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This story was not written by a robot
1. 03/06/2020, 12)31 PMThis story was not written by a robot – POLITICO
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This story was not written by a robot
But one day it might be.
Janosch Delcker 3/13/19, 7405 PM CET
2. 03/06/2020, 12)31 PMThis story was not written by a robot – POLITICO
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3. 03/06/2020, 12)31 PMThis story was not written by a robot – POLITICO
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BERLIN — Robot reporters have arrived in the newsroom. Algorithms are
writing up company earnings, covering sports championships and
dabbling in crime and politics. One day, if the techno-optimists are to be
believed, they will be doing much of the work journalists do today.
For now, your loyal correspondentʼs job seems to be safe. Despite the
galloping pace of technological progress, computers are still far from
being able to develop sources, provide high-level analysis or infuse a
narrative with character and color.
But as news organizations integrate artificial intelligence into their
operations, itʼs becoming increasingly clear that the media industry —
and its workforce — is not safe from disruption.
Automatically generated articles
Robots are already performing routine tasks once done by human
reporters.
Once, on mornings when companies published quarterly earning reports,
business journalists at the Associated Press would get up early, wait for
the numbers and hammer out copy as fast as they could.
“Earnings and sports were obvious for us
because they're data driven" — Lisa Gibbs, AP's
director of news partnerships
No longer. In 2014, the AP automated the process. Now, a software
monitors the earnings, then spits out a basic story onto the wire in under
five minutes — faster than any reporter ever did the job.
Similarly, the newswire has automated its coverage of minor league
baseball and college basketball.
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“Earnings and sports were obvious for us because they're data driven,”
said Lisa Gibbs, AP's director of news partnerships. “The sources of data
are clean, and there is a value of having information about them out very
quickly.”
The agency estimates it will publish roughly 40,000 automatically
generated articles by the end of this year — still just a fraction of the
more than 700,000 articles, including revisions, it puts out each year.
The Washington Post has been using machines to cover high-school sports | Brendan
Smialowski/AFP via Getty Images
The AP is not alone. Newsrooms around the world have begun using
software to report on sports scores, earnings' reports and election
results. Bloomberg News has automated its earnings coverage. The
Washington Post uses software to cover high-school games. The Los
Angeles Times has a bot tweeting about earthquakes.
In Europe, the Austrian Press Agency is planning to use software during
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the upcoming European Parliament election to quickly push out articles
with election results from each of Austria's more than 2,000
municipalities. Norwegian news agency NTB said it has been running
automatically generated stories about football games for three years, and
plans to expand its coverage to the country's minor leagues this year,
bringing its automated coverage to up to 170,000 games per year.
Templates
Whatever the subject of the coverage, the process for this kind of
automated journalism is similar.
First, reporters identify reliable data sets. They team up with
programmers to write a template spelling out what a story needs to say,
how it is supposed to sound and what possible variations there should
be: Have the earnings of a company “soared,” or have they
“plummeted”?
After any glitches are ironed out, the software is let off its leash to write
its stories. But this doesnʼt mean that the work of the journalists is done:
At the AP, reporters still make tweaks to headlines, or mess around with
some of the language. And they continue to monitor developments that
might make their templates out of date.
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Car chases in Southern California have become so ubiquitous, researchers were able to come
up with a template for writing news stories about them. Feed in a few key details, and out
comes journalistic prose — or something approaching it | Getty Images
If the U.S., for instance, decides to make changes to its tax law, business
editors must decide whether to turn off automation because the risk of
stories being inaccurate or incomplete is too high.
“We have editors now who used to spend their time writing and editing
earning stories,” said Gibbs, who led the APʼs business desk when it first
introduced automated reporting. “Now itʼs about maintaining a very large
database.”
Her agency currently has no plans to expand the use of automatically
generated articles into other areas, she said, adding that “we're not in the
business of automating things just because we can.”
Storytelling
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In the fall of 2015, researchers from “Structured Stories,” a now-dormant
academic project, approached reporters covering car chases in a local
NBC Los Angeles newsroom with a request.
After the journalists filed a story, they were asked to enter facts about the
incident into a database.
Every car chase is different, but if you think about them as narrative
structures, they all have reoccurring plot elements. Every chase has a
triggering event: someone is killed or a police officer notices a speeding
vehicle. And every pursuit has an ending: an accident, a surrender, a
shooting. By compiling enough examples, the researchers were seeking
to teach their machines how to cover a car chase.
And it worked. The subfield of artificial intelligence underlying most
newsroom robots is called “natural language generation” — or NLG. The
basic idea is that if you want a computer to be able to write something,
you must provide it with the information in a form it is able to process,
and then you have to teach it how to use it.
