Journalism is a creative human practice. This, according to widespread opinion, makes it harder for robots to replicate. However, writing algorithms are already widely used in the news media to produce articles and thereby replace human journalists. In 2016, Wordsmith, one of the two most powerful news-writing algorithms, wrote and published 1.5 billion news stories. This is comparable to or may even exceed what was written and published by human journalists. Robo-journalists’ skills and competencies are constantly growing. Research has shown that readers sometimes cannot differentiate between news written by robots or by humans; more importantly, readers often make little of such distinctions. Considering this, these forms of AI can be seen as having already passed a kind of Turing test as applied to journalism.
Artificial intelligence (AI) is usually defined in two ways that, in a sense, are contradictory. On the one hand, artificial intelligence is intelligence that mimics human intelligence and/or behaviour. On the other hand, artificial intelligence is intelligence that is opposite to natural, i.e., human, intelligence. As Russel and Norvig describe it, the first type of definitions measures the success of AI “in terms of fidelity to human performance”, while the second type of descriptions measures the success of AI “against an ideal performance measure, called rationality”  (p. 1).
The first approach is called “a human-centered approach”, within which researchers assess if AI is acting humanly or thinking humanly. The second approach is called “a rationalist approach”, within which researchers assess if AI is acting or thinking rationally.
Russel and Norvig’s paradigm can be interpreted through a set of relations between AI and humans. AI either simulates human nature or opposes it.
In the first case, AI simulates humans (either their acting or their thinking) until reaching a level of complete likeness. This is the approach for which the Turing test is applicable.
In the second case, rational AI opposes the irrationality of humans and “does the ‘right thing’, given what it knows”  (p. 1). This is the approach to which many sci-fi scenarios involving the rebellion of machines refer, starting with Asimov’s “I, Robot” and including Hollywood’s “Terminator” franchise.
So, AI either performs as a human and exceeds humans as a human simulation or performs as a “smarter” entity and exceeds humans as a being of the next evolutionary level.
Noticeably, both the “simulating” and “opposing” approaches imply such scenarios in which AI substitutes and then replaces humans, either by mimicking or by outdoing them, as inevitable.
The idea of the substitution of humans by artificial intelligence is the ultimate completion of McLuhan’s idea of media as “extensions of man” . Media have extended and enhanced different human faculties over the course of human evolution and civilization’s development. Within such an approach, the advent of artificial intelligence can be seen as an inevitable outcome of the evolution of media. Vice versa, the evolution of media inevitably results in the advent of artificial intelligence (at least at the human stage of evolution, if considered within Teilhard de Chardin’s paradigm of mega-evolution) .
To that end, artificial intelligence is the final point of any sufficiently long and conscientious media study. A thrilling fact is that scholars and experts are already discussing this final point in practical contexts.
While the concept of artificial intelligence is quite profoundly developed in sci-fi literature and popular movies, the real development of artificial intelligence is very practical, not that dramatic, and therefore often invisible. A popular view of AI’s development is that this development is occurring out of
curiosity. Curiosity may play a role, but in reality, there are some practical industrial and market demands for AI.
There are industries that are interested mostly in the computational capacity of AI, such as in air traffic control, social media algorithms, or virtual assistants like Siri or Alexa. Their task is to calculate and cross-analyze big data, with some predictive outcomes. This can be characterized as “very narrow” AI. They are obviously just helpers to humans.
However, there are at least three industries that seek, for very practical reasons, not just to develop better AI, but to completely substitute humans with AI. They are:
1) The military. Smart war machines are expected to make human-style decisions immediately on the battlefield, which increases the efficiency of their performance while reducing human casualties .
2) The sex industry. Smart sex dolls are expected to completely substitute sex partners and then probably even life partners for humans by simulating human sex and communication behaviour . Then, they will highly likely offer “super-human” sex and communication experiences, as any new medium first performs old functions and then creates its own environment.
3) The media. News-writing algorithms aim to eventually replace human journalists, no matter how this innovation is currently thought of. Even if someone sees robo-journalists just as helpers, a kind of intern in the newsrooms, the ultimate completion of the idea of news-writing algorithms is for them to write the news instead of humans and in a way no worse than humans (in fact, much better, faster, cheaper, and with higher productivity).
Some other areas or industries can also be listed here. The idea is that these areas represent the approach that suggests AI must replace humans. Not just some selected human functions but the very physical presence of humans.
However, a reservation here needs to be made regarding these three industries. The military wants to replace humans, but they do not want smart war machines to be indistinguishable from humans. The application of AI in military actions does not require human likeness; military AI is non-human in its appearance and performance. It replaces people through their distant and enhanced representation or multiplication (as in the case of drone swarms; a drone swarm is quite an interesting new implementation of McLuhan’s idea about media as extensions of men).
AIs for the sex industry and for the media are different. They fully implement the very spirit of the Turing test: AI must be indistinguishable from humans by humans. This is important both for the sex industry and for the media.
Strange as it may seem, the transition from a narrow AI to a general AI may happen in the sex industry and the media.
The sex industry’s demand for AI is well supported by market forces  and cultural changes . However, the application of AI in the sex industry is complicated, as it requires physical embodiment and extremely complex behavioural simulation. So, sex-based AI will most likely arrive later than media-based AI, which does not require any physical embodiment and “personal” behavioural peculiarities (so far).
Thus, media scholars are in a position of particular responsibility regarding AI studies. First, as is stated above, any media study that is conducted far enough and fair enough leads to AI. Second, the prototype of AI itself (at least “simulating” AI) will relate to media (including social media).
In fact, media-based narrow AI is already here; it is only the inertia of perception together with the defence mechanisms of people both in the industry and in the audience that prevent the public from the admission of the fact that media-based narrow AI is already among us. Many have even interacted with it, most likely without knowing it.
The paper presents the current state-of-the-art about robo-journalism in the following way.
First, several main areas of the narrow AI’s application in journalism are reviewed:
– data mining;
– commentary moderation;
– topic selection;
– news writing.
Second, several cases of real-life journalistic Turing tests, though naively conducted, are described. On the basis of these cases, two main journalistic functions are reviewed regarding their execution by robo-journalists:
– the ability to process information;
– the ability to express information (writing in style).
Third, the paper considers two main popular counterarguments about robots’ “incapability”:
– the inability to create;
– the inability to understand beauty (to write in style).
The review shows that, for every argument on what robots cannot do, there is a more convincing argument for what robots can do instead (and humans cannot).
Finally, in the section “Roadmap for AI in the media—and beyond” the paper offers some speculations on possible ways for the transition of narrow media-based AI into general AI.
The paper reviews a wide range of academic and popular publications on automated journalism. As the subject is presented only through its manifestation on the market place, the most important evidence on the current state-of-the-art about robo-journalism can be pulled out from different expert observations and also from reflections made by people in the media industry. The paper is organized as a systematized review of such observations and reflections followed by their analysis and some futurological speculations built upon this analysis.
The main idea of the review is that robo-journalism:
– outdoes the organic form of journalism in all characteristics regarding data processing;
– can compete with humans in the part of the job that relates to writing and style.
The most important revelation of the paper regarding the style of robo-journalism is that robots do not have to write better than humans; they have to write good enough (in order to be indistinguishable and to be hired).
In fact, there is no need for robots to prove they can do journalism better than humans. Such a view is becoming outdated. On the contrary, we are about to enter a market in which human journalists will be required to prove their capacity to perform no worse than robots. The paper aims to show why and how this will happen.