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The industrial sector, like other areas, is currently going through a phase of digital transformation. This means that manufacturing companies are involved in various digitalization activities [1]. Within this context, industrial data and the way it is processed, visualized and used play an essential role.
In the future, when we create and use AI systems, we need to be aware not only of the opportunities, but also of the risks and challenges of using them. Guidelines can help us create AI systems that are not only efficient, but also ethical, safe, and trustworthy.
How can you avoid a system or legacy system becoming a legacy system? Read the following article to learn how to identify legacy systems in time, remove them and ultimately avoid them.
Regardless of whether fault messages in manufacturing processes are to be analyzed, doctor’s letters are to be filed in a structured manner, or products are to be suggested automatically, Natural Language Understanding (NLU) offers a broad spectrum of industry-specific and cross-industry applications for gaining knowledge from unstructured text data.
Chat with an Artificial Intelligence – what the revolutionary language model ChatGPT is all about and what opportunities and risks its use entails.
One of the central goals of data engineering is the preparation of data sets according to the requirements of the users or the subsequent process steps. The use of data can range from model training in the field of machine learning to improved internal company reporting based on an integrated database.
