As emerging technologies like artificial intelligence and machine learning continue to dominate the news, professionals across industries are paying close attention, and voicing their concerns.
There is little consensus as to whether the explosion of large language models (LLMs) are a boon or a curse on the future of work. But one thing that is becoming more and more clear is that these powerful tools will play a critical role, one way or the other, in our collective future.
In one of the more recent AI breakthroughs, these LLMs have been leveraged alongside deep learning models to create powerful image generation platforms. In this article we’ll take an in-depth look at these automated image generation tools and the role they could play in the ever-evolving marketing landscape.
Here’s what we’ll explore:
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The emergence of AI image generation tools and how they function
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AI art generation tools
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The benefits and drawbacks of AI image generation
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Best practices for using AI-driven tools in marketing
The emergence of AI image generation tools
The use of artificial intelligence in the creation of digital images is actually not a very recent development, with some digital historians claiming that the first AI generated image was created as early as 1973.
In the late 1990s, AI systems–and the computing power behind them–had advanced far enough to be used in handwriting analysis and speech recognition. As those capabilities evolved, image recognition became more of a focus around 2009, and from there the LLMs as we know them today began to emerge. Let’s dig into how LLMs and deep learning models work together to create new images.
What is AI image generation?
In short, AI image generation is the use of an AI-driven software platform to create completely new images by “learning” from millions of data points derived from existing images. Using a text-to-image generator is almost like commissioning a unique piece of art by using a few basic terms to describe the artwork.
The difference in this case is that instead of the work of art coming from a living person’s imagination, the image generated is an amalgamation of a multitude of similar types of artwork fed into the tool’s learning model.
Of course, there are implications to this emerging technology that we’ll discuss in more detail, but it’s important to note here that these systems aren’t actually making autonomous creative decisions (yet). But then how are AI image generators able to create such detailed and specific images at a user’s behest?
How does AI image generation work?
While an AI-driven image generation tool may seem like it’s performing the role of an independently creative artist, these platforms actually require multiple, overlapping systems to be running behind the scenes to function:
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First, the system needs to understand what it’s being asked to generate, and that requires it to integrate with an LLM such as ChatGPT or a similarly powerful language model that can understand a wide range of natural language inquiries.
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Second, the platform requires access to an extensive database of images so that it can access them when queried. This means that the software engineers building these tools have “taught” the machine using massive datasets of images, typically extracted from across the internet.
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Third, an artificial neural network (aka ANN, an advanced form of machine learning algorithm) is integrated between these components. The ANN is trained using image-text pairs from the dataset, and so it can then use the prompts from the user, and the understanding the LLM provides, to determine what type of image it’s being asked to create. The ANN then combines and modifies various relevant images into a single representation of what it feels the user has asked for.
User prompts can be used to define any number of variables and factors, including everything from resolution and image size to the style or genre of the art being created. AI image generators can even mix and match different elements to create something truly unique.
Tools for AI art generation
Technology giants and start-ups alike understand the growing importance of AI-driven content creation, and so a number of developers have joined the AI image generation gold rush. However, there are few tools that have become popular in the early going. Here are a few of those tools, listed below in alphabetical order.
Bing Image Creator
While Bing itself is a search engine that leverages LLMs and has been making waves in the search engine space, Microsoft has more recently integrated image creation into the platform through its own deployment of the OpenAI DALL-E tool. The service requires an existing Microsoft account to access, but does not otherwise require users to pay for credits.
Bing’s integration with search allows it to not only find answers to queries, but also to solve problems for users. For example, you could ask Bing to create a menu for your restaurant and then ask it to generate appropriate images for your dishes using the image generator.
DALL-E 2 by OpenAI
OpenAI has paired its LLM platform, ChatGPT, with the DALL-E image generation tool (currently in its second iteration, DALL-E 2) to allow users to generate realistic images and art using natural language prompts. The platform boasts that it can combine concepts, attributes, and styles to create unique and realistic content.
The latest version of the tool allows for images with much higher resolution than the previous iteration, and has a stronger ability to match captions and to produce photorealistic results. Early registrants have free access to the tool, while new users to the platform must pay for credits to create images.
Dream by WOMBO
Dream is a mobile app-based image generator available through the Apple App Store, the Google Play store, or on Discord. While there is a free, limited access version available, Dream also offers a paid subscription that unlocks access to advanced features and functions. The app also has a desktop interface.
Midjourney
Midjourney is a paid service that grants users access to a powerful, high definition image generator and its community of supporters on Discord. Image prompts are entered through the Discord interface, and–as all created content is accessible to the community–you can also see what others are using the platform to create for inspiration. Midjourney allows users to upscale and alter images after creation and make specific modifications to tweak the results.
What does AI art generation mean for marketers?
As with other forms of AI-driven content creation platforms, it’s important to understand all the implications of leveraging AI art generation in the digital marketing and advertising space. If you’re already using a stock photo service, chances are that you’ve already used AI generated content even if you didn’t realize it.
