Almost 70 years ago, an English computer scientist named Alan Turing set out to prove that computers could think with an intelligence level on par with that of human beings. Since then, computers have defeated chess grand-masters, won television game shows and help predict customer behavior. All this is commonly referred to as Artificial Intelligence or AI. Today, the use of intelligent machines to generate human-readable text, known as AI Content, is a disruptive technology, prevalent in many different industries and taking the world by storm.
What exactly is AI?
AI or Artificial Intelligence is no longer an action film sub-plot, but the real ability of a machine to process and understand data in a human-like way and then generate a thoughtful, intelligent response or conclusion. As opposed to natural intelligence found in humans, artificial intelligence, sometimes called machine intelligence, mimics functions that are normally associated with humans such as learning and problem solving.
Almost 2.5 quintillion bytes of data is generated every day, thanks to the IoT or Internet of Things, with 90 percent of all the data in the world generated in the last two years. Thanks to this exponential growth of data in just about every industry, AI research has been fast-tracked to become one of the most important disruptive technology startup sectors. AI is being incorporated to both learn and construct intelligent reactions to solve problems or create opportunities in almost every industry.
NLG: Producing AI Content
Natural Language Generation, or NLG, is a process that involves machines that use AI to process sets of data and generate content from the knowledge gathered that is indistinguishable from natural content generated by a human. Natural Language Generation can be thought of as a translator, of sorts, as the process takes data and interprets it in order to provide a meaningful human interaction. The basic steps taken by NLG systems include:
NLG is one of a powerful set of processes along with Natural Language Processing, or NLP, and NLU, which is Natural Language Understanding. These functions work together to provide AI content that is interesting to human readers on a large scale in a matter of seconds. NLP recognizes the desired information from the vast quantities of data it is given, then NLU determines what to do with the information and NLG takes over to structure and merge the information, successfully creating desired result, which is AI Content.
How is AI Content Used today?
As a disruptive technology, AI is being developed for use in agriculture, energy, mining, healthcare, intellectual property, IT service management, manufacturing, and the list goes on. In particular, industries suffering from labor shortages are heavily benefiting from AI to become stronger and preserve jobs in the long run. One example is for the tracking and scheduling of oil and gas tankers, which uses human-like reasoning AI technology to determine when the tankers will leave port, where they will be going and how much they are transporting.
AI content is revolutionizing the marketing industry in the same way, creating a more engaging customer experience. Industries with high-touch customer requirements such as retail, travel, finance and banking are adopting AI to handle initial inquiries and create transaction funnels. The algorithms used for such a complex purpose are able to organize overwhelming amounts of data in order to provide better content and customer experience in record time. By using AI to plan topics, test landing pages, discover keywords, review analytics and shape marketing strategies, content marketing teams have become more efficient and better able to enhance the customer experience.
Here Are Some Examples of How AI Content is Put to Use:
AI is also dramatically changing the customer service experience and one of the ways is known as a chatbot. Chatbots are AI programs that simulate conversation with a human partner. Often used for first-touch conversations and inquiries, chatbots help users access information or initiate customer service quickly and efficiently.
Home assistant devices such as Amazon Alexa and Google Assistant also use AI chatbot technology to converse with human beings. In both cases, NLP, NLU and NLG are used in different degrees and ways to understand, process and generate human language. Focusing on NLG, think back to your use of a chatbot and the answers you received to your questions. While helpful, they may seem as though they are part of a pattern or formula. That is because they are; the structure of the end content relies on a set of rules or, essentially, a template in order to exist. The content that chatbots and their like are able to produce is limited to a fill-in-the-blanks type scenario. Asking these chatbots something that they have not learned yet may produce strange answers or no answer at all.
2. Media Updates
The reason you don’t have to wait hours after the finish for your favorite team’s match result? Because the mainstream media uses AI Content to keep their site updated in almost real-time. Statistical information is quickly updated by machine thanks to AI processes giving it the ability to read, interpret and generate meaningful content for our consumption. Just like chatbots, these articles rely on a set of rules that make up a template for them to follow and make sense with the content they create.
AI Content also opens up opportunities to provide real-time updated content based on statistics for weather and financial information such as stock and bond prices. Similar to sporting event reporting, automated content for weather and finance provides rich information to readers in a conversational way.
3. AI Content for Scale
As AI Content creation matures, it is being used for the generation of large amounts of high-quality content for web pages. Previously, companies struggled to create enough meaningful, relevant content at scale. Phrasetech was among the first to answer this requirement by employing AI Content creation technology to offer a solution to the demand for content at scale. Phrasetech has collaborated with many enterprise companies in retail, fashion, e-commerce, travel and more to provide rich, informative content to their users with the use of their own company data, our sophisticated technology and a collaborative set of goals.
Undaunted by the vast amount of data our partner companies maintained on inventory, products and the company itself, Phrasetech was able to quickly and easily generate AI Content to meet each company’s marketing goals.
Using a simple, rule-based template, while great for chatbots and box scores, just will not work for Phrasetech clients. Why? In the example of covering a sports event, the data set would include points, fouls and other scoring statistics as well as relevant terms such as field goal or kick-off, and finally a limited set of probable outcomes like win, lose or draw, which make it easy to create a simple tool to create content. Content at scale, however, is so much more complex and includes information that does not fit in a certain category or statistical-based structure. Using the same structure as a box-score report would also give an underwhelming user experience, which is definitely not a key to success.
AI Content at scale, therefore, has to be generated by a very sophisticated method, which is what we developed at Phrasetech. Our technology produces AI content by first delving into the company’s data sets in order to learn their domain, enabling us to produce content that is meaningful, useful and up-to-date. Phrasetech not only offers a better, faster way to automate content creation at scale, we also offer a way to transform your customer’s experience through content marketing.