For those of you unfamiliar with the world’s first ultra-realistic humanoid AI robot artist, Meet Ai-Da. In June 2019, Ai-Da opened her first solo exhibition at the University of Oxford – a collection of eight drawings, 20 paintings, four sculptures and two video works.
Ai-Da’s creators bill her as the world’s first robot artist, and she’s the latest AI innovation to blur the boundary between machine and artist; a vision of the future suddenly becoming part of our present. She has a robotic arm system and human-like features, is equipped with facial recognition technology and is powered with artificial intelligence. She is able to analyze an image in front of her, which feeds into an algorithm to dictate the movement of her arm, enabling her to produce sketches. Her goal is creativity.
To create the prism like paintings, Ai-Da draws a picture, for example a bee or a tree. Researchers at Oxford University plot the coordinates from her drawing onto a Cartesian plane (a graph), and run them through an AI neural network, a computing system modelled on the human brain. The choices of the neural network and the way it ‘reads’ the drawing coordinates create the dazzling prism effect, as neural networks interpret the Cartesian plane very differently to humans. The complex visual output is printed onto canvas, where a human artist then paints over part of the canvas.
featured image | top: An abstract painting made with artificial intelligence: by Ai-Da the humanoid robot. artwork: Univ. of Oxford | source : kurzweilai.net
Earlier this month, The Trevor Project, a non-profit focused on providing support and suicide prevention care to LGBTQ youth released the largest nationwide survey ever conducted on LGBTQ mental health with over 34000 respondents. The data, which is refreshingly inclusive of both the diversity and the intersectionality of gender-variant experiences of young people, also reveals how much work still needs to be done in this area and prompts us towards future milestones that need to be set.
“This ground-breaking survey provides new insights into the challenges that LGBTQ youth across the country face every day, including suicide, feeling sad or hopeless, discrimination, physical threats and exposure to conversion therapy.”
Data are often thought of as digital. They are also discrete, spatial, lateral and seemingly non-subjective. Data demands tags, classes, branches and categories in order to be meaningful. Data needs to exist in parts to be whole. The biases that inherently accompany the creation of Data often dwindle into the ether around captivating visualizations that take the center stage. Biases such as choices of tags, design agendas, and economic entities that commissioned the datasets.
Data needs to exist in parts to be whole.
To be visualized coherently, Data needs ‘cleaning up’ and clean datasets project outwards. They raise questions about everything but themselves. Clean datasets afford us the convenience of neat, untarnished algorithms. When these datasets are made open, in the hands of the public domain the resulting algorithmic universe amplifies. We applaud and embrace the ideals of Open Data as reflection and reassurance of a digital democracy.
But, what happens when Data wants (and needs) to be protected? What happens when communities that have collected, created, owned, applied and disseminated knowledge for generations employ methods of preservation that Data-as-we-know-it resists? What happens when it is crucial for certain types of Data to be both sheltered as well as communicated? What happens when Data refuses qualitative distillation and the quantitative bulk is intricately tethered to the undiscountable lived experience?
Indigenous Data Sovereignty is one such domain, specifically the data concerning sexual violence against Indigenous women.
Crosscut interviewed Abigail Eco-Hawk, the Chief Research Officer of the Seattle Indian Health Board about Data Collection and Knowledge Creation that are separate from and not rooted in Western methods of understanding Data.
The following are excerpts from the article.
“When we think about data, and how it’s been gathered, is that, from marginalized communities, it was never gathered to help or serve us. It was primarily done to show the deficits in our communities, to show where there are gaps. And it’s always done from a deficit-based framework.
“As indigenous peoples, we have always been gatherers of data, of information. We’ve always been creators of original technology.
“When I went to the University of Washington, I was able to take some of the Western knowledge systems and understand how that related to the indigenous. I recognized that the systems that were currently working towards evaluation, data collection, technology, science, and the way that we looked at the health of Native people weren’t serving my people, because they didn’t have the indigenous framework.
“Decolonizing data means that the community itself is the one determining what is the information they want us to gather. Why are we gathering it? Who’s interpreting it? And are we interpreting it in a way that truly serves our communities?
“[The Seattle Indian Health Board] had decided to not publish this information because of how drastic the data was showing the rates of sexual violence against Native women. There were fears that it could stigmatize Native women, and that would cause more harm than good. But those women had shared their story, and we had a responsibility to them, and to the story, and I take that very seriously.
