Tag Archives: Art

Using GANs to Create Fantastical Creatures

Creating art for digital video games takes a high degree of artistic creativity and technical knowledge, while also requiring game artists to quickly iterate on ideas and produce a high volume of assets, often in the face of tight deadlines. What if artists had a paintbrush that acted less like a tool and more like an assistant? A machine learning model acting as such a paintbrush could reduce the amount of time necessary to create high-quality art without sacrificing artistic choices, perhaps even enhancing creativity.

Today, we present Chimera Painter, a trained machine learning (ML) model that automatically creates a fully fleshed out rendering from a user-supplied creature outline. Employed as a demo application, Chimera Painter adds features and textures to a creature outline segmented with body part labels, such as “wings” or “claws”, when the user clicks the “transform” button. Below is an example using the demo with one of the preset creature outlines.

Using an image imported to Chimera Painter or generated with the tools provided, an artist can iteratively construct or modify a creature outline and use the ML model to generate realistic looking surface textures. In this example, an artist (Lee Dotson) customizes one of the creature designs that comes pre-loaded in the Chimera Painter demo.

In this post, we describe some of the challenges in creating the ML model behind Chimera Painter and demonstrate how one might use the tool for the creation of video game-ready assets.

Prototyping for a New Type of Model
In developing an ML model to produce video-game ready creature images, we created a digital card game prototype around the concept of combining creatures into new hybrids that can then battle each other. In this game, a player would begin with cards of real-world animals (e.g., an axolotl or a whale) and could make them more powerful by combining them (making the dreaded Axolotl-Whale chimera). This provided a creative environment for demonstrating an image-generating model, as the number of possible chimeras necessitated a method for quickly designing large volumes of artistic assets that could be combined naturally, while still retaining identifiable visual characteristics of the original creatures.

Since our goal was to create high-quality creature card images guided by artist input, we experimented with generative adversarial networks (GANs), informed by artist feedback, to create creature images that would be appropriate for our fantasy card game prototype. GANs pair two convolutional neural networks against each other: a generator network to create new images and a discriminator network to determine if these images are samples from the training dataset (in this case, artist-created images) or not. We used a variant called a conditional GAN, where the generator takes a separate input to guide the image generation process. Interestingly, our approach was a strict departure from other GAN efforts, which typically focus on photorealism.

To train the GANs, we created a dataset of full color images with single-species creature outlines adapted from 3D creature models. The creature outlines characterized the shape and size of each creature, and provided a segmentation map that identified individual body parts. After model training, the model was tasked with generating multi-species chimeras, based on outlines provided by artists. The best performing model was then incorporated into Chimera Painter. Below we show some sample assets generated using the model, including single-species creatures, as well as the more complex multi-species chimeras.

Generated card art integrated into the card game prototype showing basic creatures (bottom row) and chimeras from multiple creatures, including an Antlion-Porcupine, Axolotl-Whale, and a Crab-Antion-Moth (top row). More info about the game itself is detailed in this Stadia Research presentation.

Learning to Generate Creatures with Structure
An issue with using GANs for generating creatures was the potential for loss of anatomical and spatial coherence when rendering subtle or low-contrast parts of images, despite these being of high perceptual importance to humans. Examples of this can include eyes, fingers, or even distinguishing between overlapping body parts with similar textures (see the affectionately named BoggleDog below).

GAN-generated image showing mismatched body parts.

Generating chimeras required a new non-photographic fantasy-styled dataset with unique characteristics, such as dramatic perspective, composition, and lighting. Existing repositories of illustrations were not appropriate to use as datasets for training an ML model, because they may be subject to licensing restrictions, have conflicting styles, or simply lack the variety needed for this task.

To solve this, we developed a new artist-led, semi-automated approach for creating an ML training dataset from 3D creature models, which allowed us to work at scale and rapidly iterate as needed. In this process, artists would create or obtain a set of 3D creature models, one for each creature type needed (such as hyenas or lions). Artists then produced two sets of textures that were overlaid on the 3D model using the Unreal Engine — one with the full color texture (left image, below) and the other with flat colors for each body part (e.g., head, ears, neck, etc), called a “segmentation map” (right image, below). This second set of body part segments was given to the model at training to ensure that the GAN learned about body part-specific structure, shapes, textures, and proportions for a variety of creatures.

