AI Content Creation

AI Content Creation. AI Content Creation is revolutionizing the creative industry by producing high-quality visual and written content. Dall-E and Stable Diffusion can create images based on a natural language prompt, while GPT-3 generates copy in various formats. Although AI-generated content is not yet able to replace human artists and copywriters, it has come a long way and may prove to be more useful in the near future

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Meltwater - 2023 Marketing Trends Guide

AI Content Creation. When futurologists first warned that robots would be coming for our jobs, few of us imagined that they’d start with graphic artists and copywriters. But 2022 was a big year for AI-powered content creation, and it looks set to play a bigger role in the future.

We’ve reached an inflection point where a combination of improved algorithms and raw computing horsepower has enabled “Generative AI” to create visual and written content at impressive levels of quality. Tools like Dall-E and Stable Diffusion are now capable of producing images of anything you want, in any style you want, from a natural language prompt. Similarly, platforms like GPT-3 can generate authentic-looking copy in a wide variety of formats.

Numerous products are now using AI to automate copywriting tasks ranging from optimizing email subject lines to mass-producing SEO blog copy; although it’s worth noting that Google considers AI-generated content to be against its guidelines for webmasters. Can these tools replace human artists and copywriters? That depends on your quality standards. Certainly, AI is capable of producing high-quality images, even close to photo-realistic, which may be good enough for some use cases as an alternative to stock images, but for situations where you have very specific requirements, a professional graphic artist is still essential.

For AI-generated copy, the answer is less clear. When you ask an AI to write a blog post about, for example, the benefits of owning an electric car, the AI does not research that topic and build a human-like understanding of the issue before putting its thoughts onto paper. Rather, GPT-3 has been trained on millions of existing diverse examples of human-generated text, and it uses that training to predict what text is most likely to provide a plausible response to the prompt you have given it.

So the copy it produces is not based on a deep understanding of the topic, but just a statistical model of what has already been written on that topic by humans. It’s a neat trick, but it doesn’t help marketers write engaging, original content that answers the needs of their audiences. But we’ve come a long way in a short time, so it’s likely that the technology can still improve significantly, and in a year or two, it may prove far more useful to marketers.