“One of the ways that there is a continuing genocide against American Indians/Alaska Natives is through data. When we are invisible in the data, we no longer exist. When I see an asterisk that says “not statistically significant,” or they lump us together with Pacific Islanders and Asian Americans — you can’t lump racial groups together. That is bad data practice.
“I always think about the data as story, and each person who contributed to that data as storytellers. What is our responsibility to the story and our responsibility to the storyteller? Those are all indigenous concepts, that we always care for our storytellers, and we always have a responsibility to our stories.
Japanese sound and visual artist Ryoji Ikeda’s ‘data-verse’ is one of 79 artists’ works featured from around the world at the 2019 Venice Biennale Arte. The 58th volume of the international exhibition curated by Ralph Rugoff opened on May 11th and is titled ‘May You Live in Interesting Times’ inviting artists, connoisseurs and visitors to “see and consider the course of human events in their complexity, an invitation, thus, that appears to be particularly important in times when, too often, oversimplification seems to prevail, generated by conformism or fear.”
Ikeda’s work often renders the majestic and the exalted through mathematical explorations using sound and light. Supported by Audemars Piguet, ‘data-verse’ is an audiovisual installation that interprets the omnipresent nature of data in our modern lives. The three-part research piece variegating from the microscopic to the human to the macroscopic employs massive open source scientific data sets from CERN, NASA and the human genome project to orchestrate through high-definition video projections and minimalist electronic soundtrack – ‘the hidden facets of nature and the vast scientific knowledge underpinning our existence’.
“When I set out making this work, my approach was always, first and foremost, that of a composer. Rather than creating a traditional musical composition, I used data as my source material, applying a system and structure as you would with any score.” – Ryoji Ikeda
The exhibit is open to the public at the Venice Biennale arsenale through November 24th, 2019
For the last 20 years for every hour, artist Stephen Cartwright has been meticulously recording his exact position in space and time – the latitudes, the longitudes, the precipitation and his health data. The Pizzuti Collection of the Columbus Museum of Art, Ohio curated an exhibition this winter with ‘Light’ as its muse and its material. Cartwright’s Floating Map & Floating Information series is one of seven installations currently on view at the museum through May 12th that use light to translate experiences and make visible what we discern about our world.
“I’m trying to do some pieces now about breaking away from self-tracking and seeing how other people and their data can be part of my work,” Cartwright says. “I’m working on a project called Timeline Atlas, which will allow people to put simple information in a website and look at a three-dimensional rendering of their life locations, and they can add locations for loved ones and friends.” People will also be able to compare their own data against others’, and even create physical manifestations of their data.
Image: Rafael Lozano-Hemmer, Pulse Room 2006 in Rafael Lozano-Hemmer : Pseudomatismos MUAC Museum, Mexico City, Mexico 2015. Photo: Oliver Santana
Hirshhorn Museum in Washington DC exhibited Rafael Lozano-Hemmer’s immersive public-art installations, this past winter. Fast Company interviewed the Mexican Canadian artist about the place and positioning of technology in creating art, on the nuanced identities in Latin American art and the use of personal bodily data of the audience in a public art context.
The museum is not a neutral space. We are often asked to go to a museum to be inspired by what is on display and see what is deemed important by the intelligentsia. The experience is quite different. The beautiful thing about public space is that it’s out of control. It’s a place where you don’t get as many levels of intermediation.
People could stumble upon the artwork as they go home from work. Their participation is far more surprising. It’s more political because the diversity you can get in public space is of course greater if people choose to go to a museum–especially if it’s a paying one. In a museum, you think about what you’re doing and what has happened in the past.
Fernanda Viégas and Martin Wattenberg opened Columbia University’s Talk Series: Artists using Data in late March.
Messiness, Clutter and Revelation
As pioneers in data visualisation, analytics and data art, Viégas and Wattenberg have paved new pathways for users to understand and explore data.
As technologists we ask: Can visualization help people think collectively and move us beyond numbers into the realm of words and images and never-before-told stories? As artists we seek the joy of revelation.
While most of their current work is geared towards AI as part of Google Brain, Viégas and Wattenberg began by walking their audience through one of their earliest projects using Google called ‘Web Seer’ that allows users to compare Google Suggest completions.
This is also a really interesting window into public psyche. Because, this is what people are coming to google for. It visualizes exactly the same data but by adding a couple of dimensions. So, we get a sense of which ones are more popular. We can see the completions that are different for each one of the cases. But, we can also see what they have in common.