Example dataset training image and its paired segmentation map.

The 3D creature models were all placed in a simple 3D scene, again using the Unreal Engine. A set of automated scripts would then take this 3D scene and interpolate between different poses, viewpoints, and zoom levels for each of the 3D creature models, creating the full color images and segmentation maps that formed the training dataset for the GAN. Using this approach, we generated 10,000+ image + segmentation map pairs per 3D creature model, saving the artists millions of hours of time compared to creating such data manually (at approximately 20 minutes per image).

Fine Tuning
The GAN had many different hyper-parameters that could be adjusted, leading to different qualities in the output images. In order to better understand which versions of the model were better than others, artists were provided samples for different creature types generated by these models and asked to cull them down to a few best examples. We gathered feedback about desired characteristics present in these examples, such as a feeling of depth, style with regard to creature textures, and realism of faces and eyes. This information was used both to train new versions of the model and, after the model had generated hundreds of thousands of creature images, to select the best image from each creature category (e.g., gazelle, lynx, gorilla, etc).

We tuned the GAN for this task by focusing on the perceptual loss. This loss function component (also used in Stadia’s Style Transfer ML) computes a difference between two images using extracted features from a separate convolutional neural network (CNN) that was previously trained on millions of photographs from the ImageNet dataset. The features are extracted from different layers of the CNN and a weight is applied to each, which affects their contribution to the final loss value. We discovered that these weights were critically important in determining what a final generated image would look like. Below are some examples from the GAN trained with different perceptual loss weights.

Dino-Bat Chimeras generated using varying perceptual loss weights.

Some of the variation in the images above is due to the fact that the dataset includes multiple textures for each creature (for example, a reddish or grayish version of the bat). However, ignoring the coloration, many differences are directly tied to changes in perceptual loss values. In particular, we found that certain values brought out sharper facial features (e.g., bottom right vs. top right) or “smooth” versus “patterned” (top right vs. bottom left) that made generated creatures feel more real.

Here are some creatures generated from the GAN trained with different perceptual loss weights, showing off a small sample of the outputs and poses that the model can handle.

Creatures generated using different models.
A generated chimera (Dino-Bat-Hyena, to be exact) created using the conditional GAN. Output from the GAN (left) and the post-processed / composited card (right).

Chimera Painter
The trained GAN is now available in the Chimera Painter demo, allowing artists to work iteratively with the model, rather than drawing dozens of similar creatures from scratch. An artist can select a starting point and then adjust the shape, type, or placement of creature parts, enabling rapid exploration and for the creation of a large volume of images. The demo also allows for uploading a creature outline created in an external program, like Photoshop. Simply download one of the preset creature outlines to get the colors needed for each creature part and use this as a template for drawing one outside of Chimera Painter, and then use the “Load’ button on the demo to use this outline to flesh out your creation.

It is our hope that these GAN models and the Chimera Painter demonstration tool might inspire others to think differently about their art pipeline. What can one create when using machine learning as a paintbrush?

This project is conducted in collaboration with many people. Thanks to Ryan Poplin, Lee Dotson, Trung Le, Monica Dinculescu, Marc Destefano, Aaron Cammarata, Maggie Oh, Richard Wu, Ji Hun Kim, Erin Hoffman-John, and Colin Boswell. Thanks to everyone who pitched in to give hours of art direction, technical feedback, and drawings of fantastic creatures.

Source: Google AI Blog

India’s mini-masterpieces brought to life with AI and AR

Miniature paintings are among the most beautiful, most technically-advanced and most sophisticated art forms in Indian culture. Though compact (about the same size as a small book), they typically tackle profound themes such as love, power and faith. Using technologies like machine learning, augmented reality and high-definition robotic cameras, Google Arts & Culture has partnered with the National Museum in New Delhi to showcase these special works of art in a magical new way.

Virtually wander the halls of a special ‘pocket gallery’

Inspired by the domes and doorways that punctuate Indian homes and public spaces, this is the first AR-powered art gallery designed with traditional Indian architecture. Using your smartphone, you can open up a life-size virtual space, walk around at your leisure and zoom into your favorite pieces—you have this beautiful museum to yourself! 

