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Whose artwork is used to train the AI tools?

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Whose artwork is used to train the AI tools?

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Do you happen to know the year when the first piece of art created by AI was made?

1973!!! YEA THATS RIGHT.

In 1973, Harold Cohen introduced Aaron, an AI system for creating art. Aaron used a symbolic AI approach, where Cohen programmed rules and styles into the system. This allowed Aaron to help Cohen make detailed black-and-white drawings by following these rules for composition, drawing lines, and adding shading.

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AI art refers to artworks created with the assistance of Artificial Intelligence (AI) algorithms and technologies. These artworks can range from visual art, such as paintings and drawings, to music compositions and even literature.

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1. TRAINING - In AI art, it starts with a big collection of existing artworks or styles. The AI system learns patterns, styles, and details by studying this dataset. For instance, it might analyze thousands of paintings to understand colors, shapes, compositions, and artistic methods.

2. IMITATION - Machine learning algorithms are key. They examine patterns and details in the dataset to find common elements and styles. This learning helps the AI create new artwork that reflects the learned characteristics.

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For those who want to delve deeper into the itty bitty details

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For those who want to delve deeper into the itty bitty details

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If you've been paying attention to tech trends over the last few years, you've noticed an increase in artificial intelligence (AI) programs. One of the hottest new forms is AI art. In this post, we'll break down what AI art is, how it works, and how you can use it to enhance your creativity and speed up your workflow.

Let's dive in.

Artificial intelligence, or AI, refers to developing and deploying computer systems and algorithms that can perform tasks that humans would normally do. These computers are trained on large amounts of data that help them learn, reason, solve problems, and make decisions that can surpass human abilities. AI involves various technologies, such as machine learning, natural language processing, image generation, etc. They can adapt, learn, and automate tasks through data analysis, pattern recognition, and repetitive learning processes, allowing humans to pass tasks off to computers. AI will likely lead to advancements in many areas, including health care, finance, transportation, and creative areas such as web and graphic design.

What is AI Art

AI art, or generative AI, refers to creating art using artificial intelligence to automate image creation, aid with creative writing, create music, build websites, and more. AI art is different from digital art, which people with creative skills create. Instead, AI art allows those with little to no creative ability to create unlimited art forms with a text prompt.
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History of AI Art

The history of AI art can be traced back to 1973 when a computer scientist and artist named Harold Cohen created the first known AI art creation program, AARON. 41 years later, in 2014, generative adversarial networks (GANs) were developed. While not developed solely for creating artwork, GANs play a huge role in its creation today.

In 2015, researchers began training a computer to use a text prompt to generate images. They did this by reversing the process of image to text, which you might be familiar with. Have you ever noticed how your iPhone can identify objects in your pictures, then categorize them? That's a good example of image-to-text.

Flash forward to 2021, when the first major text-to-image software, DALL-E, was released. Created by Open AI (makers of ChatGPT) and named after the painter Salvador Dali and Pixar's WALL-E, it was the first program to be trained on millions of images and concepts. Thanks to the release of DALL-E, the AI text-to-image boom was born.

In 2022, a community of open-source developers began building AI art generators, grabbing whatever technology they could get their hands on. Some of those developers eventually went on to work on developing Midjourey, which we'll feature a bit later in this post.

What is a GAN?

A GAN consists of two main parts: a generator and a discriminator. The generator's job is to create new data, such as images, music, or text, while the discriminator's role is to determine if the generated data is real or fake. The generator creates random data, such as an image. Then it presents the image to the discriminator, which tries to guess whether it's real or fake. Initially, the generator isn't very good, so the discriminator can easily spot the fakes. However, both parts learn from their mistakes and get better over time.

The generator tries different variations of its output, and the discriminator becomes more skilled at discerning real from fake. They continue to play this game of trying to outsmart one another. Eventually, the generator gets so good that it produces data that the discriminator can't tell apart from the fake.

How Do AI Art Generators Work?

