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Envision, Experiment, Engineer & Expand
The starting shot has long since been fired and it is high time to pick up speed so as not to miss the boat. But speed alone is not enough. In order to use artificial intelligence (AI) sensibly as a value-adding technology, a clearly structured, methodical approach is required so that
Modern customer experience
The technical possibilities of support are developing rapidly – as are customer expectations. To ensure consistent or even higher satisfaction, the customer experience (CX) must also continuously improve in parallel with technical developments.
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The Future of Cybersecurity
In today’s digital age, cybersecurity is a critical concern for businesses and individuals alike. As cyber threats grow increasingly sophisticated, traditional methods of threat detection and prevention are no longer sufficient.
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Integration of GenAI into the employee life cycle
Technologies such as GenAI are fundamentally changing the world of work and HR departments are faced with the question of how they can use GenAI optimally in their work in order to derive maximum benefit from the technology.
AI development
A major challenge in AI development is the effort required to obtain and label real-world data. A 2023 Gartner survey identified data availability as one of the top five barriers to implementing generative AI. Synthetic data can help address this issue.
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Commentary
Since the release of ChatGPT, cybersecurity experts have been wondering how to control the falsification of GenAI. They initially assumed a threat scenario, namely that the output of GenAI is already being distorted by data poisoning.
ChatGPT & Co
Everyone is talking about generative AI: More and more people are using it, more and more experts are raising a warning finger. Those responsible in industrial companies are also asking themselves where and how they can use ChatGPT-like technologies sensibly.
Computer vision and the data-centric AI pipeline
Data-Centric AI (DCAI) represents a pioneering approach in AI by focusing on the quality and relevance of data to optimize machine learning models and system performance.
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06.11.2025
 - 06.11.2025
Frankfurt
11.11.2025
 - 12.11.2025
Hamburg
18.11.2025
 - 20.11.2025
Stuttgart
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