Automation was never about replacing jobs. It has always been about how we can best use the resources we have in a rapidly changing landscape and how we harness technology to run the best journalism company in the world ...»

The reality is that today’s news-writing software is still a long way from replacing human journalists. Which is not to say that what a robot journalist produces isn’t useful; it surely is. Last month I spoke about robot journalism at the news:rewired conference. It was fascinating to see how most of the major news media are exploring how to get robots on staff. So while we’re going to see more robot journalists, it doesn’t necessarily mean fewer human journalists. A recent study by researchers at Oxford University and Deloitte explored how susceptible various jobs were to automation: journalists rank 285th out of 366 jobs, with an ‘automation risk’ of just 8%. ...»

One such company is Apple; as Patterson notes, that automated Apple story freed up reporter Brandon Bailey to focus on this angled, more nuanced report contextualizing the company's earnings along with quotes from Apple executives. ...»

By 2018, the reports says, almost one-third of robotic deployments will be smarter, more efficient robots capable of collaborating with other robots and working safely alongside humans. By 2019, 30% or more of the world’s leading companies will employ a chief robotics officer, and several governments around the world will have drafted or implemented specific legislation surrounding robots and safety, security and privacy. By 2020, average salaries in the robotics sector will increase by at least 60% – yet more than one-third of the available jobs in robotics will remain vacant due to shortages of skilled workers. ...»

What are the variations in Natural Language Generation

Basic NLG: Basic NLG automatically translates data into text via Excel-like functions. An example of this would be a mail merge that restates numbers into language.


•Templated NLG: Here, the user is responsible for writing templates, determining how to join ideas and interpreting the output. Essentially sentence building, it relies on business rules, basic calculations (ex: sum) and templates with boilerplate text to automate content. Templated systems are limited in their ability to draw from multiple data sources, perform advanced analytics, achieve reusability from one project to the next and explain how it came to the story it created, with no understanding of what the user is trying to communicate or their particular domain.


•Advanced NLG: Advanced NLG communicates the way humans do – infusing intelligence and intent into the process from the very beginning. It assesses the data to identify what is important and interesting to a specific audience, then automatically transforms those insights into Intelligent Narratives – insightful communications packed with audience-relevant information, written in conversational language. Backed by a knowledge base, Advanced NLG systems understand the domain and can write contextually about a user’s business at a scale that is not possible by humans. ...»

In natural language generation a computer automatically creates natural language, e.g. English, Chinese, or Greek, from a computational representation. One use of Natural Language Generation is to describe software systems from a formal specification of the software system. Most people do not understand formal languages, but they understand natural languages, therefore it is desirable to have a tool which automatically generates natural language from a formal specification.The process of explaining in natural language is complex and fascinating. The Speaker needs knowledge of the domain to be explained, knowledge about the Hearer's knowledge of the domain; and knowledge of what the Hearer wants or needs to know. ...»

Wordsmith's pricing gives you complete control — only pay for the number of reports you generate. Access starts with an annual contract commitment of $2,000 a month.

NTB has automated its football coverage, with its match reports written by a robot journalist and published within 30 seconds of the end of the game.

Humans no longer check the articles before they are published, trusting the robot to be accurate after extensive testing and work. ...»

Since 2014, the Berliner Morgenpost newspaper publishes daily reports on pollution levels in Berlin... In France, in March 2015, Le Monde also took the plunge and used Syllabs, a specialist in robot writing, to produce 36,000 texts in near real time during the departmental elections. This experience was a world first, both for the context (elections) and for the large number of articles published within only a few hours. ...»

Before this program was implemented, the AP estimates it was doing quarterly earnings coverage for about 300 companies. Now it automates 3,000 such reports each quarter. Of those, 120 will have an added human touch, either by updating the original story or doing a separate follow-up piece. ...»

The journalists’ views on how far the technology could replicate their skills and routines helps explain why they consider it niche. There was a significant amount of comment on the constraints imposed by the technology, in particular its reliance on single, isolated data streams and the need to predict news angles in advance. Journalists thought these constraints meant that automated journalism would lack the context, complexity and creativity of traditional reporting. ...»

But these robots, as sophisticated as they are, can’t approach the full creativity of a human writer. They can’t contextualize Emmy snubs like Matt Zoller Seitz, assail opponents of Obamacare like Jonathan Chait, or collect summer-camp sex stories like Maureen O’Connor. My colleagues’ jobs (and mine, knock wood) are too complex for today’s artificial intelligence to handle; they require human skills like picking up the phone, piecing together data points from multiple sources, and drawing original, evidence-based conclusions. ...»

Had Narrative Science — a company that trains computers to write news stories—created this piece, it probably would not mention that the company’s Chicago headquarters lie only a long baseball toss from the Tribune newspaper building. Nor would it dwell on the fact that this potentially job-killing technology was incubated in part at Northwestern’s Medill School of Journalism, Media, Integrated Marketing Communications. Those ironies are obvious to a human. But not to a computer. At least not yet. ...»

Can Wordsmith write a novel The answer is that it’s theoretically possible—if you can figure out a way to structure the data needed to compose a compelling story. The problem is that by the time you actually create that massive structured dataset about all the variables that would go into a fictional novel, you would probably end up spending way more time creating the dataset and figuring out how to configure Wordsmith to write about it than you would’ve if you just wrote the novel the traditional way. ...»

Thomson Reuters also publishes machine-written articles, using its own in-house technology. One experimental next-gen engine was so impressive that, in a blind taste test, the machines actually came out as more readable than the humans. ...»

A study published last month in Journalism Practice found that a small group of readers could not reliably discern whether a sports article was written by a human or a bot. Those assigned the automated article found it trustworthy and informative, albeit a bit boring. ...»