Like any automated tool, AI art generation introduces unique benefits and challenges.
Benefits of AI art generators
AI art generators, when leveraged properly by a marketing team, can benefit an organization in a number of ways.
Personalizing and customizing the customer experience
Personalization and customization of the customer experience is becoming an increasingly important component in marketing strategies. Feeding a visitor’s demographic and user history data into an AI content generator allows marketers to present data-driven and highly personalized campaigns and visual experiences.
Scale with demand for content
The sheer amount of visual content a brand needs to generate in order to launch effective and personalized campaigns is a challenge in itself. AI content generators allow small teams and startups to scale their marketing efforts to meet customer demand and growth goals, leveling the playing field with much larger, resource-rich firms.
Increased workflow efficiencies
In addition to increased volume, teams also need to generate highly targeted content faster to keep up with the pace of competition. AI tools allow small marketing teams to increase the efficiency of their efforts within the limitations of the resources at hand.
Pitfalls of AI art generators
On the other hand, marketing teams must be careful not to look at AI art generators as a miracle solution that will solve all of their content needs in a single pass. These powerful platforms also present a number of challenges and potential drawbacks for unprepared teams.
Personalized, but derivative
While personalization is possible through AI content generation, these models are trained off of existing artwork and images from around the internet, including work by well-known artists. Because of this sourcing, poorly prompted or refined AI-generated art runs the risk of appearing derivative and emotionless.
Potential for a lack of passion and creativity
If your team becomes too comfortable with the time-saving process of submitting a query and having a response generated immediately, there’s a risk of becoming dependent on this technology. Marketers are inherently creative people, and should never become too removed from bringing their own unique visions to their projects. After all, this creativity is what makes your brand unique and valuable in the first place.
The output is inconsistent
The quality of an AI-generated image is highly dependent on the text descriptions and reference images provided to the AI tool. This becomes painfully (and sometimes comically) evident when a tool is asked to generate realistic images of humans. Without sufficient guidance, this type of query can result in images with very obvious anatomical errors, preposterous body proportions, and an “uncanny valley” feel.
Data can’t create emotional impact
While your demographic and user data might lead you to believe that certain types of visual content should resonate with your target audience, the data doesn’t always fully capture emotional engagement. When using generative AI, it’s important to include emotional descriptors in your queries, or–better yet–to pay a human artist to inject authentic human emotional expression into your AI-generated output.
Ethical concerns unique to AI image generation
There are certainly ethical concerns that come with automating the creation of art. While LLMs have always come with concerns of plagiarism and accuracy, these concerns are relatively easy to mitigate by having someone fact check and edit generated content to ensure that it’s accurate and original before publishing.
AI image generators, on the other hand, are specifically trained to mimic and emulate the contents of massive datasets of existing images. However, few of these platforms are programmed to reference the creators that they borrowed from. Because of this, a poorly set up AI content generator could easily run afoul of copyright and intellectual property laws.
Further, because no attribution is given to the artists whose works are being used as reference, artists are often not being compensated financially when their work is essentially being leveraged for profit.
How to best use AI image generation in marketing
To understand how best to use AI image generation tools as marketers, it’s important to remember that the LLM components are only able to imitate abstract thinking and they lean heavily on generalizations. Updating these tools frequently with current data and carefully crafting your queries can help improve the quality of your results. Here are some ways that you should, and shouldn’t, use AI image generators.
The Dos of AI image generation
Leaning into the benefits that we outlined above, you should use AI image generators to:
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Create baseline images that will appeal to your target audience.
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Increase your creative team’s productivity by generating images that can be leveraged in different ways.
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Generate images while brainstorming marketing campaigns to give your team visual inspiration for original campaign content.
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Decide how you’re going to leverage AI generated content and whether or not you need to invest in a paid platform, or if a free platform is sufficient for your needs.
The Don’ts of AI image generation
The dangers outlined above reveal some very important pitfalls to avoid when leveraging AI image generators:
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Don’t let the AI dictate creative decisions. AI tools are best used to enhance and support, but not supplant, your creative directors and marketing team decision makers.
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Don’t forget that the content you generate through AI isn’t unique or completely original, and you should never take artistic credit for it. You should, however, disclose when content was generated by an AI tool.
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Never publish or use raw output from an AI image generator. There’s a good chance that raw output contains a fundamental flaw that may not be immediately apparent. Have a human artist clean up and enhance AI-generated art to improve the way it fits into your uniquely branded marketing plan.
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Don’t forget to gauge your target audience’s reception of AI-generated content, and remember to consider the ethical concerns of AI image generation. Getting caught using unnatural, unattributed AI-generated marketing collateral can derail an otherwise promising campaign in a hurry.
Are you ready to leverage AI image generators in 2023 and beyond?
Always remember that AI-powered content generation software tools need constant, active oversight. If you’re curious but uncertain about your team’s capacity to make the most of these groundbreaking tools, consult one of the top-rated graphic design firms in the UpCity Marketplace to learn how best to integrate AI into your marketing plans.