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Adding to the history section:

Rational Approaches to Art
While art has strong emotional and psychological ties, it also depends heavily on rational approaches. Artists have to learn how to use various tools, theories and techniques to be able to create impressive artwork. Thus, throughout history, many art techniques were introduced to create various visual effects. For example, Georges-Pierre Seurat invented pointillism, a painting technique that involves placing dots of complimentary colors adjacent to each other. Cubism and Color Theory also helped revolutionize visual arts. Cubism involved taking various reference points for the object and creating a 2-Dimensional rendering. Color Theory, stating that all colors are a combination of the three primary colors (Red, Green and Blue), also helped facilitate the use of colors in visual arts and in the creation of distinct colorful effects. In other words, humans have always found algorithmic ways and discovered patterns to create art. Such tools allowed humans to create more visually appealing artworks efficiently. In such ways, art adapted to become more methodological

Creating Perspective Through Algorithms
Another important aspect that allowed art to evolve into its current form is perspective. Perspective allows the artist to create a 2-Dimensional projection of a 3-Dimensional object. Muslim artists during the Islamic Golden Age employed linear perspective in most of their designs. The notion of perspective was rediscovered by Italian artists during the Renaissance. The Golden Ratio, a famous mathematical ratio, was utilized by many Renaissance artists in their drawings. Most famously, Leonardo DaVinci employed that technique in his Mona Lisa, and many other paintings, such as Salvator Mundi. This is a form of using algorithms in art. By examining the works of artists in the past, from the Renaissance and Islamic Golden Age, a pattern of mathematical patterns, geometric principles and natural numbers emerges.

Contemporary Algorithmic art:

The Necessity of Algorithmic Art
In modern times, humans have witnessed a drastic change in their lives. One such glaring difference is the need for more comfortable and aesthetic environment. People have started to show particular interest towards decorating their environment with paintings. While it is not uncommon to see renowned, famous oil paintings in certain environments, it is still unusual to find such paintings in an ordinary family house. Oil paintings can be costly, even if its a copy of the painting. Thus, many people prefer simulating such paintings. With the emergence of Artificial Intelligence, such simulations have become possible. Artificial Intelligence image processors utilize an algorithm and machine learning to produce the images for the user.

Studies on Algorithmic and Generative Art
Recent studies and experiments have shown that Artificial Intelligence, using algorithms and machine learning, is able to replicate oil paintings. The image look relatively accurate and identical to the original image. Such improvements in Algorithmic Art and Artificial Intelligence can make it possible for many people to own renowned paintings, at little to no cost. This could prove to be revolutionary for various environments, especially with the rapid rise in demand for improved aesthetic. Using the algorithm, the simulator can create images with an accuracy of 48.13% to 64.21%, which would be imperceptible to most humans. However, the simulations are not perfect and are bound to error. They can sometimes give inaccurate, extraneous images. Other times, they can completely malfunction and produce a physically impossible image. However, with the emergence of newer technologies and finer algorithms, research are confident that simulations could witness a massive improvement. Other contemporary outlooks on art have focused heavily on making art more interactive. Based on the environment or audience feedback, the algorithm is fine-tuned to create a more appropriate and appealing output. However, such approaches have been criticized since the artist is not responsible for every detail in the painting. Merely, the artist facilitates the interaction between the algorithm and its environment and adjusts it based on the desired outcome.