The Future of Content Generation: The Rise of NLG |

The Future of Content Generation: The Rise of NLG |

Natural Language Generation (NLG) is a key part of the content creation process that is only now being recognized as a leading technology that marketers should leverage. Dr. Panagiotis Angelopoulos, CTO at Persado discusses the rise of NLG, its key use cases, and how it can help achieve business goals.

Language is recognized as one of mankind’s greatest achievements; it allows us to communicate complex ideas, express our deepest emotions and connect with each other. It’s no surprise, then, that one of the most sought-after applications of artificial intelligence (AI) is the ability to mimic the way humans communicate verbally and in writing. If we were able to build AI technology that could communicate ideas the way humans do, it would lead to one of computing’s greatest achievements.

Enter language models

For the past 20 years, tech experts have focused on teaching humans machine language. As this lesson has been largely realized by now, the next goal is to teach human language to machines. This ongoing research has already resulted in the creation of language models, a means of mathematically modeling spoken and written language. The ultimate goal is for the AI ​​to use language models to generate human-level language.

Initially, linguistic models were based on statistical methods of calculating the frequency of word sequences. Although this approach could model some basic properties of a language, it lacked the ability to generate meaningful text about the message. It has only been a few years since developments in neural networks have allowed us to build more sophisticated language models that can use the context and long dependencies needed to generate quality language at the human level. The explosion of deep learning research over the past decade has helped us develop expansive models with more layers of understanding and artificial neurons that correspond to a higher level of consciousness, effectively capturing most nuances of human language.

Arguably the best-known language model to date was developed by OpenAI. Its latest iteration, GPT-3, contains 175 billion parameters (the part of the model that is learned from historical training data) and has been trained on over 45 terabytes of textual data using sources such as common web crawling, Wikipedia and books such as references. points. OpenAI has proven that a model such as this can learn to perform many tasks such as summarizing answer information to questions without being explicitly trained on the task – with a very high level of accuracy. This level of creativity and production quality is truly impressive and often indistinguishable from what a human could come up with.

Learn more: Why Natural Language Processing is NOT the Future of Business Intelligence

Technological Concerns for Standardized Language Models

Most people who have interacted with GPT-3 have been fascinated by its ability to write consistent, high-quality language, but two main questions remain: “Should we trust it?” and “Are there any dangers when using it”? One answer to these questions is that, unfortunately, even though the quality of the text is extremely good, it is obvious that our scientific methods have not yet reached the level where the model can actually understand what it is writing. It’s about putting together words and phrases that make perfect sense but can easily, albeit inadvertently, mix fact-checked and fictional in a convincing way. This can lead to dangerous situations, such as writing news articles containing misinformation and spreading fake news. While AI is a great inspirational tool and allows humans to speed up their workflows and focus on the most important aspects of their jobs, it still requires constant supervision with checks and balances from the from humans.

A perfect example of why these checks and balances are necessary when using legacy language models is that their information relies on timely training data. He might not know who the last American president is or that society has been living through a treacherous pandemic for more than two years.

The role of LNG

Although large language models do their best to learn all they can from aggregated human knowledge – and are very good generalists because of these learnings – they are not the solution to all problems. You wouldn’t want a very generic template for writing content for your business communication where the language must adhere to strict branding guidelines, especially when the goal is to achieve maximum returns.

I mention this because the momentum around NLG adoption continues and is further recognized as a valuable tool, executives and marketers (and their teams) will need to evolve to work alongside AI to create optimal customer experiences. Recent Data shows that 53.9% of US-based business leaders are already leveraging AI or machine learning to deliver a personalized experience to their customers, and that number is likely to increase significantly in the coming months and the years to come. Personalized digital communications offer the greatest opportunity to generate experiences that attract and create lifetime value for customers. However, digital overload overwhelms customers, causing conversion rates to drop rapidly. And while personalization generates tremendous value, existing personalization approaches fall short. The reason is that personalized offers and incentives fail to address the most important factor in conversion: motivation. This is where AI and NLG come in. The challenge is usually not an organization’s offer or value proposition, but rather the need for language that drives engagement and motivation. action at the personal level.

Specialized models developed specifically for corporate communications can remedy this challenge by generating a language that speaks to each customer as if the company knows them personally. This task can only be done with AI and is generally referred to as “tight AI implementation”. It is accomplished by developing a single language classification for business communications (e.g., marketing, customer service) and labeling a large amount of communication examples with behavioral concepts such as emotions, narratives, and other linguistic and behavioral concepts. The models are then refined with the resulting interactions between brands and customers. Unlike generic templates, these are purpose-built and scale based on how customers interact with the output.

The opportunities for companies using this type of NLG are immense. As the Boston Consulting Group pointed out in a recent article on the NLG and personalization, “a major new force is taking shape in personalization that could drive up to $200 billion in additional revenue for the Fortune 500 and up.” ‘to 800 billion dollars in the world’. This number alone tells us that now is the time for companies to assess these capabilities if they want to have a tectonic impact on their enterprise-wide customer and consumer engagements.

The future of efficient and effective content generation is now. Technologies capable of developing high-value language and messaging are already available, and companies using them will be able to multiply returns and quickly recoup massive value in a short period of time. This is even more critical in times of economic turbulence, such as the one we are currently experiencing. Forward-looking companies must implement available tools such as NLG to streamline and scale their operations as well as outperform competitors in the race for market share and customer loyalty.

Do you think NLG and other content generation technologies will soon replace human content production? Tell us about Facebook, Twitterand LinkedIn.


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