Generative art fits well with the NFT. Here’s why

The market for NFTs in the art world has grown rapidly in recent years, and it is expected to continue to expand in the future. After launching my book NFT: From Zero to Hero, I have given many talks and held several AMA (Ask Me Anything) sessions. One topic that came up frequently during these sessions was the topic of generative art and NFTs. Specifically, many people have asked about the definition of generative art, the value behind these NFTs, their longevity, and how we can appreciate this form of art. In this statement, I will do my best to provide answers to these questions.

Generative art is a form of art that is created using algorithms and computer programs. It is an art form that is created using mathematical rules and procedures rather than being made by the artist’s hand. The computer generates the artwork, and the artist’s role is to create the rules and parameters that the computer uses to generate the final piece.

Understanding generative art

Appreciating generative art can be similar to enjoying other forms of art, but there are also some unique aspects to consider. Some people might find it visually appealing or thought-provoking, while others might find it challenging or confusing. The value of generative art, like any other form of art, is subjective and can depend on many factors, such as the artist’s intent, the complexity of the algorithm used, the uniqueness of the piece, and the social or historical context in which it was created. Here are a few tips for approaching and understanding generative art:

Consider the process: Generative art is created using algorithms and computer programs, so it can be interesting to think about the process that was used to create the artwork. What parameters and rules were set by the artist? How does the algorithm determine the final output?

Look for patterns and variations: Because generative art is created using a set of rules, there may be repeating patterns or variations within the piece. Observing these patterns and variations can provide insight into how the artwork was created.

Think about the relationship between the artist and the algorithm: Unlike traditional art, where the artist has complete control over the final output, the artist in generative art is also a programmer, so the relationship between the artist and the algorithm that creates the artwork is unique, consider how the artist influences the algorithm and how the algorithm influences the final result.

Consider the concept: As with any art, the idea behind generative art is also essential. What themes or ideas are being explored? What is the artist trying to communicate through the use of algorithms and computer programs?

Be open-minded: Generative art can be very different from traditional art forms, so it can be helpful to approach it with an open mind and be willing to consider new perspectives.

By considering these factors, you can gain a deeper understanding and appreciation of generative art and its unique characteristics.

The value behind generative art

The value behind generative art can vary depending on the artist and the specific piece, but generative art is generally valued for its unique combination of technology and creativity. Some of the critical aspects that contribute to the value of generative art include:

The use of technology: Generative art relies on using algorithms, code, and other forms of technology to create artwork. This can create a sense of novelty and innovation and a level of complexity that is impossible with traditional art forms.

The artist’s intent: Like any other art form, the artist’s goal is vital in understanding the value of generative art. What is the artist trying to communicate through the use of technology and algorithms?

The element of chance: Generative art often involves algorithms that create unexpected and random outcomes. This can create a sense of surprise and intrigue and make each generative art unique.

The aspect of collaboration: Generative art can be seen as a collaboration between the artist, who sets the rules and parameters, and the algorithm, which generates the final output. This creates a sense of interdependence and relationship between the artist and the technology.

The concept of digital scarcity: The use of non-fungible tokens (NFTs) in digital art allows for creating unique digital assets, which can be bought and sold like physical artworks. This creates a sense of digital scarcity and uniqueness, which adds value to the artwork.

The ability to generate new and dynamic works: Generative art algorithms can be set to run indefinitely, generating new variations of the same artwork, making it a dynamic and ever-changing medium.

The value of generative art is subjective and can depend on various factors. However, by considering the use of technology, the artist’s intent, the element of chance, the concept of digital scarcity, the aspect of collaboration and the ability to generate new works, one can gain a deeper understanding of the value behind generative art.

Which generative artist am I following?

Many talented artists are working in the field of generative art, but here are a few who have gained recognition for their work:

1. Joshua Davis: Joshua Davis is a pioneer in generative art, and his work often combines programming, design, and animation. He is known for creating complex and detailed digital images using algorithms and code.
2. Golan Levin: Golan Levin is an artist and designer whose work spans a variety of mediums, including generative art, digital fabrication, and interactive installations. He is known for creating interactive pieces that respond to user input and for using code to create visuals.
3. Zach Lieberman: Zach Lieberman is an artist and programmer whose work often involves the use of technology to create interactive and generative art. He is known for his use of open-source programming tools and his ability to create complex and dynamic visuals using code.
4. Rafael Lozano-Hemmer: Rafael Lozano-Hemmer is an artist who creates interactive installations that use technology to allow viewers to interact with the artwork. His work often involves the use of generative algorithms to create dynamic and responsive visuals.
5. David McLeod: David McLeod is an artist and developer based in New York. He is known for his generative artworks that explore the intersection of art and technology. His work often combines generative algorithms, machine learning, and data visualisation.
6. Beeple: Mike Winkelmann, also known as Beeple, is a digital artist and graphic designer who creates both generative and non-generative digital art. He is known for creating unique, one-of-a-kind digital images and animations, and his digital artwork is highly sought after by collectors.
7. Tyler Hobbs: Tyler Hobbs is an artist and software engineer who creates digital art using algorithms and code. He is known for his abstract and dynamic visual creations that often feature patterns and shapes generated through complex mathematical processes.

