adesso Blog

In the first two parts of this blog post, we looked at the concept of creativity and the role of humans and their creative potential in the creative process. In this part, we take a broader perspective and look at how some approaches from the field of GenAI work in connection with human cognitive processes.

The EU AI Act has come into force and brings with it far-reaching changes for the development and use of AI in Europe. Companies, public authorities and providers of AI systems must now prepare for the upcoming regulations and deadlines. Those who act now can avoid unnecessary risks and benefit from timely adaptation. In my blog post, you can find out which steps are necessary and which deadlines you need to keep in mind.

In the first part of the blog post, we showed what creativity is, which stages it goes through and how it has developed. In this part, we will take a look at the role of people and their creative potential in the creative process.

What is creativity and is it an exclusively human ability? In view of the advances in generative AI, the question arises again: can machines be creative? Before answering this question, let's delve into the different facets of creativity. In the first part of our blog post, we show what creativity is, which stages it goes through and how it has developed throughout history.

AI has been the megatrend of recent years. No company, no trade fair, no provider can do without this magic word. And yet, when it comes to insurance companies, I see a clear reluctance to use these methods in practice. In my blog post, I show how TrustworthyAI makes the use of artificial intelligence in the insurance industry safe and efficient.

Companies and authorities are often faced with the challenge of finding relevant information in huge amounts of data. Although Retrieval Augmented Generation (RAG) is still a relatively new technology for targeted retrieval of local domain knowledge, the technology often fails to aggregate complex distributed information. This is where GraphRAG comes into play. We present it in detail in this blog post.

A lot has happened since the breakthrough for Large Language Models (LLMs) with ChatGPT. What has remained is our desire to supplement these language models with further knowledge. There is no longer a one-size-fits-all solution, but there are numerous possibilities. This blog post provides an overview of the various options for optimising LLMs.

GenAI has revolutionised the way we experience technology. These advanced models are able to produce text, images and even videos by performing creative tasks autonomously. Their multimodal capabilities are particularly fascinating: They process different input data, be it texts, images or videos, and convert them into impressive results. From medical diagnostics to innovative applications in image processing - the potential uses of GenAI are virtually unlimited. In this blog post, I show how this technology will shape our future.

In the world of data processing, there are various approaches to improving efficiency and accuracy. One particularly promising approach is the use of Large Language Models (LLMs) to improve the linking of entities through entity linking. In this blog post, I will highlight the new possibilities and advantages of this technology.

Artificial intelligence is developing rapidly and Retrieval Augmented Generation (RAG) in particular has attracted a lot of attention recently. Large language models such as ChatGPT show their full potential when they are enriched with domain-specific knowledge through RAG. Despite this potential, users often face challenges. In this blog post, we look at the transition from basic to advanced RAG approaches and show how typical problems can be overcome.