Using examples from more than 60 car chases, the researchers came up
with a way to encode the races so that a computer could understand
them and a template for a typical story about an L.A. car chase. When
they fed it data from another chase, the software spit out an article that
read like classic journalistic prose:
Driver Drives Off 300 Ft Cliff During Pursuit
July 13th, 2015 (Point Fermin Park, San Pedro, California) — A vehicle
pursuit that began in Wilmington during the late evening of July 13th later
ended with the crash of the suspect in Point Fermin Park, San Pedro. The
incident began at about 11L00 PM when an unidentified driver, driving a
Toyota Prius, fled from officers of the Los Angeles Port Police following a
traffic stop on Pacific Coast Highway in Wilmington. The suspect was
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then pursued by the LAPP along Alameda Street to San Pedro, and then
further along Alameda Street to Point Fermin Park in San Pedro. The
incident concluded with the crash of the suspect in Point Fermin Park,
where the suspect drove over a cliff. The unidentified suspect was
injured. Referring to the incident, witness Manuel Castro said "We peeked
our heads and it was just a gray Prius and we saw the wreckage and the
cops over here." The suspect was treated for injuries at the scene and
was expected to be arrested.
What made this experiment different from articles about quarterly
earnings reports is that the machines werenʼt just punching numbers into
a template. They were recounting a series of events. In other words, they
were telling stories.
The outcome proved that "itʼs possible to represent most news stories,
and certainly formulaic news stories, as data, and it is also possible to
generate news products, say articles, that are very similar to what
journalists produce,” said David Caswell, who oversaw the "Structured
Stories" project before joining the BBC last year as the executive product
manager of its News Labs incubator.
Mass production
Robot journalists are not yet able to produce articles by themselves,
beyond stenography-style reporting about straightforward facts. Even in
cases where theyʼre able to write the story, they still need human
reporters to tell them how to process information first. They serve
primarily as virtual assistants, allowing one journalist to do work that
might have required dozens — if not hundreds.
Thatʼs the idea behind Radar, an “automated news service” that offers a
glimpse at the cutting edge of computer-assisted journalism.
Radar — the name is an acronym for “Reporters and Data and Robots” —
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was launched in September 2017 as a joint venture of the U.K.'s Press
Association and startup Urbs Media, with more than €700,000 of funding
from Googleʼs News Initiative.
It seeks to use the vast but often untapped troves of public data that is
released by the British government and other institutions, some of which
drills down to the level of the U.K.'s hundreds of local authorities.
“In a way, these tools make it more likely that
more of us will be able to do more sophisticated
reporting" — Lisa Gibbs
A story produced by Radar starts like any other: with an idea. The groupʼs
reporters root around data sets, looking for something interesting. "To my
mind, the best judge to a story is still a human journalist rather than a
machine," said Radar Editor-in-Chief Gary Rogers.
Once the team has identified a subject worth picking up — say, how often
ambulances are delayed across the U.K. — reporters might make some
phone calls or conduct interviews to understand the broader context and
harvest general quotes for their articles.
Only then does the automation begin. The reporters write a template,
which will allow them to generate hundreds of individual articles from just
one data set — in this case, noting how often ambulances are delayed in
the community and how that compares to the national average.
They add some analysis and feed it into their NLG software, which spits
out hundreds of "localized" articles Radar offers to its subscribers.
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Newsroom executives interviewed for this article argued that rather than making journalists
redundant, robot reporters will allow them to focus on elements of the job that add more value |
Xavier Galiana/AFP via Getty Images
Sometimes, local newsrooms publish the stories as they come in;
sometimes, they have their own journalists pick them up, do additional
reporting and turn them into larger features.
Radarʼs team of five reporters, plus the startupʼs two founders, hammers
out around 8,000 stories per month, covering issues ranging from crime
to transport, education, environment, health and social policy.
Some of the data they use has been available for years but had remained,
so far, untouched by journalists — partly because so many local U.K.
newspapers have shut down, and partly because much of the material is
too detailed for it ever to have been profitable for human reporters
working on their own.
"We're using NLG technologies as a writing tool, in effect," said Rogers.
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Tailored content
Newsroom executives interviewed for this article argued that rather than
making journalists redundant, robot reporters will allow them to focus on
elements of the job that add more value.
Historically, in most newsrooms, only a small fraction of reporters have
been free to truly dig deep into stories. Many of the tasks being
performed can be — or one day soon could be — done by robots.
“In a way, these tools make it more likely that more of us will be able to do
more sophisticated reporting,” said Gibbs, who now leads APʼs newsroom
AI efforts.
