworks that match your home decor.

Art Palette

From interior design to fashion, color plays a fundamental role in expression, communicating personality, mood and emotion. Art Palette lets you choose a color palette, and using a combination of computer vision algorithms, it matches artworks from cultural institutions from around the world with your selected hues. See how Van Gogh's Irises share a connection of color with a 16th century Iranian folio and Monet’s water lilies. You can also snap a photo of your outfit today or your home decor and can click through to learn about the history behind the artworks that match your colors.


Watch how legendary fashion designer, Sir Paul Smith uses Art Palette:
The art of color: Paul Smith experiences Art Palette #GoogleArts

Giving historic photos a new lease on LIFE

Beginning in 1936, LIFE Magazine captured some of the most iconic moments of the 20th century. In its 70-year-run, millions of photos were shot for the magazine, but only 5 percent of them were published at the time. 4 million of those photos are now available for anyone to look through. But with an archive that stretches 6,000 feet (about 1,800 meters) across three warehouses, where would you start exploring? The experiment LIFE Tags uses Google’s computer vision algorithm to scan, analyze and tag all the photos from the magazine’s archives, from the A-line dress to the zeppelin. Using thousands of automatically created labels, the tool turns this unparalleled record of recent history and culture into an interactive web of visuals everyone can explore. So whether you’re looking for astronauts, an Afghan Hound or babies making funny faces, you can navigate the LIFE Magazine picture archive and find them with the press of a button.

Baby Making Funny Faces.gif

Identifying MoMA artworks through machine learning

Starting with their first exhibition in 1929, The Museum of Modern Art in New York took photos of their exhibitions. While the photos documented important chapters of modern art, they lacked information about the works in them. To identify the art in the photos, one would have had to comb through 30,000 photos—a task that would take months even for the trained eye. The tool built in collaboration with MoMA did the work of automatically identifying artworks—27,000 of them—and helped turn this repository of photos into an interactive archive of MoMA’s exhibitions.
Identifying art through machine learning with the MoMA #GoogleArts

We unveiled our first set of experiments that used AI to aid cultural discoveries in 2016. Since then we’ve collaborated with institutions and artists, including stage designer Es Devlin, who created an installation for the Serpentine Galleries in London that uses machine learning to generate poetry.  We hope these experimental applications will not only lead you to explore something new, but also shape our conversations around the future of technology, its potential as an aid for discovery and creativity.

You can try all our experiments at g.co/artsexperiments or through the free Google Arts & Culture app for iOS and Android.

Experimenting at the crossroads of Machine Learning and arts

This 233,000 year old female figurine is said to be the oldest piece of artwork ever found. Based on microscopic analyses archeologists suggest that it was shaped by human hands — possibly one of the first artists in the world. Critically, those human hands used a flint to shape the head and arms.

GAC MachineLearning_Artifact.png
Made of volcanic material, this 233,000-year-old and 3.5 cm tall female figure is the oldest artwork discovered.

Ever since its ancient beginnings, art has been influenced by the tools and technology available to create and share works. Breakthroughs in painting, printing, or the invention of photography all provided tools for creative expression, and left their mark on our culture. In turn, art has inspired innovation and pushed the boundaries of technology. This remains true today.

Our team at Google Arts & Culture has been curious what Machine Learning can do in the hands of artists, museums or curators to create new experiences and help unlock art for everyone. This is why we invited creative coders - think of someone who is both a software engineer and an artist - to collaborate and experiment at our Lab in Paris. And today, we’re excited to share what we’ve been up to: check out the Google Arts & Culture Experiments, a new online space where you can see and play with the experimental projects that we have built.

Google Arts & Culture Experiments

With these experiments, you can explore hundreds of thousands of artworks and let Machine Learning aid your discovery.

  • X Degrees of separation: They say any two people in the world can be connected through just a few layers of friends of friends. Much like Kevin Bacon is connected to anyone in Hollywood. How about artworks? Created in collaboration with code artist Mario Klingemann, our ‘X Degrees of Separation’ lets you choose any two artworks and the computer using Machine Learning will find a visual pathway connecting them through a chain of similar artworks.
  • t-SNE Map: we’re all familiar with landscapes paintings, but how about a landscape of the history of art itself? You can now travel through hundreds of thousands of artworks from across centuries organized into one vast 3D landscape in this experiment. The more similar two works are seen by the computer the closer they are on the map. This was built in collaboration with digital interaction artist, Cyril Diagne.
  • Tags: A picture is worth a thousand words. In our Tags experiment the computer looked at the artworks and tagged them with all it saw in the picture. In turn this allows you to explore anything from “hairstyles” to more abstract concepts like “calm” or “happy”  in works from across the world of art.

GAC MachineLearning_Xdegrees4.gif
‘X Degrees of Separation’ lets you choose any two artworks and the Machine Learning algorithm will find a visual pathway connecting them through a chain of similar artworks.

We’re delighted to see the amount of excitement around Machine Learning in the cultural sector, especially among creative coders. If you are a creative coder yourself, or just getting started, check out the new AI Experiments website created by our friends in the Creative Lab where you can find further inspiration and resources.

