Artificial Intelligence Can Now Write Amazing Content – What Does That Mean For Humans?
If you believe anything can and will be automated with artificial intelligence (AI), then you might not be surprised to know how many notable media organizations including The New York Times, Associated Press, Reuters, Washington Post, and Yahoo! Sports already use AI to generate content. The Press Association, for example, can now produce 30,000 local news stories a month using AI. You might think that these are formulaic who, what, where and when stories and you are right, some of them certainly are. But, today, AI-written content has expanded beyond formulaic writing to more creative writing endeavors such as poetry and novels.
The software process that automatically creates a written narrative from data is called natural language generation (NLG). It’s already used for a variety of content generation needs in our world including business intelligence dashboards, business data reports, personalized email, and in-app messaging communication, client financial portfolio updates and more.
The first step in NLG is to define what format of content is desired. Each content type from social media posts to financial reports to poetry has a unique writing style and structure. The narrative design, also known as the template or narrative type, is constructed by the end-user, the NLG solution or by the software provider. Some of the NLG tools available include Quill fromNarrative Science, Amazon’s Polly, Wordsmith fromAutomated Insights, and Google’s Text-to-Speech while some organizations have created in-house tools such asHeliograf at the Washington Post.
As with any artificial intelligence solution, ownership and access to data are crucial. In the case of NLG, structured data is fed into the software and is processed through the “conditional logic” that’s part of the narrative design. The goal is for the output to sound like a human-generated each piece of content.
Why are organizations investing in natural language generation?
As with other implementations of AI, natural language generation allows organizations to process large datasets and create more efficiently than humans can. Organizations who have implemented an NLG solution can produce thousands of more narratives in a sliver of the time it would take humans to write each one individually.
In addition, NLG enables complex personalization at scale. This can have significant service and overall experience benefits for customers. If your company has a workplace savings scheme, think of the 401K portfolio summaries you receive quarterly. These are likely generated by NLG, but it is highly personalized, speaks directly to you and uses your unique set of information.
Natural language processing can also make data more insightful and easier to understand for humans who are not data experts. While charts and graphs are visually appealing, it may be a challenge for some people, especially those who aren’t used to analyzing data, to extract the important message they should receive from the visualization. NLG can further engage the reader who is looking at the information with written summaries and key insights to accompany the charts.
