More Relevance in Content Marketing Through AI

It would be conceivable, among others, for AI to adapt texts that have

already been created to the linguistic habits of different target groups, so that

a medical text, for example, could be understood by both doctors and ordinary people by having medical terms explained.

It is merely a question of time until algorithms are able to write texts for

any target group whatsoever. In the future, AI will presumably even be able

to produce excellent content at an enormous speed. This way, texts can be

individualised and personalised more easily so that all essential information

is included via a reader and which affects the written and adapted text.

AI becomes very familiar with the readership in this process and can uti-

lise all information about the recipient in such a way that every single piece

of content is unique. Just imagine the content that would be produced if AI

could read out your entire (public) Facebook profile and were able to use

this information for matching content.

In principle, it would suffice if retargeting were not used for advertising

but used for the targeted addressing of content. In content marketing, algorithms are more and more frequently taking over this task, which is neces-

sary for the targeted play out of the content as well. In addition, contents are

played out in an appropriate context (content recommendations). Instead of

one article for all, personalised content will be possible on the basis of AI,

and which are closely based on the reader’s respective range of interests. The

result of this is unique contents in the logic of mass customisation because

the AI knows their readers and responds in a personalised way. Everyone

receives their own personal content.

Comments

Possible Limitations of AI-Based Bots

The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...

Robot Journalism Is Becoming Creative

Algorithms are able to automatically search the Web for information, pool it and create a readable piece of writing. In addition, data-based reports in the area of sport, the weather or finances are already frequently created automat- ically today. Recently, for example, merely a few minutes after Apple had announced their latest quarterly figures, there was a report by the news agency Associated Press (AP): “Apple tops Street 1Q forecasts”. The financial report deals solely with the mere financial figures, without any human assistance whatsoever. Yet, AP was able to publish their report entirely via AI in line with the AP guidelines. For this purpose, AP launched their corresponding platform Wordsmith at the beginning of 2016, which automatically creates more than 3000 of such financial reports every quarter, and which are pub- lished fast and accurately. It is no longer that easy to distinguish between whether an algorithm or a human has written a text. Another exception of rece...

Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

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