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Artificial Intelligence writes about Artificial Intelligence

I can’t resist showing an example of OpenAI’s ChatGPT current capabilities. The readers can decide for themselves how good or insufficient the options are at the moment. The program generated the following 408 italic words in English.

Artificial intelligence has come a long way in recent years, and one of the most exciting developments is the ability for machines to generate written content. This has the potential to revolutionize the way we create and consume information online, but it also raises questions about the role of human creativity and the quality of AI-generated content.

AI-generated blog posts are created by algorithms that use natural language processing to analyze and generate text based on a set of parameters or data inputs. These inputs can include anything from historical data on blog post performance to information about the intended audience and topic.

One of the most significant advantages of using AI to generate blog content is its ability to produce content quickly and efficiently. This can be especially useful for businesses or individuals who need to create a high volume of content on a regular basis. With AI-generated content, it’s possible to create blog posts in a matter of minutes, rather than hours or days.

Another benefit of using AI for blog content creation is the potential for increased accuracy and relevance. AI algorithms can analyze vast amounts of data to determine the most relevant and useful information for a particular audience or topic. This can help to ensure that blog posts are more informative and engaging for readers.

Nevertheless, there are also some drawbacks that might happen when using AI-generated content. One of the most significant concerns is the quality of the content produced. While AI algorithms can be highly accurate and efficient, they may lack the creativity and nuance that human writers bring to their work. This could result in content that is formulaic, repetitive, or unengaging.

Another concern is the potential for bias or inaccuracies in AI-generated content. Algorithms are only as good as the data they are trained on, and if that data is biased or incomplete, it could result in content that is also biased or inaccurate.

Despite these potential drawbacks, there is no denying the potential of AI-generated blog posts to revolutionize the way we create and consume online content. As technology continues to advance, we may see more and more businesses and individuals turning to AI for their content creation needs.

Ultimately, the key to successful AI-generated content is to find the right balance between efficiency, accuracy, and creativity. By leveraging the strengths of both humans and machines, we can create content that is informative, engaging, and relevant to our audiences.

Bottom line: Write your own text about artificial intelligence that writes about artificial intelligence, in order to compare your text with the generated one. For many, the generated text will be satisfying to describe the topic – it goes very fast, the content is OK, and the wording is acceptable. Be aware! The text above has not yet been revised. In any case, the generated text can serve as a quick start to a blog post. With the appropriate editing, such a text gets additional qualities and, above all, the missing personal touch. It is a good alternative for anyone, who lacks creative writers.

P.S.: Despite all the current hype, everyone should be aware that it’s not about stringing letters together, but about the dissemination of your messages. They have to match your intentions and to be read by the receiver. The new possibilities increase the already existing information overload. And if nobody reads the content, the best generation is senseless.

The skillful grading determines the insights

The evaluation of tangible and intangible aspects concerning things, feelings, expectations, events and consequences, is determined by the choice of the scale. What reveals an evaluation, if you can only assign an upward thumb? This question has to be asked by everybody, who wants to make a survey, because the skillful grading determines the insights.


As soon as you prepare an inquiry, you have to worry about the scales. In the following examples you find basic variants.

  • The „Like “: good
    The “Likes” of Facebook provide users the possibility to positively mark a post, in the sense of „I like it “. They do not offer further alternatives, since there is actually only this value. Its meaning is not very clear, since on the one hand the “Like” is not always clear. If someone denounces a drawback and many people “Like” the contribution, do they mean the comment or the actual article? The reasons for only a few or no „Likes “are likewise diverse. Perhaps only a few people saw the post or their threshold for liking is not yet reached. For meaningful evaluations „Likes” are not suitable.
  • The binary inquiry: bad/ good
    The actual evaluation with the thumb needs additionally at least the thumb that shows downward. Thus the two poles at the end of a scale are defined. This format gets the question to the heart of the point. Unfortunately the reality mostly exists between the extreme poles (see The polarizing character of this question can be defused by speaking of rather good and rather bad. This format serves well for fast inquiries of the mental state by a show of hands.
  • The simple inquiry: bad/ neutral/ good
    The gray zone can be established most easily with an additional value between the poles – for example by using neutral. De Bono calls it PO. Thus gives people the possibility to select neither good nor bad. The disadvantage of this middle value is the fact that it offers a back door to the respondents, in order not being obligated to decide. This form of questioning is unfortunately not quite meaningful.
  • The pragmatic inquiry: bad/ rather bad/ rather good/ good
    The simple inquiry offers an alternative that covers the entire gray zone. In this case the neutral zone is as large as the two poles. Above all, the scorers are forced to decide for one or the other side. This format overcomes the disadvantages of the binary and the simple inquiry. It supplies fundamental valuations – good or bad.
  • Realistic inquiry: very bad/ bad/ rather bad/ rather good/ good/ very good
    In order to eventually also determine gradual differences, both sides should be further graded. Thus, the respondents have the possibility to select one side and to determine the degree at the same time. Very should be used in this context only for unusual good or bad. This form of inquiry will deliver in most cases suitable results.
  • The filigree inquiry: 1=very very very bad/ 100=very very very good
    In the planning the questioners are quickly carried away to develop detailed grading’s, in order to allow as much as possible differentiation for the responds – in extreme cases from 1 to 100. This classification offers sufficient room for the description the gray zone (from 2 to 99). However it creates two disadvantages. First of all, it will be more difficult for the respondents to deliver their evaluations, since they have to arrange the gray zone for themselves. Secondly, the evaluators will have difficulties to draw insights from the filigree grading. Therefore you should only provide so many ranges that you need for a meaningful evaluation. More produces noise that softens the meaning of the results.

Bottom line: The choice of the value ranges determines the quality and the significance of an evaluation. What counts is: less is more – however do not ask less than you need. Eventually the skillful grading determines the conclusions that one can draw from the survey.

P.S.: Do not use only Multiple Choice. Always add a field for comments, in order to better understand the comments.