Anomaly detection in industrial image data
AI enables new forms of automated quality assurance – with enormous potential, but also practical hurdles in terms of data availability, model architecture and system integration.
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AI enables new forms of automated quality assurance – with enormous potential, but also practical hurdles in terms of data availability, model architecture and system integration.
Quantum Machine Learning (QML) combines quantum computing with AI and promises revolutionary advances in data-intensive areas such as medicine, industry and finance. Despite technical hurdles, QML is considered a key technology of the future.
The rapid development of artificial intelligence (AI) is revolutionizing software development. Companies are integrating AI to improve products, speed up development processes and offer unique solutions. However, data protection, security and intellectual property remain key challenges.
Workflows can be improved through the (partial) automation of tasks such as the creation of documents. Customer service can also be optimized through AI-supported personalization. The provision of data insights by AI systems also helps to optimize processes. Large language models (LLMs) such as ChatGPT offer a wide range of promising applications for this.
As part of the ARES research project, software was developed to support doctors in analyzing cerebral aneurysms. Innovative technologies are used to precisely characterize geometry and biomechanics in order to facilitate treatment decisions.
How machine learning and structure-borne sound data help to monitor the wear of flame cutting nozzles and increase the efficiency of flame cutting.