The first AR-powered art gallery inspired by the domes and doorways of India.

Art meets AI, with  Magnify Miniatures

Miniatures are rich in detailed representations of topics that have shaped Indian culture. Thanks to machine learning, you can now discover these attributes across a collection of miniature paintings. Select from tags like ‘face’, ‘animal’, or even ‘moustache’, and see where these features occur!

Take a closer look with immersive in-painting tours 

Art Camera, our ultra-high-resolution robotic camera, was deployed to produce the most vivid images of masterpieces ever seen. Using these images, we’ve created over 75 in-painting tours to help you stop and appreciate details like wisps of smoke from firecrackers, or see how finesse and variety of every person’s attire in this royal procession—flourishes that you wouldn’t be able to see well with the naked eye.

You can zoom in to see the wisps of smoke in this miniature titled "Lady Holding a Sparkler"

Explore thousands of rich stories and images 

The virtual collection includes 1,200 high resolution images from 25 collections all around the world and more than 75 stories, depicting scenes that include legendary marriage processions, the joy of being among nature, or epic battles. Curious minds,  students and families will find playful and educational ways to enjoy the world of Indian miniatures, such as an interactive coloring book

We’re glad that through the power of technology, people all over the world can engage with these miniature masterpieces like never before.

Posted by Simon Rein, Program Manager, Google Arts & Culture

A digital exhibit to elevate Indigenous art

In March 2020, the 22nd Biennale of Sydney opened to wide acclaim—only to close after 10 days because of COVID-19. The Biennale has since physically reopened to limited audiences, but now, through a virtual exhibit on Google Arts & Culture, people all over the world can experience it.
This year’s Biennale is led by First Nations artists, and showcases work from marginalised communities around the world, under the artistic direction of the Indigenous Australian artist, Brook Andrew. It’s titled NIRIN—meaning “edge”—a word of Brook’s mother’s Nation, the Wiradjuri people of western New South Wales.
To commemorate the opening of this unique exhibition, and learn more about its origins and purpose, we spoke with Jodie Polutele, Head of Communications and Community Engagement at the Biennale of Sydney.

Tell us about the theme of this year’s exhibition. 
NIRIN is historic in its focus on the unresolved nature of Australian and global colonial history. It presents the work of artists and communities that are often relegated to the edge and whose practices challenge dominant narratives.
As a community, we’re at a critical point in time where the voices, histories and spheres of knowledge that have been historically pushed to “the edge” are being heard and shared. The recent Black Lives Matter protests in the United States and in other parts of the world have triggered a belated awakening in many people—particularly in Australia—about the real-life impacts of systemic racism and inequality. But we have a long way to go, and the art and ideas presented in NIRIN are one way to start (or continue) the conversation.
What does this offer audiences, both in Australia, and all over the world, particularly during this time? 
Many of the artworks ask audiences to be critical of dominant historical narratives, and our own perspective and privilege; we are forced to recognise and question our own discomfort. In doing so, they also present an opportunity to inspire truly meaningful action.
What are some of the highlights of the exhibition? 
Some highlights include Healing Land, Remembering Country by Tony Albert, a sustainable greenhouse which raises awareness of the Stolen Generations and poses important questions about how we remember, give justice to and rewrite complex and traumatic histories. Latai Taumoepeau’s endurance performance installation on Cockatoo Island explores the fragility of Pacific Island nations and the struggle of rising sea levels and displacement. Zanele Muholi’s three bodies of work at the Museum of Contemporary Art look at the politics of race, gender and sexuality. Wiradjuri artist Karla Dickens’ installation A Dickensian Circus presents a dramatic collection of objects inside the Art Gallery of New South Wales’ grand vestibule, reclaiming the space to share the hidden stories and histories of Indigenous people.
Tony Albert's sustainable greenhouse posing important questions about historical and intergenerational trauma
This virtual exhibit was not what you originally imagined. Can you tell us what hurdles you have had to overcome? 
The Biennale of Sydney takes more than two years to produce with a team of dedicated people. Closing the exhibitions and cancelling or postponing a program of more than 600 events was devastating. But with the enormous support of the Google Arts & Culture team, we have delivered a virtual exhibition that is respectful of artists’ works and conveys the true vision of NIRIN—inspiring conversation and action through a meaningful arts experience. We hope that NIRIN on Google Arts & Culture will be an enduring legacy for the exhibition, and also for the talented team who made it happen.
Watch Latal Taumoepeau's endurance performance, The Last Resort 