Experimenting at the crossroads of Machine Learning and arts

This 233,000 year old female figurine is said to be the oldest piece of artwork ever found. Based on microscopic analyses archeologists suggest that it was shaped by human hands — possibly one of the first artists in the world. Critically, those human hands used a flint to shape the head and arms.

GAC MachineLearning_Artifact.png
Made of volcanic material, this 233,000-year-old and 3.5 cm tall female figure is the oldest artwork discovered.

Ever since its ancient beginnings, art has been influenced by the tools and technology available to create and share works. Breakthroughs in painting, printing, or the invention of photography all provided tools for creative expression, and left their mark on our culture. In turn, art has inspired innovation and pushed the boundaries of technology. This remains true today.

Our team at Google Arts & Culture has been curious what Machine Learning can do in the hands of artists, museums or curators to create new experiences and help unlock art for everyone. This is why we invited creative coders - think of someone who is both a software engineer and an artist - to collaborate and experiment at our Lab in Paris. And today, we’re excited to share what we’ve been up to: check out the Google Arts & Culture Experiments, a new online space where you can see and play with the experimental projects that we have built.

Google Arts & Culture Experiments

With these experiments, you can explore hundreds of thousands of artworks and let Machine Learning aid your discovery.

  • X Degrees of separation: They say any two people in the world can be connected through just a few layers of friends of friends. Much like Kevin Bacon is connected to anyone in Hollywood. How about artworks? Created in collaboration with code artist Mario Klingemann, our ‘X Degrees of Separation’ lets you choose any two artworks and the computer using Machine Learning will find a visual pathway connecting them through a chain of similar artworks.
  • t-SNE Map: we’re all familiar with landscapes paintings, but how about a landscape of the history of art itself? You can now travel through hundreds of thousands of artworks from across centuries organized into one vast 3D landscape in this experiment. The more similar two works are seen by the computer the closer they are on the map. This was built in collaboration with digital interaction artist, Cyril Diagne.
  • Tags: A picture is worth a thousand words. In our Tags experiment the computer looked at the artworks and tagged them with all it saw in the picture. In turn this allows you to explore anything from “hairstyles” to more abstract concepts like “calm” or “happy”  in works from across the world of art.

GAC MachineLearning_Xdegrees4.gif
‘X Degrees of Separation’ lets you choose any two artworks and the Machine Learning algorithm will find a visual pathway connecting them through a chain of similar artworks.

We’re delighted to see the amount of excitement around Machine Learning in the cultural sector, especially among creative coders. If you are a creative coder yourself, or just getting started, check out the new AI Experiments website created by our friends in the Creative Lab where you can find further inspiration and resources.

Experimenting at the crossroads of Machine Learning and arts

This 233,000 year old female figurine is said to be the oldest piece of artwork ever found. Based on microscopic analyses archeologists suggest that it was shaped by human hands — possibly one of the first artists in the world. Critically, those human hands used a flint to shape the head and arms.

GAC MachineLearning_Artifact.png
Made of volcanic material, this 233,000-year-old and 3.5 cm tall female figure is the oldest artwork discovered.

Ever since its ancient beginnings, art has been influenced by the tools and technology available to create and share works. Breakthroughs in painting, printing, or the invention of photography all provided tools for creative expression, and left their mark on our culture. In turn, art has inspired innovation and pushed the boundaries of technology. This remains true today.

Our team at Google Arts & Culture has been curious what Machine Learning can do in the hands of artists, museums or curators to create new experiences and help unlock art for everyone. This is why we invited creative coders - think of someone who is both a software engineer and an artist - to collaborate and experiment at our Lab in Paris. And today, we’re excited to share what we’ve been up to: check out the Google Arts & Culture Experiments, a new online space where you can see and play with the experimental projects that we have built.

Google Arts & Culture Experiments

With these experiments, you can explore hundreds of thousands of artworks and let Machine Learning aid your discovery.

  • X Degrees of separation: They say any two people in the world can be connected through just a few layers of friends of friends. Much like Kevin Bacon is connected to anyone in Hollywood. How about artworks? Created in collaboration with code artist Mario Klingemann, our ‘X Degrees of Separation’ lets you choose any two artworks and the computer using Machine Learning will find a visual pathway connecting them through a chain of similar artworks.
  • t-SNE Map: we’re all familiar with landscapes paintings, but how about a landscape of the history of art itself? You can now travel through hundreds of thousands of artworks from across centuries organized into one vast 3D landscape in this experiment. The more similar two works are seen by the computer the closer they are on the map. This was built in collaboration with digital interaction artist, Cyril Diagne.
  • Tags: A picture is worth a thousand words. In our Tags experiment the computer looked at the artworks and tagged them with all it saw in the picture. In turn this allows you to explore anything from “hairstyles” to more abstract concepts like “calm” or “happy”  in works from across the world of art.

GAC MachineLearning_Xdegrees4.gif
‘X Degrees of Separation’ lets you choose any two artworks and the Machine Learning algorithm will find a visual pathway connecting them through a chain of similar artworks.

We’re delighted to see the amount of excitement around Machine Learning in the cultural sector, especially among creative coders. If you are a creative coder yourself, or just getting started, check out the new AI Experiments website created by our friends in the Creative Lab where you can find further inspiration and resources.