You Won’t Be Able to Conclusively Tell Whether It Was Written by a Human

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Tuesday was a great day for W. Roberts, as the junior pitcher threw a perfect game to carry Virginia to a 2-0 victory over George Washington at Davenport Field. Human or machine? Here’s the answer: It was written by a machine. (I wrote the opening paragraph.) See, when it comes to a computer-generated story, there aren’t any telltale signs that it’s manufactured by a machine. That’s how good this machine-written content is getting. Content creator versus machine Perhaps you have heard: machines are taking over the world. Not H.G. Wells-style, with fighting machines equipped with heat rays and “black smoke.” No, this invasion is more prosaic: machines are kicking us out of the cubicle. In fact, according to a 2013 Oxford Martin School study, 47 percent of U.S. jobs are susceptible to machine automation. But this shouldn’t surprise us. Machines have been displacing us for more than a century. During the industrial revolution, machines took away the dirty and dangerous jobs. In the 20th century, machines took away the dull ones. And now they are after jobs that require decision-making. The 21st century has ushered in an era where Natural Language

Generation (NLG) machines are making headlines — and yes, that Whatsapp Number List statement has a double meaning. What is an NLG? 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. They are even producing content in a voice that resonates with the content’s specific audience. Why you shouldn’t fear NLGs “


Insights from a curious and fertile mind” are missing from computer-generated content. As Jordan Teicher writes: The upside here is crucial: Those who break news may get a few minutes of fame before their stories are recycled by other outlets, but those who produce original research and analysis have material that typically leads to greater recognition and is harder to rip off. Furthermore, a machine can’t report on the aftermath of a devastating hurricane, interview eye witnesses or experts, or live blog a conference (which involves decisions about what is meaningful and what is not meaningful). That takes brains on the ground.