These are the humans trying to give our jobs to robots
There’s been a lot of talk lately about Narrative Science, its boss Kristian Hammond, and their algorithmic journalist robots of the future. Most of the controversy has been over a few audacious comments, as most controversy usually is (via Wired):
Last year at a small conference of journalists and technologists, I asked Hammond to predict what percentage of news would be written by computers in 15 years. At first he tried to duck the question, but with some prodding he sighed and gave in: “More than 90 percent.”
He also predicted that a computer will win the Pulitzer Prize by 2017. But that’s just talk — from reading what his algorithms have done, it’s hard to expect a Pulitzer, but it’s not as easy to rebuke the 90% assumption.
Narrative Science is one of several companies developing automated journalism software. These startups work primarily in niche fields—sports, finance, real estate—in which news stories tend to follow the same pattern and revolve around statistics.
Take the financial articles that NS writes for Forbes, as considered a little later in the article:
Don’t miss the irony here: Automated platforms are now “writing” news reports about companies that make their money from automated trading. These reports are eventually fed back into the financial system, helping the algorithms to spot even more lucrative deals. Essentially, this is journalism done by robots and for robots. The only upside here is that humans get to keep all the cash.
Following the diplomatic/commodity trail that influences stock prices, or tracking stats and numbers in sports to find stories, may eventually become an obsolete task for us humans as robots begin to cover them more efficiently, and faster. And, having begun to crawl through Twitter for election coverage, Narrative Science’s scope may (soon! soon!) slowly grow.
FJP: But as for what this post covers, the concern is a lot like other problems people have with today’s journalism. In the same way that programmers or bloggers won’t replace columnists and reporters, but will instead facilitate, complement, and in all sorts of ways share the new workload, so too might Narrative Science-esque algorithms cover some of the responsibilities that future journalism expects, but which are difficult/unreasonable/impossible for, say, a journalist from ten years ago to handle.
Newt Gingrich received the largest increase in Tweets about him today. Twitter activity associated with the candidate has shot up since yesterday, with most users tweeting about taxes and character issues. Newt Gingrich has been consistently popular on Twitter, as he has been the top riser on the site for the last four days. Conversely, the number of tweets about Ron Paul has dropped in the past 24 hours. Another traffic loser was Rick Santorum, who has also seen tweets about him fall off a bit.
While the overall tone of the Gingrich tweets is positive, public opinion regarding the candidate and character issues is trending negatively. In particular, @MommaVickers says, “Someone needs to put The Blood Arm’s ‘Suspicious Character’ to a photo montage of Newt Gingrich. #pimp”.
Stilted and inelegant to be sure, the computer generated story was created by Narrative Science, an Illinois-based startup, that’s combining machine learning, data analysis and artificial intelligence to produce short and long form articles from data heavy industries such as real estate, finance, sports and polling.
For example, Narrative Science technology creates computer generated sports recaps for the Big Ten Network, a joint venture between the Big Ten Conference and Fox Networks.
The Narrative Science software can make inferences based on the historical data it collects and the sequence and outcomes of past games. To generate story “angles,” explains Mr. Hammond of Narrative Science, the software learns concepts for articles like “individual effort,” “team effort,” “come from behind,” “back and forth,” “season high,” “player’s streak” and “rankings for team.” Then the software decides what element is most important for that game, and it becomes the lead of the article, he said. The data also determines vocabulary selection. A lopsided score may well be termed a “rout” rather than a “win.”
Glass half empty: journalists will be automated out of their jobs.
Glass half full: journalists will be freed from writing drudgey news summaries and can focus on more significant work.