Previously it was thought that no robot could create anything that is related to creativity or art. However, scientists of Konstanz University have created a robot artist that paints on white paper with its digital painter program. Its name is eDavid and it works just like a human painter. It is a combination of digital computers, normal robotic arm and a camera. Image: Biomorphic Robot Action Painting Performance by Tom Estes at the exhibition Ultraviolet Sun
There is a popular meme in tech and economics right now: the idea that technology — or robots specifically — will take our jobs and put us all out of work. The technology is here. But the jobs are nowhere to be found. Thanks to the efficiency of the internet and automated systems, productivity and GDP have grown during the last few decades, but the middle class and jobs are disappearing. But what about artists? Can art made by humans be replaced by machines? Creativity is one of humanity’s uniquely defining qualities. Numerous thinkers have explored the qualities that creativity must have, and most pick out two important factors: whatever the process of creativity produces, it must be novel and it must be influential.
The history of art is filled with good examples in the form of paintings that are unlike any that have appeared before and that have hugely influenced those that follow. Leonardo’s 1469 Madonna and child with a pomegranate, Goya’s 1780 Christ crucified or Monet’s 1865 Haystacks at Chailly at sunrise and so on. Others paintings are more derivative, showing many similarities with those that have gone before and so are thought of as less creative.
The job of distinguishing the most creative from the others falls to art historians. And it is no easy task. It requires, at the very least, an encyclopedic knowledge of the history of art. The historian must then spot novel features and be able to recognize similar features in future paintings to determine their influence.
Creativity is one of humanity’s uniquely defining qualities. Numerous thinkers have explored the qualities that creativity must have, and most pick out two important factors: whatever the process of creativity produces, it must be novel and it must be influential. Image: Biomorphic Robot Action Painting Performance by Tom Estes at the exhibition Ultraviolet Sun
Making artistic decisions are a tricky tasks for a human and until recently, it would have been unimaginable that a computer could take them on. But today that changes thanks to the work of Ahmed Elgammal and Babak Saleh at Rutgers University in New Jersey, who say they have a machine that can do just this.
They’ve put it to work on a database of some 62,000 pictures of fine art paintings to determine those that are the most creative in history. The results provide a new way to explore the history of art and the role that creativity has played in it.
Several advances have come together to make this advance possible. The first is the rapid breakthroughs that have been made in recent years in machine vision, based on a way to classify images by the visual concepts they contain.
These visual concepts are called classemes. They can be low-level features such as color, texture, and so on, simple objects such as a house, a church or a haystack and much higher-level features such as walking, a dead body, and so on.
This approach allows a machine vision algorithm to analyze a picture and produce a list of classemes that describe it (up to 2,559 different classemes, in this case). This list is like a vector that defines the picture and can be used to compare it against others analyzed in the same way.
Technology is going to hit every corner of human civilization. This approach is not just limited to art. Elgammal and Saleh point out that it can also be used to explore creativity in literature, sculpture, and even in science. Image: Biomorphic Robot Action Painting Performance by Tom Estes at the exhibition Ultraviolet Sun
The second advance that makes this work possible is the advent of huge online databases of art. This is important because machine visions algorithms need big databases to learn their trade. Elgammal and Saleh do it on two large databases, one of which, from the Wikiart website, contains images and annotations on some 62,000 works of art from throughout history.
The final component of their work is theoretical. The problem is to work out which paintings are the most novel compared to others that have gone before and then determine how many paintings in the future have uses similar features to work out their influence. Elgammal and Saleh approach this as a problem of network science. Their idea is to treat the history of art as a network in which each painting links to similar paintings in the future and is linked to by similar paintings from the past.
New developments in artificial intelligence and it’s relationship to art is acted out in a Live Art Performance by Tom Estes at The Venice Biennale. While computers are beginning to act more and more like humans, artist Tom Estes reverses this relationship by acting like a computer program.