For an AI program to understand a wide range of prompts, it needs a large dataset of images (neural network) to work from. We're talking hundreds of millions of images and their text descriptions. Training models scrape alt tags, captions, titles, and text descriptions from images on the internet. It's important to note that the generated image doesn't come from the training data. Rather, it comes from the latent space (more on that later) of the deep learning model. Deep learning models don't see images the way we do. They see millions of pixel values for red, green, and blue (RGB).

Once the program determines how to decipher the prompt, you'll see the result like this:

Generative AI uses deep machine learning to produce text from images. At first, the computer takes a guess at what your text prompt is looking for, then continues to run it repeatedly until it gets it right. The program is given variables to consider to look for metrics to match the text prompt to generate an image. For example, use the prompt a pink garden gnome. The AI will begin processing the information considering different variables such as examples of gnomes, shapes, pink images, gardens, and other variables. It makes its best guess what it thinks a pink garden gnome is.

How Output is Generated

As algorithms go through the training data, they search for variables to improve their results and, during that process, build a multi-dimensional space that houses all of these data points. Let's take the pink garden gnome prompt and use it as an example. Within that space, the AI will place the gnome in one dimension, the color pink in another, and a dimension for gardens. It will evaluate all the dimensions and save space amongst hundreds of dimensions for the output. This is called latent space. The more descriptive words within the prompt, the more dimensions are required.

Before the output is complete, there is a generation step called diffusion. Using words in the text prompt and the dimensions containing images of pink, garden, and gnome, the AI produces a random image and then makes little improvements. Each adjustment makes the output image more refined and closer to the intended output.

What is Diffusion?

Using the garden gnome prompt example, imagine millions of pictures of gnomes, gardens, and images containing the color pink. During the process, all data points are analyzed, then an image of a gnome is produced. Diffusion begins by adding a series of transformations to the image, gradually increasing the noise. This includes transforming the photo into a pink gnome or placing it in a garden. During a series of transformational steps, the image becomes more different from the original, resulting in a unique, generated image of a pink garden gnome.

Types of AI Art Programs

Generative AI art encompasses more than just images. It includes art forms such as avatars, videos, logos, and photo editing programs. Let's explore each of these forms of AI art so you'll better understand what's possible with generative AI.

An AI avatar generator creates unique avatars for social media, gaming, live-streaming, and more. Some use text-to-image, while others, like Lensa AI, let you take selfies and turn them into avatars. They can be chatbots or virtual assistants that can understand what we say or type and respond in a helpful way. They can be designed to look and act like real people, with emotions and expressions, like Synthesia. Others create 3D avatars to use as profile pictures or gamer avatars. For example, Picsart allows you to upload 10-30 images of yourself, then create a custom avatar at the click of a button.

Anyone who's ever created a video for a presentation or website knows what a long process it is. That's where AI video generators can help. With a company like Pictory, you can create awesome marketing videos in a few minutes rather than days or weeks. If you want to automate the process fully, add a URL to Pictory, and it will turn your post into a functional, engaging video in a matter of minutes.

AI Art Generators

As previously mentioned, AI art generators, or text-to-image, allow you to enter a text prompt, sit back, and watch as your image is generated before your eyes. There are dozens of text-to-image generators available, but a few more popular ones stand out. These include DALL-E 2, Midjourney, Leonardo, and Stable Diffusion.

Each one has its strengths, but some are better than others. We'll use the same prompt to showcase the difference between platforms: a photograph of a dirt rally race car covered in dirt, moving at a fast speed, dirt flying, mountain scene, award-winning photography, soft shadows, and cinematic lighting.

Dall-E

Midjourney

Leonardo AI

Stable Diffusion

AI Art Side-by-Side Comparison

For this example, we entered the same prompt, left all settings at default, and didn't use any other enhancements. DALL-E 2, Midjourney, and Leonardo AI are the AI art generators used.