Exploring and Visualizing an Open Global Dataset

Machine learning systems are increasingly influencing many aspects of everyday life, and are used by both the hardware and software products that serve people globally. As such, researchers and designers seeking to create products that are useful and accessible for everyone often face the challenge of finding data sets that reflect the variety and backgrounds of users around the world. In order to train these machine learning systems, open, global — and growing — datasets are needed.

Over the last six months, we’ve seen such a dataset emerge from users of Quick, Draw!, Google’s latest approach to helping wide, international audiences understand how neural networks work. A group of Googlers designed Quick, Draw! as a way for anyone to interact with a machine learning system in a fun way, drawing everyday objects like trees and mugs. The system will try to guess what their drawing depicts, within 20 seconds. While the goal of Quick, Draw! was simply to create a fun game that runs on machine learning, it has resulted in 800 million drawings from twenty million people in 100 nations, from Brazil to Japan to the U.S. to South Africa.

And now we are releasing an open dataset based on these drawings so that people around the world can contribute to, analyze, and inform product design with this data. The dataset currently includes 50 million drawings Quick Draw! players have generated (we will continue to release more of the 800 million drawings over time).

It’s a considerable amount of data; and it’s also a fascinating lens into how to engage a wide variety of people to participate in (1) training machine learning systems, no matter what their technical background; and (2) the creation of open data sets that reflect a wide spectrum of cultures and points of view.
Seeing national — and global — patterns in one glance
To understand visual patterns within the dataset quickly and efficiently, we worked with artist Kyle McDonald to overlay thousands of drawings from around the world. This helped us create composite images and identify trends in each nation, as well across all nations. We made animations of 1000 layered international drawings of cats and chairs, below, to share how we searched for visual trends with this data:

Cats, made from 1000 drawings from around the world:
Chairs, made from 1,000 drawings around the world:
Doodles of naturally recurring objects, like cats (or trees, rainbows, or skulls) often look alike across cultures:
However, for objects that might be familiar to some cultures, but not others, we saw notable differences. Sandwiches took defined forms or were a jumbled set of lines; mug handles pointed in opposite directions; and chairs were drawn facing forward or sideways, depending on the nation or region of the world:
One size doesn’t fit all
These composite drawings, we realized, could reveal how perspectives and preferences differ between audiences from different regions, from the type of bread used in sandwiches to the shape of a coffee cup, to the aesthetic of how to depict objects so they are visually appealing. For example, a more straightforward, head-on view was more consistent in some nations; side angles in others.

Overlaying the images also revealed how to improve how we train neural networks when we lack a variety of data — even within a large, open, and international data set. For example, when we analyzed 115,000+ drawings of shoes in the Quick, Draw! dataset, we discovered that a single style of shoe, which resembles a sneaker, was overwhelmingly represented. Because it was so frequently drawn, the neural network learned to recognize only this style as a “shoe.”

But just as in the physical world, in the realm of training data, one size does not fit all. We asked, how can we consistently and efficiently analyze datasets for clues that could point toward latent bias? And what would happen if a team built a classifier based on a non-varied set of data?
Diagnosing data for inclusion
With the open source tool Facets, released last month as part of Google’s PAIR initiative, one can see patterns across a large dataset quickly. The goal is to efficiently, and visually, diagnose how representative large datasets, like the Quick, Draw! Dataset, may be.

