Develop the next set writebot

Machines might take over every single dirty, dangerous, dull, and decision-making task in the world, but you can and will adapt.


That’s what makes you a human.


Monitor and modify the work


On January 3, 2014, the city of Los Angeles woke up to a magnitude 3.0 earthquake. Within minutes, the story was scooped up and published online by Ken Schwencke. But the article was the work of a machine.


Ken is a programmer and journalist at the Los Angeles Times who created an algorithm to help him auto-report and publish stories about earthquakes.


In the content creation world, for example, you could use an NLG to create a rough draft, and then edit that draft into shape, adding your unique flair to the finished product.




NLGs are machines that create content out of data. Here are two notable examples:

• Read Forbes’s earning reports? You are reading machine-generated content created by Narrative Science’s Quill platform.
• The Associated Press generates more than 3,000 financial reports each quarter using the Wordsmith platform by Automated Insights.

NLGs are also creating product reviews and economic studies. Machines might be behind the content in many places where you find a massive, complex, and confusing amount of data that needs to be parsed and published.