Prompt: high-quality photograph of a dog astronaut, star and planet backdrop, cinematic lighting, helmet with face glass, wide angle.

We'll let you judge for yourself which one did the best job.

Other AI Use Cases

Aside from videos and images, other AI use cases can help increase your creativity and productivity, allowing you to save that time for doing other things.

AI Logos

AI logo generators can do the job if you're a small business owner or a creative that needs some inspiration. There are many choices, with some standouts being the Wix Logo Generator, which works inside the Wix website builder. It will create an entire logo for you, or you can start the process with AI and finish making revisions yourself. You might be asking why do I need an AI logo maker if I use an AI art generator. The answer is that it sits squarely in the middle of the struggle bus regarding creating text. It just can't make the type legible. So, if you need a new logo for yourself or a client, check out one of the programs on our list.

AI Music Generators

Another big move for AI has come in the form of AI music generators. Content creators looking for an easier and faster way to create soundtracks for their projects will benefit (literally) by incorporating one of these tools. For example, Mubert is a generative AI model that allows you to create music and make money. You can create a track with a text prompt, then list it on the Mubert Studio marketplace.

AI Photo Editing

This next AI art use case allows you to enhance, modify, or manipulate images. This can include AI photo enhancers, image upscalers, or generative text-to-image add-ons like generative fill in Photoshop.

Using Photoshop AI's text-to-image generative fill, you can add or remove elements from photos, extend the canvas, or generate entire composite images with nothing but text prompts.

AI Website Builders

People needing a new website who don't have the time or expertise to build it themselves can benefit from an AI website builder. Different tools can assist you with a few lines of text or creating images. If you need an entire website created from text prompts, AI models like Framer AI can handle that job.

Using AI Art Ethically

When creating art with artificial intelligence, some ethical considerations must be made. Legal questions are being presented regarding copyright infringement. There is no precedent, so there are no clear answers on how to proceed. Some platforms, such as Adobe Firefly, have taken this into consideration. Their models were trained on royalty-free images, which should keep them out of legal hot water. Additionally, any image generated through Firefly is for entertainment purposes only. However, we anticipate that changing soon.

Before committing to a platform, research how the models were trained and gather as much information as possible. If you plan to use the art you create commercially, make sure your chosen platform supports that. Most platforms allow images to be used commercially as long as you sign up for a paid plan, but it's always best to double-check.

Final Thoughts on AI Art

AI art holds incredible potential. It's poised to transform the creative industry with innovative new technologies that assist creatives in making inspiring art and pushing the boundaries of human imagination. There are incredible tools like Synthesia for creating custom interactive AI avatars to incredible images through Midjourney to explore and utilize. As long as AI is used ethically and responsibly, it is an excellent creative companion to increase productivity.

Eager to learn more about AI? Check out our detailed tutorial on how to make AI art.

Frequently Asked Questions (FAQs)

Before we wrap up, let's answer some of your most common questions regarding AI art. Did we miss one? Leave a question below and we will respond!

What is the best AI art creator?

How do I use AI for art?

What is the most popular AI art tool?

How can you create AI art with Midjourney?

Featured Image via Leonardo AI / Leonardo.ai

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What is AI Art? How Art Generators Work (2024)

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In this post, we'll break down what AI art is, how it works, and how you can use it to enhance your creativity and speed up your workflow!

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Artificial intelligence (AI) has seen rapid advancement, especially in recent years. It's estimated that over 250 million companies (77%) are using or exploring AI in some capacity within their operations, and this doesn't show any signs of slowing anytime soon. If anything, the use and availability of AI will continue to permeate in the months and years to come.

Should We Be Mindful of AI Imagery?

AI has been instrumental in powering innovations such as self-driving cars to behavioral pattern analysis, among numerous others. One area which has gained substantial notoriety in recent months is AI image generation.

