The Evolution of AI Content Generation

Persado’s CTO, Dr. Panagiotis Angelopoulos, recently published an article on Spiceworks.com that discussed the rise of Generative AI for language, also known as natural language generation (NLG), its use cases, and how this emerging technology may represent one of the “greatest achievements in computer science. 

Here’s a summary of the article:

The growth of Generative AI for language

Natural language generation, or NLG, is a subset of Generative AI for language. It enables computers to create human-like language from structured and unstructured data. In its ideal form, NLG can seamlessly create context-appropriate language that can greatly benefit marketing communications in enterprise environments. 

Early language models used statistical methods to calculate the frequency with which people used word sequences. This approach lacked the ability to generate meaningful, on-message text. Over the last 10 years, however, extensive deep learning research has spearheaded the development of expansive models. These have more layers of understanding and artificial neurons that provide a higher level of awareness. This advancement effectively captures more of the nuances of human language. 

OpenAI’s GPT-3 is the most well-known of these more expansive Generative AI language models. Given any text prompt, like a phrase or a sentence, GPT-3 returns the next most logical text in the sequence in natural language. Developers can program GPT-3 by showing it just a few examples or prompts.

“Most people that have interacted with GPT-3 have been fascinated by its ability to write coherent, high-quality language.” 

Generative AI language models still need constant supervision

While GPT-3 shows great promise, it isn’t at the point where it fully understands what it is writing. Words flow together well and make sense. However, the text often inadvertently jumbles facts with fiction. Unchecked, this scenario can lead to potentially dangerous content generation situations.

OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. The organization strives to build safe and beneficial AI, but end users also need to do their part to make sure to only disseminate accurate information.

Checks and balances for content generation

Although larger language models do their best to learn all they can from aggregate human knowledge, they aren’t the solution to every problem. Legacy language models rely on timely training data. For example, the model might not know who the current U.S. president is. 

“While AI is a great tool for inspiration and allows humans to accelerate their workflows and focus on the most important aspects of their jobs, natural language generation still needs constant supervision with checks and balances from humans,” says Panagiotis.  

Personalization

As momentum around Generative AI for language builds, executives, CMOs and their teams will need to work alongside AI to create optimal customer experiences. Personalized digital communications offer the largest opportunity to drive experiences that attract and build customer lifetime value. 

Many customers are overwhelmed by digital overload, and conversion rates are dropping. One reason that personalized offers and incentives backslide is that the content fails to motivate. This roadblock is where Generative AI for language will come into play. The technology can successfully generate content that motivates engagement and action on a personal level.

Natural Language Generation for enterprise communications

Specialized Generative AI language models developed for enterprise communications produce language that speaks to each customer as if the company knows them personally. Referred to as a narrow AI implementation, this task is only achieved with AI. It is performed by establishing a unique classification of language for enterprise communications and tagging a vast amount of communicative examples with behavioral concepts such as emotions, narratives, and other language and behavioral concepts. Then, the models are further refined with the resulting interactions between brands and customers. Unlike generic models, these are designed for a specific purpose and evolve based on how customers interact with the output.

Conclusion

“It’s no surprise that one of the most sought-after AI applications is the ability to mimic how humans communicate verbally and in writing,” says Panagiotis. “If we were able to build AI technology capable of communicating ideas the same way humans can, that would lead to one of the greatest achievements in computer science.” 

NLG is a key element of the content generation process that marketers should leverage to inspire customers to engage and act. Working in concert with this emerging technology, marketing teams can move the needle to motivate their audiences.

Personalize Messages With Persado

Persado enables companies to generate using AI personalized language that resonates across every touchpoint in a customer journey. It motivates customers as individuals. Organizations that use Persado reach a tipping point in their ability to understand the customer. They can generating powerful, on-brand content and drive value with the world’s most powerful machine learning optimization and personalization capabilities.

The Persado Motivation AI Platform leverages insights about customer engagement with language and uses it to generalize personalized content to motivate action across all channels and customer journey stages. Learn more here.Dr. Panagiotis Angelopoulos’ article, The Future of Content Generation: The Rise of NLG, was published on Spiceworks.