Here’s a screenshot from the Quick,Draw! dataset within the Facets tool. The tool helped us position thousands of drawings by "faceting" them in multiple dimensions by their feature values, such as country, up to 100 countries. You, too, can filter for for features such as “random faces” in a 10-country view, which can then be expanded to 100 countries. At a glance, you can see proportions of country representations. You can also zoom in and see details of each individual drawing, allowing you to dive deeper into single data points. This is especially helpful when working with a large visual data set like Quick, Draw!, allowing researchers to explore for subtle differences or anomalies, or to begin flagging small-scale visual trends that might emerge later as patterns within the larger data set.
Here’s the same Quick, Draw! data for “random faces,” faceted for 94 countries and seen from another view. It’s clear in the few seconds that Facets loads the drawings in this new visualization that the data is overwhelmingly representative of the United States and European countries. This is logical given that the Quick, Draw! game is currently only available in English. We plan to add more languages over time. However, the visualization shows us that Brazil and Thailand seem to be non-English-speaking nations that are relatively well-represented within the data. This suggested to us that designers could potentially research what elements of the interface design may have worked well in these countries. Then, we could use that information to improve Quick,Draw! in its next iteration for other global, non-English-speaking audiences. We’re also using the faceted data to help us figure out how prioritize local languages for future translations.
Another outcome of using Facets to diagnose the Quick, Draw! data for inclusion was to identify concrete ways that anyone can improve the variety of data, as well as check for potential biases. Improvements could include:
  • Changing protocols for human rating of data or content generation, so that the data is more accurately representative of local or global populations
  • Analyzing subgroups of data and identify the database equivalent of "intersectionality" surfaced within visual patterns
  • Augmenting and reweighting data so that it is more inclusive
By releasing this dataset, and tools like Facets, we hope to facilitate the exploration of more inclusive approaches to machine learning, and to turn those observations into opportunities for innovation. We’re just beginning to draw insights from both Quick, Draw! and Facets. And we invite you to draw more with us, too.

Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, Nick Fox-Gieg, built Quick, Draw! in collaboration with Google Creative Lab and Google’s Data Arts Team. The video about fairness in machine learning was created by Teo Soares, Alexander Chen, Bridget Prophet, Lisa Steinman, and JR Schmidt from Google Creative Lab. James Wexler, Jimbo Wilson, and Mahima Pushkarna, of PAIR, designed Facets, a project led by Martin Wattenberg and Fernanda Viégas, Senior Staff Research Scientists on the Google Brain team, and UX Researcher Jess Holbrook. Ian Johnson from the Google Cloud team contributed to the visualizations of overlaid drawings.

Neural Network-Generated Illustrations in Allo

Taking, sharing, and viewing selfies has become a daily habit for many — the car selfie, the cute-outfit selfie, the travel selfie, the I-woke-up-like-this selfie. Apart from a social capacity, self-portraiture has long served as a means for self and identity exploration. For some, it’s about figuring out who they are. For others it’s about projecting how they want to be perceived. Sometimes it’s both.

Photography in the form of a selfie is a very direct form of expression. It comes with a set of rules bounded by reality. Illustration, on the other hand, empowers people to define themselves - it’s warmer and less fraught than reality.
Today, Google is introducing a feature in Allo that uses a combination of neural networks and the work of artists to turn your selfie into a personalized sticker pack. Simply snap a selfie, and it’ll return an automatically generated illustrated version of you, on the fly, with customization options to help you personalize the stickers even further.
What makes you, you?
The traditional computer vision approach to mapping selfies to art would be to analyze the pixels of an image and algorithmically determine attribute values by looking at pixel values to measure color, shape, or texture. However, people today take selfies in all types of lighting conditions and poses. And while people can easily pick out and recognize qualitative features, like eye color, regardless of the lighting condition, this is a very complex task for computers. When people look at eye color, they don’t just interpret the pixel values of blue or green, but take into account the surrounding visual context.

In order to account for this, we explored how we could enable an algorithm to pick out qualitative features in a manner similar to the way people do, rather than the traditional approach of hand coding how to interpret every permutation of lighting condition, eye color, etc. While we could have trained a large convolutional neural network from scratch to attempt to accomplish this, we wondered if there was a more efficient way to get results, since we expected that learning to interpret a face into an illustration would be a very iterative process.

That led us to run some experiments, similar to DeepDream, on some of Google's existing more general-purpose computer vision neural networks. We discovered that a few neurons among the millions in these networks were good at focusing on things they weren’t explicitly trained to look at that seemed useful for creating personalized stickers. Additionally, by virtue of being large general-purpose neural networks they had already figured out how to abstract away things they didn’t need. All that was left to do was to provide a much smaller number of human labeled examples to teach the classifiers to isolate out the qualities that the neural network already knew about the image.