Much like how ChatGPT and Bard have been centerpieces in discussions around AI text generation and coding, similar tools like DALL-E 2, Midjourney, and Stable Diffusion have been at the forefront of generating realistic images that would otherwise take much longer at the behest of a human photographer or graphic designer. Using text prompts and instructions, these tools can create detailed and thought-provoking images, artwork, and infographics that can be used for a variety of applications.

While it's easy to look at AI image generation with rose-tinted glasses, it's important to recognize some inherent ethical concerns that warrant further discussion. While many are lamenting the new creative potential of this technology, experts are cautiously advising that AI image generators be treated with care and that business leaders be mindful of the possible implications it can have on hardworking creatives. Unsupervised use of AI might lead many businesses to replace teams rather than augment them, while others may not be inherently aware of the cybersecurity threats that exist within these tools.

So how do you strike that right balance of improved efficiency with AI while safeguarding the creative and experienced minds of your team? This article will examine some of the key ethical benefits and considerations of widespread AI generation.

The Positives of Augmenting Creativity With AI

One of the most lauded aspects of AI image generators is their innate ability to make digital art, design, and creation more accessible.

Tools like DALL-E 2 essentially give users access to a full suite of intuitive elements that can allow them to create images with relative ease. It can be argued that this 'democratization' of creativity offers plenty of advantages for businesses and individuals who can leverage tools effectively.

From an environmental perspective, using AI tools could provide a sustainability and social responsibility boost. Entrusting image generation to computers may be much better for the environment than the collective emissions caused by human creatives' journeys and the purchasing of new photography equipment. However, sustainable reuse and repurposing of tech can mitigate this problem.

In another positive, AI image and art generators allow anyone regardless of experience or skill level to draw and express creative freedom. Even amateur designers or novices with no natural art proficiency can explore their artistic side, which may have eluded them in the past. What's more, experienced designers, artists, and content creators can rapidly iterate images that can supplement and support their projects. They can alleviate many of the inherent time pressures that persist when creating assets from scratch to tight deadlines, not to mention the time-consuming editing and refining processes.

There are also cost benefits associated with AI use; sole creators and companies alike can lower their outgoings on several paid services like stock photo libraries, storage tools, external hard drives, and so on. At its core, AI is an innovative technology that relies heavily on machine learning and deep learning. In other words, the more you use it, the more accurate and usable the output will be. By leveraging AI images successfully, it may enable new forms of visual media and creativity for your business to explore in greater depth.

Copyright and Plagiarism Concerns

In the interest of fairness, it's imperative to look at the major concerns around the unregulated and unsupervised use of AI image generation. The potential for copyright infringement and plagiarism is huge; if you don't exercise caution, you could be opening many proverbial 'cans of worms'. Most AI tools are trained to recognize and utilize large datasets of images scraped off the Internet, without any mechanisms in place to alert them whether those images themselves are originals or whether they have been used unlawfully.

When prompted, AI tools may then replicate protected elements, symbols, styles, or typography, without any correct accreditation to their rightful owner(s). Even if a system doesn't generate new pictures that directly copy the original artwork, photographs, or branding, they may produce similar or closely inspired derivatives, which can still be a legal concern.

Images generated by AI could - indirectly or directly - infringe on copyright source material which could affect compensation or remuneration for original artists. It doesn't help that there is uncertainty and a lack of clarity around copyright laws that can sufficiently protect the works of original photographers and artists. Furthermore, disguising AI-generated works as originals only serves to deceive audiences that want to celebrate and champion human ingenuity and creativity.

There has never been a greater need for transparency and reassurance around AI generation. As it stands, the legal side is still evolving and adapting, but to preserve people's livelihoods, these problems must be addressed and solved before long.

The Impact on Employment and Workforce Dynamics

One other overt concern about AI's rise to prominence is the employment implications that it could have on human creatives. If companies can churn out images, designs, and text with open-source tools, what does that mean for the experienced graphic designers, artists, and content creators of the world? What does it mean for their livelihoods?

