The Emergence of Computational Creativity
Computers have long been used to create visual art and paintings. The latter is an interesting extra hurdle, as most creative domains produce mainly digital output. Creating a physical painting, in contrast, involves applying color to a canvas, requiring the computer to interact with the physical world through robotics.
One of the best-known painting programs is AARON, created by the late Harold Cohen. Cohen started developing it in 1973 and worked on it for decades, often altering AARON’s output methods which ranged from plotting to inkjet printing. As a pioneer in the field, Cohen provided valuable insights into the early breakthroughs:
“But when I started thinking about color and how a computer program might handle it and trying to do that in the classic AI terms of modeling my own behavior, I was up against a brick wall. I didn’t know how to proceed. And it was only finally when I realized — what seemed very obvious once I realized it — that machines and human beings a really quite different entities, that a program has one set of capabilities that don’t correspond at all to human beings’ capabilities, then I started to make progress.”
The University of Konstanz in Germany has also developed a painting robot called e-David, which uses visual sensors to create a “visual control loop” that allows the system to correct its own mistakes during the painting process.
However, the more fundamental progress in computational creativity has been on the software side, particularly with the recent advancements in machine learning. A team of researchers from Rutgers University and Facebook’s AI lab built an artificial intelligence system that aims to create pictures with an original style. Their “creative adversarial network” (CAN) consists of two neural networks – one that generates the pictures and another that evaluates them against criteria for “art or not art” and “style ambiguity.”
The results show that the CAN system is able to generate images that humans have a hard time distinguishing from human-created abstract expressionist art. Interestingly, respondents rated the computer-generated images as more “intentional, visually structured, communicative, and inspiring” than those created by real artists.
Computational Creativity in Game Development
Video games, in contrast to paintings, are entirely digital products focused on efficient creation. While the artist’s narrative and persona are crucial in fine art, most people buy games for reasons other than the creator’s identity. As such, artificial intelligence and computational creativity have played a bigger role in the gaming industry.
Procedural generation is a computational creativity technique that game developers have employed since the 1980s. The most common use case is the random creation of a game’s environment, such as maps and levels. Titles like Rogue, Elite, Diablo, Sim City, and Civilization have all used procedural generation.
The most prominent recent example is No Man’s Sky, which contains 18 quintillion algorithmically generated planets for players to explore. Creating such a vast universe by hand would have taken a large team a very long time. However, the true challenge lies in teaching the algorithm the difference between good and bad game design:
“The true craft of making a game like No Man’s Sky was creating a system from which a variety of interesting results can emerge, but with no boring or obtuse results.”
AI is also being used in other areas of the game design process, such as balancing game mechanics. Veteran game designer Paul Tozour explained how machine learning-based approaches can help:
“You may not be able to write a fitness function to put an exact number on entertainment value, but if you can state your design goals clearly, then you can very often write a fitness function that measures whether some part of your game satisfies them.”
Going a step further, researchers are also working on algorithmically generated games. Michael Cook, a senior research fellow at Falmouth University’s MetaMakers Institute, developed a program called ANGELINA that autonomously creates entire games, including the setting and mechanics. While the results are still crude, the motivation is clear:
“AI will invent genres that humans could never have possibly conceived of, I believe… One day people will steal ideas from software, not because they want the fame or the pride, but because it’s the current mobile trend and it’s too good not to steal.”
The catch is that AI and human designers need to work together for the best results. As Cook notes, “Computers are quite good at considering options equally… They can’t forget things, they don’t get tired, they don’t get confused by emotional needs.” However, human creativity and intuition are still essential to guide the process.
The Value of Humanity in an AI-Driven Creative World
The rapid progress of computational creativity, particularly in areas like generative art and procedural storytelling, has understandably sparked concerns and debates within the creative industries. Many professional artists fear that AI will soon be able to produce work indistinguishable from human-created art.
However, there is still inherent value in human-created art that AI cannot easily replicate. The “human experience” and “human effort” behind a work of art are what give it deeper meaning and emotional resonance. Hyperrealistic art, for example, is valued not just for its realism, but for the mastery and training required to create it by hand.
Similarly, in the field of chess, AI systems have long surpassed the abilities of human grandmasters. Yet chess remains incredibly popular, with human players still celebrated as regional celebrities. Viewers are not interested in watching two AI engines play against each other, but rather in the human drama and creative expression of a skilled player.
As AI continues to advance, the value of art and creativity will adapt. Humans will find new ways to express their unique perspectives and experiences through creative mediums. The “democratization of the expressive medium” brought about by AI tools may actually expand opportunities for more people to engage in creative pursuits, even if the quality of the output is variable.
Moreover, AI can serve as a collaborator, empowering human artists and creators rather than replacing them. Techniques like style transfer, where an AI system can apply the style of one artist to the work of another, open up new creative possibilities. Exploring these human-AI partnerships will be an exciting frontier for the future of creative industries.
Conclusion: Embracing the AI-Driven Creative Landscape
The rapid progress of computational creativity, from generative art to procedural storytelling, is undoubtedly transforming the creative industries. While this has sparked fears among some professionals, it also presents tremendous opportunities to expand the horizons of human creativity.
By understanding the unique strengths of both human and machine creativity, we can find ways for them to complement each other. AI can automate certain tasks, free up human creators to focus on higher-level expression, and even inspire new creative directions that would be difficult for humans to conceive alone.
As AI systems become increasingly sophisticated, the creative landscape will continue to evolve. Rather than viewing this as a threat, we should embrace the changes as a chance to redefine the value of human creativity and find new modes of artistic expression. The future of creative industries lies in the seamless integration of computational and human creativity – a future that is already unfolding before our eyes.