The problem of determining the most creative is then one of working out when certain patterns of classemes first appear and how these patterns are adopted in the future. “We show that the problem can reduce to a variant of network centrality problems, which can be solved efficiently,” they say.
In other words, the problem of finding the most creative paintings is similar to the problem of finding the most influential person on a social network, or the most important station in a city’s metro system or super spreaders of disease. These have become standard problems in network theory in recent years, and now Elgammal and Saleh apply it to creativity networks for the first time.
The artist Tom Estes acts out the emergence of a new intelligence by wearing the “H” from the hologram “Rimmer” in the British sci-fi TV comedy Red Dwarf and the personality disc of the characters called ‘programs’ found in the film Tron.
The results of the machine vision algorithm’s analysis are interesting. The figure above shows artworks plotted by date along the bottom axis and by the algorithm’s creativity score on the vertical axis.
Several famous pictures stand out as being particularly novel and influential, such as Goya’s Christ crucified, Monet’s Haystacks at Chailly at sunrise and Munch’s The Scream. Other works of art stand out because they are not deemed creative, such as Rodin’s 1889 sculpture Danaid and Durer’s charcoal drawing of Barbara Durer dating from 1514.
Many art historians would agree. “In most cases the results of the algorithm are pieces of art that art historians indeed highlight as innovative and influential,” say Elgammal and Saleh.An important point here is that these results are entirely automated. They come about because of the network of links between paintings that the algorithm uncovers. There is no initial seeding that biases the search one way or another.
Of course, art historians will always argue about exactly how to define creativity and how this changes their view of what makes it onto the list of most creative. The beauty of Elgammal and Saleh’s techniques is that small changes to their algorithm allow different definitions of creativity to be explored automatically. This kind of data mining could have important impacts on the way art historians evaluate paintings. The ability to represent the entire history of art in this way changes the way it is possible to think about art and to discuss it. In a way, this kind of data mining, and the figures that represent it, are new instruments of reason for art historians.
And this approach is not just limited to art. Elgammal and Saleh point out that it can also be used to explore creativity in literature, sculpture, and even in science. In fact, we have reached a tipping point where technology is now destroying more jobs than it creates. And if the trend continues we could face a serious crisis, said Wendell Wallach, a consultant, ethicist, and scholar at the Yale University Interdisciplinary Center for Bioethics. Robots, 3D printing, and other emerging technologies are all fueling technological unemployment and global wealth disparity. Technological unemployment is the concept of technology killing more jobs than it produces. While that fear has been considered a Luddite fallacy for the past 200 years, it is now becoming a stark reality.
“This is an unparalleled situation and one that I think could actually lead to all sorts of disruptions once the public starts to catch on that we are truly in the midst of technological unemployment,” Wallach said during a presentation at the Carnegie Council for Ethics and International Affairs.
It is true that tech companies employ fewer people directly. But they create ecosystems that employ more people indirectly. Think of Facebook, which has more than 2 million advertisers. Or the hundreds of thousands of sellers on eBay and Amazon. The mobile app industry alone is now bigger than the entire movie industry. Google alone is now bigger than the entire newspaper and magazine ad industry. Employment obeys its own cycles governed by overall economic growth, not tech. The most frustrating thing about the “robots are taking our jobs!” meme is that it feels true on an anecdotal basis. You can draw that blue trend line arrow wherever you want, of course. It is true that new tech may destroy jobs temporarily. But Tech jobs also tend to be better paid than the old jobs, too. Remember all those people who used to be employed making beepers? All those jobs are gone but the workers who did those beeper jobs are not unemployed. Society is not overrun by an army of destitute beeper assembly workers. In 2008,The Times named Science Fiction writer Iain Banks in their list of “The 50 greatest British writers since 1945. In this interview he sums it up quite nicely:
“People still buy paintings even though the camera was invented… people still go the the theater despite the invention of cinema. I don’t think any of these things necessarily mean the end to what came earlier… perhaps it is best to see it as an opportunity rather than a threat.“