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Thursday, April 25, 2019

What is machine learning and why should I care? | Tow Center - Columbia Journalism Review

You may not realize it, but you’ve probably already used machine learning technology in your journalism, explains Nicholas Diakopoulos, assistant professor at Northwestern University School of Communication, author of the forthcoming book "Automating the News: How Algorithms are Rewriting the Media" on automation and algorithms in news media.
 
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Perhaps you used a service like Trint to transcribe your interviews, punched in some text for Google to translate, or converted the Mueller Report into readable text. And if you haven’t used it yourself, machine learning is probably at work in the bowels of your news organization, tagging text or photos so they can be found more easily, recommending articles on the company website or social media to optimize their reach or stickiness, or trying to predict who to target for subscription discounts.

Machine learning has already infiltrated some of the most prosaic tasks in journalism, speeding up and making possible stories that might otherwise have been too onerous to report. We’re already living the machine-learning future. But, particularly on the editorial side, we’ve only begun to scratch the surface.

To be clear: I’m not here to hype you on a fabulous new technology. Sorry, machine learning is probably not going to save the news industry from its financial woes. But there’s nonetheless a lot of utility for journalists to discover within it. What else can machine learning do for the newsroom? How can journalists use it to enhance their editorial work in new ways? And what should they be wary of as they take up these powerful new tools?...

Finally, because of the wide variety of machine-learning approaches available, part of the challenge for journalism is figuring out which techniques are appropriate (and useful) for particular journalistic tasks. One way to tackle this challenge would be to invite experts in machine learning to take up residence in newsrooms where they could determine which strains of machine learning could be most useful to the journalists there. Another possibility might be to invite editorial thinkers to do fellowships in computing environments. With more collaboration over time, we can flesh out where and when machine learning is most useful in journalism, and thus broaden the capacities of even the largest newsrooms to investigate the secrets hidden in the vastness of digital data.

In summary, I’m bullish on the capabilities and opportunities that machine learning presents to editorial work, but also cautious enough to remind readers that machine learning is not the answer to every journalistic task. The grand challenge moving forward is to experiment with when and where the different flavors of machine learning truly do bring new editorial value, and when, in fact, we may just want to rely on good ol’ human learning.
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Recommended Reading

Automating the News:
How Algorithms Are Rewriting the Media
Source: Columbia Journalism Review