When creating a text, editing options such as ‘copy & paste’, formatting and spell checking are now taken for granted. AI systems such as ChatGPT, GPT-3, GPT-4, GPT-Turbo from OpenAI or Claude from Anthropic promise significantly more extensive support in text creation. Great progress has also been made in the transcription of videos and audio recordings such as podcasts, as well as in the tagging or generation of images (DALL-E, Midjourney, Stable Diffusion, etc.).

What the new generative AI processes based on so-called LLMs (Large Language Models) have in common is an inherent unpredictability of results. This is particularly problematic because it is also impossible to predict whether the result is correct.

GPT stands for ‘Generative Pretrained Transformer’. A GPT such as ChatGPT is based on a relatively simple principle: it can generate the most probable ‘next word’ for a given text based on countless learned texts and sentence patterns. It then uses the given text and the first word generated to generate the next word. And so on.

In this way, a GPT is able to generate a text for a given question that is highly likely to be a suitable answer. Or, when the sentence ‘Shorten this text’ is added to a text, it can generate a shortened version of that text.

A GPT - like everything in the world of computers - is actually deterministic. Therefore, the answer to a given question would always be the same; even verbatim. So why is ChatGPT not deterministic? As described, a GPT always generates the "next word" with the highest probability. Since there are many words that are very close to this highest probability, this is used to introduce "creativity" into the behavior of GPT. To do this, one of the words with the highest probability is chosen at random. Initially, this makes little difference, but since this little touch of randomness is also added with each additional word generated, a sentence with only 10 words and the assumption that there are ten options to choose from for each word already results in "¹⁰¹⁰", i.e. a billion, variants. In this way, a GPT can generate countless texts on one topic.

If you let a GPT work on the basis of a given text and few ‘instructions,’ the results are usually good. The smaller the basis in terms of the number of documents learned for the text to be generated, the less reliable the result. What is particularly problematic here is that a GPT can only cite a source for a generated text if it was generated based on one or more specific documents. Why is that?

A GPT has learned all its information and its ability to generate text based on an incredibly large amount of text. Generated text is therefore never based on a single source. Only if you instruct a GPT to use one or more texts as the basis for a text to be generated, a search or similar, can a direct reference be made.

Otherwise, the results are based on all the texts on which the GPT was trained.

This can be compared to humans. We would not be able to cite the sources for all our statements either. This is not only because our memory is limited, but also because we base our statements on many sources that we have become aware of throughout our lives. In the process, identical information reinforces itself and contradictions weaken each other.

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InterRed ContentAgents are based on state-of-the-art AI technology. A unique selling point is the reliability of the processes used. The AI of ContentAgents always observes the set limits and rules. GPT technology is only used in the form of self-correcting GPT. And generated texts always contain references to the underlying sources.

A self-correcting GPT independently monitors compliance with specified rules – both in terms of content and form. For example, if it is asked to name three key characteristics of self-correcting language models, each in a single keyword, it adheres precisely to this specification: It names exactly three terms, each limited to one word, without additional explanations or digressions. In addition, it indicates the underlying source for each individual keyword – such as a scientific paper, a technical document or a documented recommendation for use – in order to make the origin and traceability of the terms transparent.

We call our AI, which adheres to rules and boundaries and documents the underlying sources of generated content, ‘SmartAI’.

With ContentAgents, AI can be used for any form of computer-aided writing without uncertainty. Thanks to SmartAI, there is no need for the sometimes time-consuming checking of AI results, which otherwise often eats up the time saved.

The ContentAgents automatically analyze all content from the InterRed ContentHub as a basis for your work. Any other sources can be added. The analysis is carried out completely autonomously based on the predefined sources.

The InterRed ContentAgents recognize the topics based on the connections in the individual texts. They learn independently, for example, to distinguish topics about a (parking) bank from topics about financial institutions (banks) and to link them with other suitable content. For example, they would assign articles about the money economy to the topic "bank" (as the financial institution), but never to the topic "bank" (as a park bank).

This is achieved through so-called ‘embeddings’. These allow words and documents to be transformed into mathematical objects (vectors). This can then be used, for example, to measure semantic distance or to determine the word that is semantically closest to another.

The InterRed ContentAgents also recognize people and places and provide further information about them. They can also contextually classify and associate synonyms, i.e. associate words such as "sun" or "warmth" with "summer".

Because they learn independently, constantly readjust themselves and automatically pick up and incorporate new topics, they are clearly superior to manual, hierarchical classification systems such as tagging, ontologies, categorizations or keyword directories. In day-to-day practice, it quickly becomes clear that AI delivers significantly better results than manually maintained and updated classification systems, which can involve a great deal of effort.

The InterRed ContentAgents make AI usable in all areas of everyday editorial work.

All processes for creating a print product can be automated with SmartPaper technology. From the selection of suitable content, the choice of suitable images and image sections to the automated layout and shortening of text. A complete edition can be created automatically in just a few minutes. And thanks to the SmartAI, which adheres strictly to specifications, the face of the product is retained and the result does not have to be laboriously checked. Among other things, InterRed SmartPaper uses the artificial intelligence of InterRed ContentAgents and thus the InterRed SmartAI technologies for automation.

Whether in everyday editorial work or at the touchpoint for the target group. The ContentAgents offer a powerful recommender system that can recommend related texts and images. Suggestions for texts to be linked or suitable images can be integrated into a text simply by dragging and dropping. And at the touch of a button, the AI can generate SEO-optimized links based on up-to-date SEO data at any time. The optimal teasers can be displayed on mobile and web. You set the target. Whether reach, reading time, paywall conversion or product purchase, the ContentAgents support the implementation of the respective strategy.

Instead of laboriously selecting texts and constantly adapting them to new circumstances over time, SmartCollections make it very easy to define a group of texts with the help of AI. These can then be used as required. For example, for teasers, a newsletter or part of a print product. As soon as new texts are created that fall within the definition of the SmartCollection, the usage - no matter where - is automatically updated. This feature also makes SmartCollections suitable as a "content radar". You can have the AI automatically inform you about new texts. By e-mail or message in Microsoft Teams, for example.

The integration of ContentAgents into the InterRed FlowEditor makes AI easy to use. Headings, subheadings, bulleted lists, pre-texts, teaser texts, summaries and social posts (Twitter tweets etc.) can be created at the touch of a button while writing in the editor. And, thanks to SmartAI, always reliably according to your own specifications.