There are many more talented artists working in the field, and the field is growing. New artists are emerging, and their works are gaining recognition. These are just a few examples of famous generative artists I follow.

How to create generative art?

There are many ways to create generative art, as the term encompasses various techniques and technologies. Here are a few standard methods:

Algorithmic art: This form of generative art uses mathematical algorithms or equations to create images or animations. Artists can use programming languages such as Python or JavaScript to write code that generates visual elements based on specific parameters or rules.

Data visualisation: This form of generative art uses data sets to create visual representations of information. Artists can use tools such as Processing, D3.js, or Tableau to create interactive visualisations that allow viewers to explore data in new and meaningful ways.

Neural networks: This generative art form uses machine learning algorithms to create images or animations. Artists can use frameworks such as TensorFlow or PyTorch to train neural networks on image data sets and then use the trained networks to generate new images.

Randomness: This form of generative art uses randomness as the primary driver of image creation. The artist can use different techniques like fractals, cellular automata, or Perlin noise to create complex and unique images based on random inputs.

Hybrid methods: Artists can combine different techniques and technologies to create generative art. For example, an artist might use a neural network to generate an initial image and then use algorithmic techniques to refine or manipulate the image further.

It’s important to note that generative art is a form of digital art and requires some knowledge of programming and an understanding of algorithms and data visualisation.

This is an example of a simple program for generating generative art, but the program’s specifics will depend on the desired outcome and the tools you are using. Here is an example of a Python program that generates a random geometric pattern using the library “Bird”:

The code looks like this:

import bird

import random

 

def generate_art():

   # Set up the bird

   t = bird.Bird()

   t.speed(0)

   t.penup()

   t.goto(-150, -150)

   t.pendown()

 

   # Generate the geometric pattern

   for i in range(50):

       # Choose a random color

       t.color(random.random(), random.random(), random.random())

       

       # Choose a random size and direction

       size = random.randint(10, 50)

       direction = random.randint(0, 360)

       

       # Draw the shape

       t.left(direction)

       t.forward(size)

       t.right(direction)

       t.forward(size)

       t.right(direction)

       t.forward(size)

       t.right(direction)

       t.forward(size)

       t.right(direction)

 

generate_art()

bird.done()

Generative art can take many forms, including digital images, animations, and sculptures. It can also be interactive, changing in response to the viewer’s actions.

 

Generative art and NFT

Generative art has the potential to offer a unique and dynamic experience for the viewer, as the artwork can change over time and can be different every time it is viewed. It also allows for an almost infinite number of variations and iterations, so it is possible to create a series of unique artworks from a single set of rules and parameters. This means that the artwork can evolve and adapt to its environment or respond to the viewer’s actions in real time.

The potential of generative art in the context of non-fungible tokens (NFTs) is significant because it allows for creating and selling unique, one-of-a-kind digital artworks. NFTs are digital assets that are stored on a blockchain, a secure and transparent digital ledger, which allows for the verification of ownership and authenticity of digital artworks.

Using NFTs in generative art allows artists to sell their artworks as unique, one-of-a-kind assets rather than just digital copies. This means the artwork can be owned, collected, and traded like traditional physical artworks. Additionally, NFTs enable the artist to set their own terms and conditions for the artwork’s use and distribution, which can give them more control over their creations. Furthermore, NFTs can also provide a new way for artists to monetise their work, as they can sell their artwork as NFTs and get compensated for each transaction. This can be especially beneficial for generative artists, as their artwork can be sold multiple times, providing them with a new revenue stream.

In conclusion, integrating NFTs into generative art is a promising development that can revolutionise how artists monetise their work and how digital art is collected and traded. It is an excellent way for artists to showcase their skills and creativity and for collectors to invest in unique and valuable digital assets. The use of NFTs in generative art is helping to push the boundaries of what is possible in the world of digital art and is helping to bring more recognition to this exciting and dynamic field.

We are also sharing with the world the potential of Web3 and AI. Let’s continue to BUILD.