Force (Make). AI. Words. - How can artificial intelligence shape and influence words?

For about three years now, artificial intelligence (AI) has been demonstrating its potential to profoundly change the way we communicate. First and foremost, our linguistic interaction is changing because we are entering into a more literal dialogue with ‘machines’ than ever before (Generative AI) in order to quickly achieve the desired results, especially in the creation of content (text, image, audio, video).

With its far-reaching language models, the so-called Large Language Models, AI also suggests new or as yet unknown words and arguments for text production, which can change our communication with our fellow human beings - our previously familiar language patterns and linguistic means are influenced and in some cases newly characterised by AI.

In addition, more and more AI systems (AI Agent) are being used to take over almost all communication between companies, institutions etc. and their customers or target groups, especially in the service or support sector. And AI (Analytical AI) is also increasingly being used in sales and marketing when it comes to developing and providing a personalised customer approach.

In his lecture at the LebensPhasenHaus on 4 April 2025 in Tübingen, Pietro Triscari will explain in more detail how drastic these changes in communication can be and how words can be reshaped and language influenced.

1. Education: In educational contexts, AI can make suggestions and concepts to make complex topics easier to understand or to facilitate access depending on the level of knowledge.

2. Adaptation: AI can help to adapt the language of texts or messages to the expectations or understanding of certain target groups. On the one hand by using technical terms for specialists or on the other hand by using colloquial or easily understandable expressions for the general public.

3. Text generation: AI can generate texts that are very similar to human expressions, suggesting words or terms that make sense in certain contexts.

4. Translation: AI can help translate texts into different languages, suggesting alternatives or synonyms that are likely to be more understandable in the target language.

5. Content production: AI is increasingly being used to create content such as articles, blog posts or social media contributions. It can develop new messages, claims or phrases that can then be disseminated to the general public.

6. Trend analysis: AI can help to identify and use linguistic trends or current memes by analysing which terms are frequently used on the web and especially on social media.

7. Marketing: AI is used to create or optimise marketing messages and advertising texts. This can result in new, memorable slogans or terms that influence brand awareness.

8. Personalisation: AI systems that provide personalised recommendations can influence the way people consume information and how they act on it (e.g. purchase intentions, recommendation of a product, brand, etc.)

9. Support: AI agents can recognise the content of recurring and often similar queries and respond very quickly or in 24/7 mode by responding with standardised assistance and information, some of which they generate themselves. Only in the case of very difficult, complex enquiries are these escalated by the AI agents and forwarded to the relevant support staff.

10. Sales: AI systems are increasingly providing more and more comprehensive support for sales automation by independently compiling customised offers and generating so-called follow-ups, which they send or present via email, messenger services or chat bots. This allows companies to respond to individual customers and their interests in a targeted and very specific way.

Generative AI
Generative AI is a collective term for AI-based systems that can produce professional-looking and creative results based on unstructured data, e.g. existing content from the internet. Generative AI uses deep learning models known as foundation models (FMs) for this purpose. These models are trained in advance and the algorithms they support can be used for various downstream tasks, including the generation of - new - content such as text, images, videos, audio, programme code or similar.

Large Language Model
Large Language Models (LLMs) are a type of AI algorithm that uses deep learning models and large data sets to understand, summarise, generate and predict new content. LLMs are characterised by their ability to understand context, generate coherent text and perform language-related tasks. They have been developed specifically for the generation of text-based content and now achieve a high level of accuracy without the need for extensive further post-processing.

AI Agent
AI agents are software programmes that can act (almost completely) autonomously to perform regularly recurring tasks and achieve specific business goals by making decisions based on the data and information provided to them and then taking or initiating action independently. Advanced AI agents can also solve more complex tasks and problems independently and interact both with their (human) environment and with other (artificial) AI agents.

Analytical AI
In contrast to Generative AI, Analytical AI uses structured data to recognise patterns and trends in real time and make predictions. One example of this is predictive AI, which is used in predictive marketing, the main aim of which is to improve customer loyalty, for example by providing personalised marketing messages that are tailored to each individual customer.

Where: LebensPhasenHaus | Auf der Morgenstelle 15, 72076 Tübingen

Event series: Treffpunkt: LebensPhasenHaus - Wie wollen wir in Zukunft leben?

When: Friday, 4 April 2025, 17:00 to 18:30

Topic: Macht. KI. Worte. Wie kann Künstliche Intelligenz Worte prägen und beeinflussen?

Speaker: Pietro Triscari, CEO der d-serv GmbH