adesso Blog

GenAI opens up great opportunities for insurance companies to generate value. Likewise, it is also becoming a key driver of their digital transformation. The rapidly evolving technology can be used to automate and speed up insurance processes and significantly improve the customer experience. From a financial perspective, insurers benefit from GenAI’s ability to more accurately assess risks.

The world of artificial intelligence is developing at a breathtaking speed, and large language models (LLMs) are at the forefront of this revolution. LLM operationalisation is an essential part of this development and offers companies the opportunity not only to push the boundaries of technology, but also to set new standards for human–machine interaction. We explain why this is the case in our blog post.

The Artificial Intelligence Act (AI Act) was finalised at the end of 2023 after intensive negotiations between the European Parliament and the European Council. The debates were characterised by talks on the definition of AI and the categorisation of systems into risk classes. Numerous details were clarified, in particular regarding unacceptable risk (highest risk level) AI systems and high-risk AI systems. National interests and the development of certain technologies played an important role, such as the ban on real-time biometric identification in public spaces and the use of foundation models or generative AI. I will show you in this blog post what the EU AI Act looks like in detail.

The ‘Intelligent knowledge database – support during Aleph Alpha integration’ project focuses on the use of Aleph Alpha’s large language models to make the day-to-day work involved in maintaining text documents and editorial processes more intelligent and efficient. adesso implements the work processes for data analysis and clustering in the database as a pro bono service. In my blog post, I will provide a detailed look inside the workshop that accompanies the AI project as well as key aspects of it.

In this blog post, I will take a closer look at traceability tools that show great promise with you and go beyond theory by showing practical applications in AI projects. I will also be presenting a demo to show how these tools not only support the technical side of research, but also how they can be used in practice.

With all the hype currently surrounding generative AI (GenAI), people are divided into two main camps, namely those who support it and those who remain sceptical. This blog post provides a short overview on how GenAI can drive innovation in the insurance sector and why effective change management is important for both its sceptics and proponents.

In the world of artificial intelligence (AI), it has often been assumed that larger models are better. However, recent research shows that smaller language models, which were previously considered to only be an intermediate step on the path towards larger models, outperform or at least match the performance of large language models (LLMs) in various applications. In my blog post, I explore this point and present a variety of small language models. I will also take a look at the pros and cons of SLMs in a direct comparison with LLMs.

In this blog post, I would like to take you on an exciting journey through the world of traceability in AI projects and give you a look inside the challenges surrounding the black box dilemma. I will also explore the key role that traceability plays in the transparent and ethically responsible development of AI systems.

This is the third and final instalment of my blog series titled ‘Machine-generated text summarisation in Aleph Alpha Luminous using R’. Here, I will use a high-level example to explain the various steps in a transformation pipeline and present the intermediate results.