Artificial intelligence (AI) has made remarkable strides in creating images that are not only stunning but stunningly different in style. Ten years ago, such an achievement would have been considered improbable by experts. Today, AI can create images using specific artistic styles, such as Van Gogh's unique approach, with an infinite range of variations.
This raises an intriguing question. How can a series of instructions running on a computer produce art that rivals human creativity?
Art history and AI
Examining the similarities between the development of AI andart historycan provide some answers.
The history of modern science can be characterized by the development of specific models that represent the world. Traditional models are developed using mathematical equations, physics and logic. For example, Newton's law describes how gravity works with a very simple formula.
However, modern artificial intelligence has become increasingly dependent on generic models that can learncomplex relationshipsfrom large datasets. They do this without encoding explicit knowledge (so-called artificial neural networks). These new models do not rely on physics or complex mathematical equations, but rather are built by assembling many small computational units. As a whole, these can learn and reproduce any pattern present in the data.
Interestingly, the evolution of art mirrors AI in many ways. Art has also undergone a significant transformation, from being rooted in explicit knowledge and classical traditions to embracing approaches that challenge the boundaries of art itself.
Like artificial intelligence, art has evolved to incorporate a more organic and intuitive approach that emphasizes the discovery and creation of new forms and styles.
The time to build accurate models
For decades, engineered AI models relied on analytical solutions and equations. These were defined by a handful of meaningful parameters devised by human experts. The primary purpose of this early iteration of AI was to adjust these parameters to explain experimental data.
Artists also used models and adapted them to represent what they observed. Their models came from the careful study of anatomy, color and form. For example, during the Renaissance, da Vinci dedicated himself to studying the human form by performing dissections of humans and animals. He constructed a mental model of what a human body should look like, which was then used to accurately reproduce characters or imagine allegorical religious paintings.
In 2004, anatomy professors Massimo Gulisano and Pietro Bernabei used computers to analyze Michelangelo's David, confirming the extreme anatomical accuracy of the sculpture (except for a missing muscle, which on examination was due to the imperfection of the stone).
These mental models became more sophisticated during the 16th century, when artists perfected the texture and vibrancy of fabrics, water, and light.
The data-driven era
With advances in computing, new methods emerged that enable AI to recognize sophisticated patterns in large data sets. ONEwatershed momenttook place in 2012 when computer scientiststrained a large, deep convolutional neural networkto recognize the content of images. It outperformed existing methods by a wide margin. The research community suddenly realized the potential of data-driven approaches.
Ever since, increasingly complex models have been learning more sophisticated patterns from ever larger datasets. It seems there is no end to what enough data and a large model can achieve with enough resources.
In the 19th century, artists began to paint what they saw, refusing to be limited by the subject matter that gave rise to the Impressionist art movement. This is very similar to a data-driven approach: reproduce the data (observed scene) without trying to use models of the subject.
In 1874, Claude Monet's defining Impression, Sunrise was exhibited in Paris. It was derided by art critics as a personal 'impression' rather than a realistic representation of the scene in question.
This was the seed of the impressionism movement and is often considered a watershed moment in art history. Others soon realized how powerful art could be when the constraints of realism were lifted.
The reason for this development
While the Internet and advanced computing disrupted AI, the technology also disrupted the art world.
For art it was photography. Until the 19th century, the only way to produce an image was through the work of artists. There was a huge demand to immortalize events, individuals or places.
By the end of the 19th century, photography was good enough to meet this demand, and the art was disrupted. Art evolved to produce "impressions" or abstract images, leaving the job of documenting reality to photographs.
The modern era
The development of art is reflected even today in how drawing is taught.
One approach is to draw only what you see, without reference to any model, which is deceptively challenging. Another involves studying anatomy, basic shapes and how light interacts with shapes so that one can draw a model using the scene simply as a reference and not as something to reproduce exactly.
Over time, artists blend these techniques with others to develop their personal approaches. However, the duality between model-based and data-driven approaches is still clear and ever-present.
For example, many artists strive to identify the essence of things or feelings, a technique well illustrated by Picasso's bull study. This is a key theme in AI, which reduces the complexity of very large models to identify the core architecture, generating the simplest possible model.
As methods have blended in the modern art world, so too have many researchers pushed for a blended approach to AI, infusing data-driven models with symbols and physics. The goal is to strike a balance between the interpretability of symbolic AI and the scalability and power of data-driven methods.
By combining these techniques, researchers hope to create more robust and adaptable AI systems that can handle complex and diverse real-world problems.
Meanwhile, art continued to build on impressionistic freedom. This resulted in an explosion in techniques and movements throughout the 20th century that continues unabated to this day.
This is not unlike the way modern AI systems can create realistic and beautiful images.
Patterns in images can be learned by AI, and patterns from these patterns, to such an extent that any image can be understood as a hierarchical collection of patterns. All originated from a large library learned by the AI system.
The era of generative models
Fast forward to 2023, and generative AI methods have been developed to assemble patterns in new ways to create never-before-seen images. The selection and ways these patterns are combined are informed by prompts. These can range from human-defined text prompts, rough sketches and audio to even the movements of a crowd, as in the case of Refik Anadol's Unsupervised. The AI-powered digital display constantly creates a new image depending on the viewers' location.
While generative artificial intelligence is undoubtedly impressive, it presents several challenges. These include copyright ownership, deeply fake images and videos, and AI-generated works that flood art websites with an endless supply of new works.
Moreover, this technology raises deeper questions about the nature of creativity and human experience.
As we witness a world increasingly dominated by AI-generated art, we must wonder if anything new can ever be created again, or if what we consider new art is just an improbable and interesting coincidence fluctuations that happen to resonate with us. After all, what people generate is often a complex collection of experiences and existing pieces assembled in new and innovative ways.
While AI's ability to create art that rivals human creativity remains a subject of debate, the evolution of AI and art share remarkable similarities.
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