To create an illustration of you that captures the qualities that would make it recognizable to your friends, we worked alongside an artistic team to create illustrations that represented a wide variety of features. Artists initially designed a set of hairstyles, for example, that they thought would be representative, and with the help of human raters we used these hairstyles to train the network to match the right illustration to the right selfie. We then asked human raters to judge the sticker output against the input image to see how well it did. In some instances, they determined that some styles were not well represented, so the artists created more that the neural network could learn to identify as well.
Raters were asked to classify hairstyles that the icon on the left resembled closest. Then, once consensus was reached, resident artist Lamar Abrams drew a representation of what they had in common.
Avoiding the uncanny valley
In the study of aesthetics, a well-known problem is the uncanny valley - the hypothesis that human replicas which appear almost, but not exactly, like real human beings can feel repulsive. In machine learning, this could be compounded if were confronted by a computer’s perception of you, versus how you may think of yourself, which can be at odds.

Rather than aim to replicate a person’s appearance exactly, pursuing a lower resolution model, like emojis and stickers, allows the team to explore expressive representation by returning an image that is less about reproducing reality and more about breaking the rules of representation.
The team worked with artist Lamar Abrams to design the features that make up more than 563 quadrillion combinations.
Translating pixels to artistic illustrations
Reconciling how the computer perceives you with how you perceive yourself and what you want to project is truly an artistic exercise. This makes a customization feature that includes different hairstyles, skin tones, and nose shapes, essential. After all, illustration by its very nature can be subjective. Aesthetics are defined by race, culture, and class which can lead to creating zones of exclusion without consciously trying. As such, we strove to create a space for a range of race, age, masculinity, femininity, and/or androgyny. Our teams continue to evaluate the research results to help prevent against incorporating biases while training the system.
Creating a broad palette for identity and sentiment
There is no such thing as a ‘universal aesthetic’ or ‘a singular you’. The way people talk to their parents is different than how they talk to their friends which is different than how they talk to their colleagues. It’s not enough to make an avatar that is a literal representation of yourself when there are many versions of you. To address that, the Allo team is working with a range of artistic voices to help others extend their own voice. This first style that launched today speaks to your sarcastic side but the next pack might be more cute for those sincere moments. Then after that, maybe they’ll turn you into a dog. If emojis broadened the world of communication it’s not hard to imagine how this technology and language evolves. What will be most exciting is listening to what people say with it.

This feature is starting to roll out in Allo today for Android, and will come soon to Allo on iOS.

This work was made possible through a collaboration of the Allo Team and Machine Perception researchers at Google. We additionally thank Lamar Abrams, Koji Ashida, Forrester Cole, Jennifer Daniel, Shiraz Fuman, Dilip Krishnan, Inbar Mosseri, Aaron Sarna, and Bhavik Singh.

It’s time to start sketching, Canada. Doodle 4 Google is back!

Today’s guest post is brought to you by Canadian YouTube stars Mitch and Greg of AsapSCIENCE 
Submissions are now open for Doodle 4 Google!
If you’ve watched our videos, you already know how much we love science... and art! Whenever we visit the Google homepage, we’re always tickled to find a doodle, which combines the best of both. Google doodles are fun illustrations of the Google logo that celebrate holidays, anniversaries, and the lives of famous artists, pioneers, and scientists -- everything from the discovery of water on Mars to Canadian inventor Sandford Fleming’s 190th birthday.

Now with Doodle 4 Google, kids have the chance to see their artwork on the Google homepage for the whole country to enjoy. Doodle 4 Google is a nationwide competition, inviting students from kindergarten to Grade 12 to redesign the Google logo.*

As Canada blows out a whole lot of candles this year for its 150th birthday, what better way to celebrate than by imagining what the next 150 years will look like? That’s why Google is asking students to submit doodles based on the theme: “What I see for Canada’s future is…”.

Creating the top doodle comes with major perks: not only will their artwork adorn the Google.ca homepage for a day, but the winner will receive a $10,000 university scholarship, a $10,000 technology grant for his/her school, and a paid trip to the final Doodle 4 Google event in June. For more details, check out g.co/d4gcanada.

