The era of AI “hallucination”: How generative technology is transforming marketing and design

The word of the year and why.

The word “hallucinate” was recently elected the word of the year for 2022 by the Collins Dictionary. This is mainly due to the growing use of this word in the context of generative artificial intelligence. AI models capable of generating images, texts, and other content are often described as having the ability to “hallucinate” or imagine things that do not exist.

Understanding generative AI.

What is generative AI?

Generative artificial intelligence refers to AI models trained to produce original outputs based on a prompt or input request. This includes generating entirely new images, text, code, audio, and video.

Famous examples.

The most famous example currently is DALL-E from OpenAI. It can create realistic images of virtually any text description you provide. Other popular examples include ChatGPT from Anthropic, which maintains seemingly human-like conversations, and Claude, also from Anthropic, which generates coherent texts on various topics.

How the technology works.

These AI models are trained on huge datasets to learn patterns and correlations. Afterward, they can receive a prompt and generate new outputs that match the style and context of the original data. It’s as if they can “hallucinate” or imagine entirely new versions of what they learned before.

Transforming marketing and design.

New creative possibilities.

As a marketing and technology professional, I find the rapid development of generative AI models fascinating. These tools have the potential to revolutionize digital content creation, graphic designs, brand communication, and much more.

For example, instead of hiring human designers, companies will be able to simply “ask” an AI to create logos, ads, infographics, and other customized materials for their businesses. This will save a lot of time and money.

More efficiency and scale.

Furthermore, the automated generation of content, such as blog posts, emails, video captions, and website texts, will also be possible. Thus, marketing professionals can scale and customize their communications like never before.

In the future, entire advertising campaigns could even be generated by AI based on strategic goals and target audience profiles. This will bring more efficiency and relevance to communications.

Concerns and challenges.

Ethical and security issues.

However, generative models also raise concerns about copyrights, privacy, and security. We need to be careful not to allow these tools to be used to spread misinformation or generate harmful content.

Moreover, as AI becomes more sophisticated, we will need to deal with complex issues about creativity, authorship, and the nature of human work. We don’t have all the answers yet.

Current limitations.

For now, generative models still have many limitations. They can make false claims, repeat existing biases, and fail completely when asked to perform complex or creative tasks. The technology still has a long way to go before we can truly rely on it for critical tasks.

Future perspectives.

Cautious optimism.

Overall, I am excited to see where this technology can take us. I believe generative models will bring many positive innovations if developed and used ethically and responsibly.

With the right precautions, this new era of “AI hallucination” has the potential to automate mundane tasks, enhance human creativity, and make information more accessible to everyone.

What are the main examples of generative AI today?

Some of the main examples of generative AI today are DALL-E (image generation), ChatGPT (conversational chatbots), Claude (text generation), and Jukebox (music generation).

Can generative AI replace human designers and writers?

Not yet. Generative AI still has many creative and contextual limitations. For now, it works best to automate simple tasks or serve as an assistant to human designers and writers.

Do generative models pose any risk?

Yes. They can be used for spreading misinformation, violating copyrights, or generating inappropriate content. Therefore, we need ethical safeguards as the technology develops.

What is needed to train these AI models?

Enormous datasets (millions of parameters) and significant computational power are required. Large technology companies invest massive resources in this training.

Will generative AI replace marketing and communication professionals?

Not in the next few years. Humans will still be needed for creative strategies, emotional connections, ethics, and supervision. However, many tasks will be automated, allowing professionals to focus on higher-value activities.