Demand for human creatives may decrease with AI image generation's continued evolution. Furthermore, companies may view AI generation as an alternative to fairly compensating human designers or photographers, which is morally questionable in the eyes of many.

Even if jobs aren't replaced directly, the responsibilities of workers will unlikely shift and change. There is a belief that AI creative tools aren't likely to replace human thought entirely; no computer can ever replicate the complexities and experiences of a person's brain. However, it's clear that ‌workforce impact needs to be weighed heavily in professionals' decisions on whether to adopt AI company-wide or take a more methodical, thoughtful approach to its integration.

Unconscious Biases and Influences

Socially speaking, the imagery and content produced by AI systems is reflective of their training data. Biases and toxic perspectives have been evident when studying their outputs, and the continued lack of regulation means that this discourse will only continue to spread and perpetuate through generated art and imagery.

This can come in the form of race, gender, sexuality, disability, religion, or other biases and stereotypes, or amplified hateful, abusive, or violent images as 'requested' by users. With the AI tools' lack of moral compass and 'filter', appropriating or casting harmful aspersions on cultures is easier, as is the potential spread of misinformation. Ongoing conversations around ethics, governance, and policies will be essential as adoption spreads.

Moving Forward Responsibly

AI image generation seems poised for major growth and applications across many industries. With this disruption comes plenty of risks for creators, companies, and policymakers alike, making the act of navigating this technology shift difficult.

Transparency and thoughtful legal guidance will be key, with clear disclosure of AI tool use and adherence to relevant data protection and copyright regulations. AI systems should be regularly audited and tested for problematic biases and validated that original artists are credited and compensated for their work when required.

This emerging technology still requires open, ongoing dialogue around its ethical application. That said, ‌future possibilities are undeniably exciting, provided that more professionals can thoughtfully implement AI.

Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE's position nor that of the Computer Society nor its Leadership.

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Ethical Pros and Cons of AI Image Generation

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Ethics is accelerating AI technology and powering innovations. Uncover what this means for the future and potential ethical implications.

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Who owns the pieces of work created by AI tools?

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Who owns the pieces of work created by AI tools?

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Should AI-generated art be clearly labeled as such to distinguish it from human-created art? 

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Should AI-generated art be clearly labeled as such to distinguish it from human-created art? 

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Does AI art unintentionally perpetuate stereotypes or misrepresent cultural heritage?

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Does AI art unintentionally perpetuate stereotypes or misrepresent cultural heritage?

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Art is the interpretation and expression of one's thoughts and observations in a creative manner. In this definition, AI art is entirely ethical. Because we consider that art is not entirely about the product but more so the process. The intention and thought behind the details of the art are the focus, and that has not changed with the inclusion of AI as a tool. Still, humans are the ones pulling the strings in providing the prompts and shaping the output of AI. And so despite a change in medium, the ethics of art has not changed. And while critics may argue and question "what about the inputs used to feed AI?". That criticism would be entirely dismissive of the fact that humans are also sponges for the constant inflow of information from around us. Humans similarly absorb and interpret the world around us, and simply because a machine can help us make sense of a greater breadth of inputs, does not make it any less similar to our natural process of understanding the world. To knock AI for such a process and question its inputs would be to call our interpretation of the world unethical.

That being said, as all things in life, AI art is not void of its own controversies. AI art has the potential to be unethical due to the factors that govern its output. When the prompt inputs are unethical in nature, AI being an undiscerning tool will naturally output an unethical work of art. While there are safeguards in place to avoid direct infringements of rights, the line between art and theft is a fine and blurry one. With such a fuzzy delineation of rights, arises instances of unethical work. Furthermore, AI being entirely input dependent relies heavily on a robust and diverse data set. However, the required input may lack such strength in instances of niche topics. In such instances, the AI has minimal references and hence is more likely to replicate what it refers to. As such there can be cases of unethical work when AI art is concerned as AI is complicit to external factors that can cause it to be unethical.

~Justin 08/23