You see a richness in these kind of data sets. It also starts to show how vulnerable some people are when they come to google for answers.
Tying into the idea of data created by the masses, the artists unfolded processes that went into their notable ‘History Flow Tool’ which, visualized the behind-the-scenes dynamics of publicly edited Wikipedia pages in 2004, when the online encyclopedia was a relatively new and mysterious place on the web. Viégas prospected commonly overlooked occurrences on Wikipedia like vandalism, watch-listing, edit wars and disambiguation that go unnoticed due to the sheer Web 2.0 speed at which the giant encyclopedia gets edited.
Wikipedia fosters a knowledge community built upon trust. An interesting feature that the artists discovered in their process were Watch lists. Something that we commonly seem to be unaware of. Watch lists on article topics help active Wikipedia contributors take notice of vandalism. Every time an article of their interest is edited, contributors receive notifications. A notification from a new IP address or a user they haven’t seen before would be cause for alarm, wherein the community would check to make sure that it’s not a vandal. A real-time visualisation within the History Flow tool would show no discontinuities.
An article on Cat tends to be longer than a lot of other articles such as ‘Design’, as more people edit ‘Cat’. The visualisation of the history of the ‘Abortion’ page would have distinct discontinuities, reflective of the polarized opinions around that topic. The artists also colored the text based on its age instead of the authors to determine parts of an article that could be posited as qualitatively more stable.
We were really interested in how people were negotiating in this sphere, how were they deciding what fits and what doesn’t fit. Questions like these, were out first exploration into how these collaboration dynamics work.
History Flow became a part of MoMA’s collection in 2003.
An attempt to extract structure from longer pieces of music, Wattenberg’s ‘The Shape of Song’ from 2011 looked at notes of repetitions from classical music and folk songs to Jazz and Led Zeppelin’s Stairway to Heaven.
Jazz is actually quite interesting. You get something that’s relatively simple at the beginning, which then explodes into complexity towards the end. This, to me, is actually capturing something visually that you can otherwise only hear.
In 2009, a Boston-based print magazine ‘The Positive Things’ commissioned Viégas and Wattenberg’s piece Flickr Flow with a brief to visualize Boston. The artists turned to the photo-sharing website Flickr for a year’s worth of creative-commons images of Boston Common, a central public park in downtown Boston, Massachusetts – with the intent to capture Boston’s visual dimension through its seasonality. The images were organized by months and parsed through for different kinds of reds, greens and so on, counting pixels for each image, which became raw data for drawing the ribbons.
This is a very “dirty” data set if you will, because these were not all going to be beautiful pictures. There would be pictures of benches, for example and other things that have nothing to do with flowers or foliage. But, we decided to work with the messiness and see if we can get somewhere.
Even with all the messiness in the data, there was still some signal that there is change. In fact, this looks very fluid. But if we break it down into the height of each season, you can see that the color distribution is dramatically different between winter, fall, summer and spring.
Art of Reproduction
Playing with the idea of visual half-truths, Watternberg’s Art of Reproduction was a collection of fragmented collages of famous artworks representing dramatic differences across the reproduced images.
Not all of these images are really the correct image at all. For one thing, they are different sizes. But, more deeply, the colors are different. And if you keep looking, you realize just how broad the variation is. We all know that reproductions are not the same as the original on some level. But, seeing the breadth of these different things is impressive.
The Wind Map
In 2012, the artists pioneered a distinctive way of visualizing the wind – something that has virtually no visual form. Working with government data of the United States, they initially began by conceiving of wind as ‘particles that we see as a pattern’. Eventually, they settled on the idea of particles that would leave behind little trails, which allowed for communicating subtler forms information such as change in direction.
When Hurricane Isaac made landfall in August 2012, the artists began receiving emails from people affected by the natural disaster.
It was a very strong experience to have something on the web that is real-time that people were looking at for very different reasons and that we had people in these very specific situations talking to you about the data that you’re visualizing.
When working with data such as this, designers tend to aggregate in turn obfuscating a lot of the detail. Viégas and Wattenberg, instead emphasize the texture and richness of the data relying on the viewer’s visual system and intuitive understanding of the difference between ‘broad patterns of wind versus delicate things’. This particular map came to be used professionally by farmers, and scientists who observed bird migrations and butterfly migrations, and teachers and school children to learn forecasting. Cameron Beccario, a software engineer adapted this tool to scale it to the entire earth at different levels going up to the stratosphere, creating greater accessibility to the data for purposes such as aerial navigation.
