RISK PROBABILITY BASED ON THE SEMINAL FREY AND OSBORNE STUDY
Professional Group characteristics and similar occupations
Authors, Journalists and Linguists
Authors, journalists and linguists conceive and create literary works; interpret and communicate news and public affairs through the media; and translate or interpret from one language into another.
Tasks performed usually include: writing literary works; appraising merits of literary and other works of art; collecting information about current affairs and writing about them; researching, investigating, interpreting and communicating news and public affairs through newspapers, television, radio and other media; translating written material from one language to another; simultaneously translating from one language to another. (ILO, 2018)
- Newspaper editor
- Newspaper reporter
- Sports writer
- Sub editor
- Translators, Interpreters and Other Linguists
The internet (online news, social media) has broken down the traditional news monopolies that existed prior to the 2000s by creating online competition and fragmenting their access to advertisers (Petty, 2017). This day and age it is possible for a media agency to exist online and have access to massive audience and advertisers by continually generating reports and news-bites and ensuring high engagement (The Economist, 2006).
Much of what journalists report or post in social media can now be automated using machine learning tools, such as Narrative Science ( Natural Language Generation, 2018), that is able to create narratively sound reports from analysing data in a natural language. In fact, if you’ve read a financial earnings report in the past year or two, you’ve probably read an article or press release generated by a machine (Reddan, 2017). The first places these programs will be used is in the financial/ts reports, domestic or foreign election results and any other simple reports, which rely heavily on data and number manipulation. The quality of those machine generated reports are virtually indistinguishable to a human’s due to the given format and language used.
However, these software is not even close to the limit of their impact. Online platforms already exist that “scrape” content from news sites and creatively “rewrite” it for reposting. The reason of the low automation score is the fact that journalism as a collection of networks and human interactions, as well as critical thinking, creativity, on foot investigations etc. are still beyond the capacities of these technologies (Holmes, 2016).
Quill is a software from the company Narrative Science that has the ability to gather, analyse and integrate data to text with the structure and coherence that imitates a real person writing the report. “..Quill was being used to report on baseball games for TV and online sports outlets, and company earnings statements for clients such as Forbes…” (A.I. that automatically communicates relevant information at scale, 2018) all the way down to taking into account the emotional impact a story can have in the intended audience “..writing about sports for an audience likely to favour a particular team, for instance, Quill can write a story that softens the blow of a loss…”
(A.I. that automatically communicates relevant information at scale, 2018)
In 2015, The New York Times activated its own AI project known as Editor, that aimed at teaching the machine the processes of journalistic reporting by using tags for the important parts of an article. Through this “learning” the journalist has now on her tips all relevant historic information related to a new story thus aiding the journalistic process by cutting down research time, thus freeing more space for the human interactions that are unreplaceable by software (Editor , 2015).
Digital publication DIGIDAY reported that The Washington Post, used machine learning technologies and its own home developed AI to post around 850 digital articles in 2017 (Moses, 2018). The AI creates short reports on sports and election results for the most part.