I thought this career path was safe from the robots. Hey, I was convinced they'd only go after the jobs people actually want, like IT technician, pilot, or bomb disposal engineer.
But writing? C'mon. But now the robots are putting out poems like nobody's business.
The robots have been taken off the subs bench, due to the amount of content needed to create great digital experiences.
The more current, the more personalised, the more targeted the experience is, the better the customer engagement. According to Dawn Papandrea of NewsCred Insights: "Content marketing revenues are projected to grow at a 14.4% compound annual growth rate from 2017 to 2021."
Gartner estimates that “By 2020, natural-language generation and artificial intelligence will be a standard feature of 90 percent of modern BI platforms"
So, this is where Natural Language Generation, or NLG, comes in.
NLG is an AI-driven software that takes a bunch of data from a bunch of sources, in order to reproduce natural sounding prose. This means taking a huge amount of raw, and meta, data and turning it into consumable text.
It can also:
“Natural language generation uses machine learning to mimic the ways human analysts learn from data and provide recommendations for action," says Kaushal Mody of Accenture.
"As such, the technology turns raw data into human narratives; communicating meaning in the same way people do, and providing complete transparency into how analytical decisions are made.”
Let's have a look into the types of NLG, why don't we?
A subtle, but vital, part of NLG is its ability to grasp grammar. Using a combination of metadata and uh, data, it creates content that sounds human.
This is because the software uses linguistic algorithms to render the data into human-readable text, ensuring that the tone is correct, and the grammar/spelling/syntax are all top notch, with no need for human intervention.
A study conducted by Christer Clerwall reported that: “respondents were subjected to different news articles that were written either by a journalist or were software-generated. The respondents were then asked to answer questions about how they perceived the article—its overall quality, credibility, objectivity, etc.”
As we can see from the results displayed above, respondents found that the software-driven text was found to be more informative, trustworthy, and objective, while journalists’ copy was more pleasant to read. In most of the other categories of measurement, they are neck and neck. And this was four years ago; no doubt a similar study today would yield even better results for the ‘robot journalist copy’.”
It's not all robot peaches and cyber-cream, however. Though, that is a great name for a new-wave band.
As with most tech, it's important to ask:
Though it might save you time in the long run, you have to count on investing some serious man, not robot, hours into the project before it gets up and running.
Also, metadata, keywords and other data sources vital to generating the content have to be identified and structured. Without this, NLG just won't work.
Just like human writers, NLG needs time to learn a brand's specific voice. Though, it might take more time on the robots part - this is the AI/ML aspect of NLG. So, it might take some time before it figures out what makes your brand, your brand.
Plus, it can't completely replace your human writers, especially in terms of creativity. And your editors are safe as the text will still need to be tweaked and reviewed, just in case the prose isn't up to scratch.
But enough of the negatives, let's ask instead: