What is AI Art and How is it Created? | Definition from TechTarget – TechTarget

You forgot to provide an Email Address.
This email address doesn’t appear to be valid.
This email address is already registered. Please log in.
You have exceeded the maximum character limit.
Please provide a Corporate Email Address.
Please check the box if you want to proceed.
Please check the box if you want to proceed.
By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.
AI art (artificial intelligence art) is any form of digital art created or enhanced with AI tools. Though commonly associated with visual art — images and video, for example — the term AI art also applies to audio compositions, including music.
Since the earliest pictures found on cave walls, human creativity alone has driven art’s history. Inspired humans using hand-held tools — musical instruments or paintbrushes — generated all manner of art throughout recorded history. AI art shatters that paradigm.
Using machine learning algorithms, computer technology — trained on a body of art to learn what art is and how to describe it — applies various techniques, such as a generative adversarial network (GAN), to change or enhance existing human creations or generate entirely new works of art.
AI art challenges the millennia-old requirement of humans as the sole creators of art. Its introduction raises questions about the genesis of creativity and carries ethical and legal concerns. It’s also an opportunity to extend the boundaries of art — and creativity — in many ways.
This article is part of
AI art allows anyone to create works or even entire collections of art, but in a small fraction of the time non-AI methods afford. In addition, AI art can create visual or audio compositions that would be difficult to create otherwise. With text-to-image generative AI tools, such as Dall-E or Stable Diffusion, humans no longer need to attempt to draw the image they want; they simply type a text prompt into the tool, which generates the desired imagery.
The earliest iterations of AI art appeared in the late 1960s, with the first notable system appearing in 1973 with the debut of Aaron, developed by Harold Cohen. The Aaron system was an AI assistant that used a symbolic AI approach to help Cohen create black-and-white art drawings.
AI-generated art began its recent ascent in 2014, when GANs — a foundation of generative AI technologies — were first discussed. In 2015, Google released DeepDream, which uses a convolutional neural network (CNN) as an experimental approach to AI art, further advancing the field.
Ganbreeder was launched in 2018 and rebranded itself as Artbreeder, using GAN models to allow humans to use AI to modify existing images and create new ones. That same year, an artist collective operating under the name of Obvious made headlines by selling a painting called Edmond de Belamy, created using GAN models, at Christie’s auction house for the princely sum of $432,500. Those GAN models were trained on a corpus of 15,000 portraits from the 14th to the 19th century that were publicly available on the WikiArt website.
The public debut of text-to-image GAN-based online services for image generation sparked the imagination and interest of users around the world in January 2021. That month, OpenAI launched Dall-E, providing a publicly accessible and usable system that enabled anyone with internet access to create AI art with text prompts, giving the world a look at AI art’s possibilities.
In May 2022, Google announced its Imagen text-to-image technology as another option for AI art. This was followed in August 2022 by Stability AI, which launched Stable Diffusion’s services, another GAN-based, publicly accessible option to create AI art with text prompts.
The growth of AI art tools continued in 2023, with large software vendors joining the market. Notably, the Adobe Firefly service was announced in March 2023. This GAN-based approach integrates with Adobe’s popular image and video editing tools, including Photoshop and Premier.
Though AI art uses a variety of models and techniques, the fundamental process remains the same. The first step is machine learning, during which an AI model is trained on a data set to begin to form a knowledge base. Once an understanding of a data set is established, models can begin the next step: creating and generating images. As part of an interface to the models, modern AI art tools often employ some form of natural language processing, or NLP, to understand and interpret the text users input in their request to generate an image.
Different types of AI models used to generate art include the following:
Again, the primary tools of artists were physical items like brushes, paints, chisels or musical instruments. But the introduction of AI expands the palette of capabilities available to all artists in the following ways:
Making AI art is an increasingly simple task for artists of nearly any skill level.
At the most advanced, complex level, an artist can choose to train an AI model to create art. In this approach, the artist first needs to collect or have access to a data set of art. Once the target data set has been assembled, the next step is to train the model to learn from the assembled data. With the trained data set on an appropriate GAN model, the next step is to generate the art.
It is significantly easier for an artist to use an AI tool that has already been trained on a data set of existing art. It is possible, depending on the tool, to focus additional training on an artist’s own set of images to further refine the model. With the pre-trained model and any customization, the artist can then generate images. Images can be generated with text prompts, then refined after they have been generated. Some tools will allow for further generation with supplemental text prompts, while others can provide artists with additional visual design tools to fine-tune a creation. Many of the tools offer new users with free credits to explore the process of AI art.
Among the many AI image generator tools available to generate AI art today are the following:
As explained above, AI-generated content has many positive aspects, but potential pitfalls to AI art include the following:
The analytics and data integration vendor’s new suite combines AI and ML capabilities with generative AI to provide a secure and …
The data management specialist will add query and analysis capabilities to its portfolio of data quality and data governance …
By delivering data to workers within their workflows in an easily digestible form, inserting BI into other applications is one …
As Congress grows more concerned about the risks of AI, regulation is becoming a hot topic among policymakers.
Cross-departmental relationships are key to long-term business success. Discover why CIOs must focus on teamwork with these three…
Blockchain’s unique characteristics address many business issues. Here are 10 important benefits of blockchain and examples of …
The data virtualization specialist plans to use the funds to drive expansion, including R&D efforts for generative AI and data …
Modern data platforms help manage large volumes of data and empower real-time decision-making. It starts with overcoming the top …
The CRM giant’s new metadata management layer is capable of processing more data than previous versions to better enable …
AI can help service companies improve their field service management in a myriad of ways. Learn some of the benefits of …
ERP and accounting applications operate separately, but ERP systems can deliver valuable financial insights for companies. Learn …
IoT can benefit field service management in various ways. Learn about some of its applications in FSM, including inventory …
All Rights Reserved, Copyright 2018 – 2023, TechTarget

Privacy Policy
Cookie Preferences
Do Not Sell or Share My Personal Information


Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top