Artificial intelligence: On the trail of the "self-driving car

Stephan Heck

Artificial intelligence will change many, indeed most likely all, industries. And this change is already clearly picking up speed. When you think of artificial intelligence or AI, self-driving cars from Audi, Tesla or Waymo may come to mind.

Artificial intelligence will change many, indeed most likely all, industries. And this change is already clearly picking up speed. When you think of artificial intelligence or AI, self-driving cars from Audi, Tesla or Waymo may come to mind.

The possibility of autonomous driving is an easy-to-understand example of the use of artificial intelligence and holds a great deal of fascination for me. But at least as fascinating to me are the questions:

What are other industries' self-driving cars?
Are there finished products yet?

If not, then it's just a matter of time and money.

According to a recent report by Accenture, the information and communications sector, and therefore the publishing industry, is one of the biggest future beneficiaries of artificial intelligence. Since I have been at home in the publishing and media world for over twelve years, I have taken a closer look at the potential of artificial intelligence here.

The impact of AI on Industry Growth (Publishing & Media)

Because actually - and this much is clear - this sector should have developed the self-driving car of the publishing world long ago; especially since the traditional business model is under great pressure.

Technological requirements in the publishing industry

The necessary technologies in the publishing industry already exist in the form of Natural Language Processing (NLP) and Natural Language Generation (NLG). A subfield of artificial intelligence, NLP enables computers to understand, interpret, and generate human language. It has been researched since the 1940s and has made enormous leaps in development, especially in recent years. This is due on the one hand to more powerful hardware, and on the other to new machine learning possibilities.

NLP determines the grammatical structure of sentences and phrases, the so-called syntax. Based on this, individual words and their units are determined in order to understand the meaning of each term at the lexical level.

The meaning of the entire sentence can be derived from the structure of the sentence, the meaning of the individual terms and their context. However, to determine the statements of human language, the so-called semantics, without errors is not very easy - but you have probably already made this experience without a computer.

Therefore, there are different methods in NLP that help to understand the semantics of a sentence. Named Entity Recognition, also called Entity Extraction, Sentiment Analysis or Disambiguation are to be emphasized here. If these techniques are used together, a computer understands us humans excellently.

Another area is Natural Language Generation (NLG), which deals with the production of text based on data by algorithms. In areas where there is a lot of data, it is no longer possible to distinguish whether the text was produced by a computer or a human. Therefore NLG is increasingly used in areas with a lot of data: Stock market information or also sports and weather reports.

So I hold: The basic requirements for the self-driving car of the publishing world are given.

Why isn't the publishing industry any further ahead?

Every system has to learn at the beginning and therefore inevitably makes mistakes. Everyone involved must understand and accept this. Unfortunately, however, skeptics like to exploit these mistakes and thus cast the enormous existing potential in a bad light.

Personally, I find the interaction between man and machine the most exciting. Incidentally, this is also the view of Dr. Benjamin Kreck, CTO Intelligent Cloud at Microsoft Germany, who outlined a collaboration between man and machine at the VDZ Tech Summit 2019. According to Dr. Kreck, the only way to meet the challenges of digitization is through close cooperation between journalists and IT experts.

Artificial Intelligence in the Publishing Industry: Lecture by Dr. Benjamin Kreck Image
Dr. Benjamin Kreck during his presentation "Innovations in Data & AI - Opportunities and Challenges for the Publishing Business".

So it's not about replacing editors, but about enriching journalistic work. I'm convinced that NLP can take a lot of the unpopular, monotonous work off journalists' hands, allowing them to concentrate again on their true core competence: creating excellently researched content. After all, writing weather reports is rarely fun, and linking online articles to each other even less so.

Technology that supports us humans, not replaces us, through this approach can hopefully mitigate some of the prejudices and fears. In the following sections, I would now like to present three key areas in which artificial intelligence has particularly great potential for publishers.

1 Artificial intelligence in topic search

Finding the right topics is always a major challenge for journalists. A machine can help: For example, it can process and interpret patterns in data to an extent that is simply impossible for humans to master.

For example, Named Entity Recognition can be used to create a so-called topic model. The computer then knows which topics the editorial team is writing about. Now the Internet can be scanned almost in real time. Which topics are currently in vogue? Do the suggested topics fit the format? In this case, interesting topic suggestions can be passed on to the editorial team.

Another use case in which the created topic model plays a key role is seasonal topic recommendations. As a rule, every editorial team knows from its gut which topics are interesting when. Nobody is interested in diets in December, but the topic becomes important again in January. Algorithms can recognize seasonality and provide exciting input that goes beyond gut feeling and years of experience.

2 Artificial intelligence in content creation

Artificial intelligence can also support editors in the creation of content. And I don't mean text generation, but support in writing texts - without restricting creativity.

When creating an online article, journalists usually have to rely either on the automatic tagging available in the content management system or add tags manually. However, there are smarter alternatives such as Editor, a self-learning text editing interface from the New York Times. This editor automatically tags text passages and creates annotations based on information gathered through a series of neural networks.

Other exciting use cases in content creation include automatic search engine optimization, suggesting related internal and external articles, automatic translation, image recognition, or suggesting synonyms that show higher search volume.

3 Artificial intelligence in content distribution

Linking online articles is no fun, and it's also very time-consuming. How many editorial departments can afford to optimize all links over and over again? Adjusting links to three-year-old articles so that they redirect to an article that has just been published? A perfect challenge for an intelligent algorithm.

This algorithm should take into account specific goals (e.g. conversion to subscribers) and optimize all links with regard to these goals. Links are therefore set dynamically, in such a way that the goal is achieved as much as possible and Google still likes you.

Affiliate links can also be optimized in the same way: dynamically generated in each article, with the goal of increasing sales.


These are just a few of the many examples of how artificial intelligence can help journalists work more efficiently. Unfortunately, due to the complexity of the topic as well as the fear of new technologies, magazine and newspaper publishers are still often overwhelmed and have so far tended to observe instead of acting more courageously.

However, there is another way. Because some publishers are already using artificial intelligence at full force and are developing a decisive edge. These media companies will benefit disproportionately from their active pioneering role.

Finally, I want to make one thing clear: I don't believe software can ever replace journalists. Why? Simply because human creativity is an irreplaceable piece of the puzzle in creating high-quality content.

However, I also think that editors don't need to spend time linking articles to each other or finding suitable keywords. Artificial intelligence can do that better and faster. And who knows, with a little imagination, the self-driving car of the publishing world may not be too much longer in coming.

With content intelligence, you know more, are more productive and increase your revenue. Want to learn more about AI in publishing? Then arrange a meeting with one of our experts today.

The article was originally published in Wirtschaftsinformatik & Management (Springer Fachmedien).

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