Traditionally, most journalism is a single-use product. A reporter is
assigned a story, gathers information, and then writes. Over time, they
build up sources and knowledge, and get better at what they do — but
when it comes to the writing part, they have to start fresh each time.
“The editorial side of journalism is going to be
more important than ever. But it’s going to be
completely different" — David Caswell, BBC
That's where robot-journalists could revolutionize the business,
technologists like the BBCʼs David Caswell believe. He envisions that
instead of writing articles, breaking news reporters of the future will work
on templates that — fed with new data — can produce a limitless number
of stories.
Newsrooms will build up libraries of templates, enabling them to quickly
produce stories, sometimes for multiple audiences. The reporting about a
car chase in L.A. could, for example, be run through five templates,
producing, in turn, a short summary, a listicle, a colloquial blog post, a
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colorful article with lots of detail and a version in Spanish for the cityʼs
Latino community.
“The editorial side of journalism is going to be more important than ever,”
said Caswell. “But itʼs going to be completely different.”
'Double-edged sword'
For some journalists, that will mean new opportunities. For others, it
could mean having to learn new skills — or risk losing their jobs.
Some fields of journalism — investigative projects, magazine features, in-
depth political and business analysis, op-eds and commentaries — seem,
for now, to be safe from technologies like NLG.
Templates and databases can only do so much. “You canʼt automate
creative writing,” said Alexander Siebert, one of the CEOs of Berlin-based
tech company Retresco. “Artificial intelligence can grasp the structure of
grammar and turn data into creative language — but the ‘creative ideaʼ is
and remains in the hands of humans. It will take many additional years of
research until this can be done by machines.”
But other newsroom areas could see significant disruption.
"There are new ways of doing journalism that will be completely
accessible and possible for new generations of journalists,” said Caswell.
“But itʼs maybe harder for older journalists to adapt to those kinds of
thinking."
"If the emphasis on AI … is motivated to further
reduce costs and resources — meaning
people’s jobs — then that’s a problem" — Sarah
Kavanagh, Senior NUJ official
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To write templates, journalists will have to learn to look for recurring
patterns in whatever they're covering — similarly to what computer
scientists and developers do when they write code. Such computational
thinking is a skill most working journalists currently lack.
“Itʼs essential that employers provide training,” said Sarah Kavanagh, a
senior campaigns and communications officer at the U.K.ʼs National
Union of Journalists.
Kavanagh described recent efforts to automate reporting in the industry
as a “double-edged sword.” While her union welcomes the use of
technology, including AI, to enhance reporting — particularly in
underserved news deserts — she also warned that the option to
automate reporting could bring newsrooms to lay off reporters.
Similarly, disruptive innovations had been used in the past, she warned,
"in a way that is it not supporting quality, sustainable journalism, but
about cutting costs.”
“If there are technological tools that are developed that will help people
to dig into lots of information and to save time in their work of journalists,
then … they should be welcomed,” she said. “But if the emphasis on AI …
is motivated to further reduce costs and resources — meaning peopleʼs
jobs — then thatʼs a problem.”
Fake news
Just what changes artificial intelligence will ultimately bring to the
newsroom is still unclear. While there are distinct limits to NLG, other
avenues are just being explored.
“Itʼs not far-fetched to assume that in five to 10 years from now,
technology will be at a place that, depending on the story one is writing,
the machine will just make suggestions about sentences, or an outline for
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the piece,” said Seth Lewis, an emerging media researcher at the
University of Oregon.
Enter "deep learning."
Unlike NLG, where computers are told what to do, deep learning analyzes
vast troves of data and learns from that experience. That makes it highly
effective, but it also turns computers into black boxes, making it
impossible to fully understand the reasons behind their decisions.
In February, the American nonprofit OpenAI made headlines when it
said it had created a software that uses "deep learning" to generate text
and that it is so good — and has so much potential for misuse — that it
wouldnʼt release its full research.
Human journalists might not be superseded by robot reporters just yet | Image via iStock
It was trained by being fed 8 million documents. Using an approach
similar to the predictive text generator on a smartphone, the program
produces articles that read like near-perfect prose by predicting what
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word is likely to follow another one.
The only problem: As authentic as the stories may seem, the facts they
contain are completely made up. Asked to generate an article about the
OECD, the software created a straightforward news piece, including a
fabricated quote attributed to a chairperson at the organization.
This makes the program, arguably, a potential competitor to fiction
writers, novelists or poets, who aren't necessarily constrained by the
facts. But it's much less useful for reporters, whose job is to stick to the
truth.
For journalists, including your loyal correspondent, that should come as a
relief. We wonʼt have to dust off our resumes. At least not yet.
Judith Mischke contributed reporting.