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

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

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

What are the self-driving cars other industries?
Are there any finished products?
prototypes?
concepts?

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

A recent Accenture report According to this, the information and communication sector, and thus the publishing industry, is one of the biggest future beneficiaries of artificial intelligence. Since I've been at home in the publishing and media world for over twelve years, I took a closer look at the potential of artificial intelligence here.

Because actually — and this much is clear — this sector should have done the Self-driving car in the publishing world have developed; especially as the traditional business model is under great pressure.

Technological requirements in the publishing sector

The necessary technologies in the publishing industry already exist in the form of Natural language processing (NLP) and Natural Language Generation (NLG). As a branch 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. On the one hand, this is due to more powerful hardware and, on the other hand, to new machine learning options.

For sentences and phrases, NLP determines the grammatical structure, the so-called syntax. Based on this, individual words and their units are determined in order to understand the meaning of each individual term on 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, it is not easy to determine the statements of human language, the so-called semantics, without errors — but you have probably already had this experience without a computer.

That is why there are various methods in NLP that help to understand the semantics of a sentence. The following should be emphasized here Named Entity Recognition, also known as entity extraction, sentiment analysis or disambiguation. When 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 using data through algorithms. In areas where there is a lot of data, it is no longer possible to distinguish whether the text was generated by a computer or by a human being. For this reason, NLG is increasingly being used in areas with a particularly large amount of data: stock market information or even sports and weather reports.

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

Why is the publishing industry still stuck?

Every system has to learn at the beginning and is therefore bound to make mistakes. Everyone involved must understand and accept this. Unfortunately, skeptics like to exploit these mistakes and thus cast a bad light on the huge existing potential.

Personally, I find the interplay between humans and machines the most exciting. By the way, this is also the view of Dr. Benjamin Kreck, CTO Intelligent Cloud at Microsoft Germany, who outlined collaboration between humans and machines at the VDZ Tech Summit 2019. According to Dr. Kreck, the challenges of digitization can only be met through close cooperation between journalists and IT experts.

It is therefore not about replacing editors, but about Enriching journalistic work. I am convinced that NLP can do much of the unloved, monotonous work for journalists so that they can concentrate on their true core competence: creating excellently researched content. Because writing weather reports is rarely fun, linking online articles to each other is even less fun.

Technology that supports and doesn't replace us humans will hopefully alleviate some of the prejudices and fears through this approach. 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 the 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 control.

For example, named entity recognition can be used to create a so-called topic model. The computer then knows which topics the editor is writing about. The Internet can now be scanned almost in real time. Which topics are trending right now? 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 major role is the seasonal topic recommendations. As a rule, every editorial team knows from the bottom of their head when which topics are interesting. Nobody is interested in dieting in December, but the topic will be important again from January. Algorithms can recognize seasonalities and provide many exciting inputs that go beyond gut feeling and years of experience.

2. Artificial intelligence in content creation

Artificial intelligence can also support editors when creating content. And by that I don't mean text generation, but assistance in writing texts — without restricting creativity.

When creating an online article, journalists usually have to either rely on the automatic tagging available in the content management system or add tags manually. However, there are smarter alternatives such as editor, one Self-learning interface for New York Times text editing. This editor automatically tags passages of text and creates annotations based on information collected across a range 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 the distribution of content

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.

Conclusion

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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