There were a lot of decisions we made in this visualisation – design decisions. We’re not using color, for instance. We’re not showing pressure or temperature. We’re not drawing (geopolitical) boundaries on the map. We wanted this to be as unobtrusive as possible. We wanted you to see the shape because that’s what we wanted to see and then, people started using it in really unexpected ways.
It speaks to the power of just making complex data easily accessible. How can you make anyone digest and interact with complex data. This is one of the aspects of data visualisation that’s near and dear to us.
Design and the Just City in NYC exhibit that opened in early January this year to the public nudges the audience to think of the city through the lens of justice. Featuring research from Harvard GSD’s Just City Lab led by Toni Griffin, it examines a values-driven approach to urban planning and design to address conditions of injustice in the city.
Imagine that the issues of race, income, education, and unemployment inequality, and the resulting segregation, isolation, and fear, could be addressed by planning and urban design. Would we design better places if we put the values of equality, equity, and inclusion first? If communities articulated what they stood for, what they believed in, and what they aspired to be, would they have a better chance of creating healthy and vibrant places?
The project invariably draws one’s attention to the nature of data that informs urban planning processes, moving away from traditional statistical models towards humane, intentional, complex and nuanced forms of city data.
Situated in a cozy, reflective nook at the Center for Architecture, the exhibit engages the visitors in rethinking their neighborhoods and places in terms of the values they represent.
A large walled map of New York City is a canvas for New Yorkers to collectively aggregate these values and add their dimensions to the city.
Post-truth politics is a “political culture in which debate is framed largely by appeals to emotion disconnected from the details of policy”. The narrative takes center stage and factual rebuttals to the political agenda are consciously disregarded. Demagoguery assumes the role of the protagonist. Fake news, Disinformation and Hoaxes become the setting. And, the Internet is the theatre. Since 2016 however, these post-truth plays have been largely harbored by Twitter.
In a study commissioned by the Knight Foundation, Matthew Hindman of George Washington University and Vlad Barash of Graphika examined how ‘fake news’ actually spread across tweets in the months preceding and post the 2016 US presidential elections.
In a vivid interactive by Accurat that carefully captures the fluid temporality of the twitterverse, the study depicts more than “10 million tweets from 700,000 Twitter accounts that linked to more than 600 fake and conspiracy news sites.”
One myth that this study debunks is that fake news is spread by thousands of small, independent sites when in reality, it is largely concentrated around a handful of websites, and in the case of the 2016 elections, 24. A pattern that seems to run throughout these coordinated twitter campaigns is that of Clusters, a network of twitter users who inter-tweet and inter-link to disinformation from these sources. These accounts, a whole 13861 of them, the study samples as the most crucial in spreading fake news, half of which were found to be automated based on their posting cycles.
“Climate Change is an abstraction. We read about it, but it’s happening too slowly for us to perceive, and so it’s hard to accept it as something that’s real and urgent. This artwork is an attempt to give a ‘voice’ to a specific glacier in the hopes of making climate change something we can hear and see — something that feels real.” — Ben Rubin
It’s hard to visualise mental illness in the same way we might visualise physical disabilities, and this can make it difficult for people with no experience of mental health problems to empathise or imagine how they affect peoples’ lives.
“Waterways get polluted. But they can also be cleaned up. It’s a process that’s reversible… Data forces you to work with constraints, at the same time as it gives unexpected results and surprises,” Kildall says.
A neat little fact about the solar system demonstrated in animation. Did you know that the radius of the Earth in ratio to the radius of the Moon is 3:11? This remarkably means that If you create a square of a side length equivalent to Earth’s diameter, it will have the same perimeter length as a circle of diameter equivalent to the Earth and Moons’ diameters together.
Frick imagines a future in which your smart watch will know how your body is responding to someone. Then it will combine with Facebook data about their personality. And that will let you know whether that person makes you lethargic, raises your blood pressure or depresses you.
Let’s retrieve your listening history of the last 100 days. Nothing that the recent GDPR wouldn’t allow though! Just the precise date, genre of the songs listened to and how many times you’ve listened to the same song/artist.
Upcoming Events in New York City:
1. Making Art in the Age of Algorithms Symposium– Friday, December 7, 2018 | Register
2. Culture Shifts in Social Data – How Brands Learn and React to Global Shifts in Thinking – Tuesday, December 11, 2018 | Register
3. NYC #OpenData 102 — Unlocking Open Data through data journeys – Thursday, January 17, 2018 | Register