Art has been part of our culture for thousands of years as a form of expression, representing different generations of our culture. As our culture changes, the art that it produces changes as well, letting us see the projection of the ideas of the time. Nowadays, we live in a generation with technology that is able to create art by itself, or generative art.
What is generative art?
Generative art is essentially art created by an autonomous system, such as a machine. The way in which generative art is created can differ, usually through random generations or certain algorithms used to make patterns.
Although it would seem that the autonomous system does everything in the making of the generative art, we need to remember that these systems need to be coded by someone, and because of that, the author can always control what kind of art is generated. Simplicity and complexity are up to the author of the art.
A brief history of generative art
The first examples of generative art date back to the 1960s with artists like Georg Nees in 1968. Nees’ piece titled ‘Schotter’ had a 12 row grid with increasing random rotation. This piece could have also easily been adjusted with a few changes in code, but it is an impressive beginning to the field.
Around those years, Frieder Nake, Michael Noll, and Georg Nees were exploring the potential of generative art, and they were not the only ones that were pioneering generative art at the time. Vera Molnár and Lillian Schwartz also got involved in the process of generative art, making their own creations as a way to express their visions.
There were also some interesting events in the history of generative art, such as the creation of the platform of "Design By Numbers '', by John Maeda, which was a platform made so that artists and designers could explore programming. This platform grew into what today is called "Processing", which had the same concept of learning to code in the context of visual arts, making the generative art more accessible to everyone. That was a big accomplishment in the history of generative art, as those programs grew with time to be able to handle a lot more work and therefore more complicated algorithms.
But it got even more interesting with the introduction of Artificial Intelligence to generative art. In 2014, Ian Goodfellow came up with the idea of GANs, which were composed of two neural networks, which were programs designed to work to think like a human brain. Basically, these programs are made to train themselves to make better art, or at least good enough to give the illusion of reality.
In this particular example, we use custom code to generate either lines or circles; but we spiced things up a bit by adding depth, transparency and clusters. The depth is random and gets rendered with gaussian blur to create the illusion of depth. Transparency is arbitrarily applied to elements, makes it more interesting. Finally, to produce some more noticeable structure, we randomly generate areas of interest and cluster the lines and circles inside them. There are many other techniques to consider, if you are interested in learning more, take a look at the book Generative Art: A Practical Guide Using Processing
Uses of generative art
Generative art may not be replacing human artists any time soon, but it can lead to very interesting visual effects, especially with more complex algorithms. But it doesn't stop there, as much as it can be used for the love of the art itself, it can also be used as a potential candidate to generate the profitable yet controversial NFTs, which can be sold on places such as opensea.io
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