To help judge this year’s competition, the Honourable Kirsty Duncan, Minister of Science, En Masse co-founder Jason Botkin, president of the National Inuit Youth Council Maatalii Okalik, and Google Doodler Sophie Diao, will join us as your panel of esteemed doodle judges.

When we come up with themes for our videos, we look to cool things in science and tech for inspiration. If you know a young artist that may need a little nudge to get their creative juices flowing, we’ve worked with Google to create classroom activities that will help parents, teachers and students brainstorm, design and submit their doodles.

Participating is easier than ever. This year, students can submit a doodle made from almost any medium….including code! Ladies Learning Code created an online tutorial offering inspiration and a step-by-step guide to coding a Google doodle. Check it out here.

In Toronto in April? All throughout the month of April, parents and kids can visit the Art Gallery of Ontario to get inspired and create a doodle during Family Sundays.

Teachers and parents can download entry forms on the Doodle 4 Google site. Doodles can be uploaded digitally to Google’s site or mailed directly. Submissions are due on May 2nd. There’s no limit to the number of doodles from any one school or family... Just remember, only one doodle per student.

Let’s get our doodle on, Canada!

*Entrants need a parent or legal guardian’s permission (and signature on the entry form) in order to participate. Residents of Quebec must be at least thirteen years of age. Please see full terms and eligibility requirements here: doodles.google.ca/d4g/rules.html

Exploring the Intersection of Art and Machine Intelligence

In June of last year, we published a story about a visualization techniques that helped to understand how neural networks carried out difficult visual classification tasks. In addition to helping us gain a deeper understanding of how NNs worked, these techniques also produced strange, wonderful and oddly compelling images.

Following that blog post, and especially after we released the source code, dubbed DeepDream, we witnessed a tremendous interest not only from the machine learning community but also from the creative coding community. Additionally, several artists such as Amanda Peterson (aka Gucky), Memo Akten, Samim Winiger, Kyle McDonald and many others immediately started experimenting with the technique as a new way to create art.
GCHQ”, 2015, Memo Akten, used with permission.
Soon after, the paper A Neural Algorithm of Artistic Style by Leon Gatys in Tuebingen was released. Their technique used a convolutional neural network to factor images into their separate style and content components. This in turn allowed the creation, by using a neural network as a generic image parser, of new images that combined the style of one with the content of another. Once again it took the creative coding community by storm and immediately many artists and coders began experimenting with the new algorithm, resulting in Twitter bots and other explorations and experiments.
The style transfer algorithm crosses a photo with a painting style; for example Neil deGrasse Tyson in the style of Kadinsky’s Jane Rouge Bleu. Photo by Guillaume Piolle, used with permission.
The open-source deep-learning community, especially projects such as GitXiv, hugely contributed to the spread, accessibility and development of these algorithms. Both DeepDream and style transfer were rapidly implemented in a plethora of different languages and deep learning packages. Immediately others took the techniques and developed them further.
“Saxophone dreams” - Mike Tyka.
With machine learning as field moving forward at a breakneck pace and rapidly becoming part of many -- if not most -- online products, the opportunities for artistic uses are as wide as they are unexplored and perhaps overlooked. However the interest is growing rapidly: the University of London is now offering a course on Machine learning and art. NYU ITP offers a similar program this year. The Tate Modern’s IK Prize 2016 topic: Artificial Intelligence.

These are exciting early days, and we want to continue to stimulate artistic interest in these emerging technologies. To that end, we are announcing a two day DeepDream event in San Francisco at the Gray Area Foundation for the Arts, aimed at showcasing some of the latest exploration of the intersection of Machine Intelligence and Art, and spurring discussion focused around future directions:
  • Friday Feb 26th: DeepDream: The Art of Neural Networks, an exhibit consisting of 29 neural network generated artworks, created by artists at Google and from around the world. The works will be auctioned, with all proceeds going to the Gray Area Foundation, which has been active in supporting the intersection between arts and technology for over 10 years.
  • On Saturday Feb 27th: Art and Machine Learning Symposium, an open one-day symposium on Machine Learning and Art, aiming to bring together the neural network and the creative coding communities to exchange ideas, learn and discuss. Videos of all the talks will be posted online after the event.
We look forward to sharing some of the interesting works of art generated by the art and machine learning community, and being part of the discussion of how art and technology can be combined.