Artificial Intelligence (AI): Field-proven applications for companies

Whether foundational training courses that give you a sound basis for understanding AI concepts, or intensive training courses that allow you to address the needs of your company in a deeper and more targeted manner: with our know-how and that of our cooperation partners, you will attain the ideal mindset to implement AI with structure and focus in successful use cases.

To support high processing speed and data reduction, we employ a near-sensor approach in data processing. This involves analyzing, evaluating and classifying readings in the sensor, so that subsequently only the data required for the evaluation and training of AI systems has to be forwarded.

When teaching an AI system, we always include expert knowledge about the respective use case, so that systems are trained more quickly and above all more reliably. The objective of our work in all cases is for the systems to fulfill high requirements with regard to robustness and reliability, which we are able to guarantee by virtue of our longstanding expertise in domains such as system verification and functional safety.

In practice, there is often a lot of data available, but it is not always the right data for solving a problem. That being so, we use model-based approaches such as digital twins or simulations. This enables us to model all the necessary manifestations of a problem without the need for the laborious teaching of AI systems in real environments. With this approach, we also take AI out of the black box and make it possible to retrace the paths along which our systems arrive at solutions with the help of methods such as machine learning and big data analyses.

When it comes to using AI without external clouds, special AI hardware that performs the data processing internally can be the method of choice. We are able to implement hardware solutions with great efficiency through our modular hardware configuration (all the way down to chiplets), state-of-the-art packaging methods, rapid generation of building blocks and, upon request, the integration of our AI algorithms. Altogether, this creates particularly resource-conserving and data-reduced solutions that are not only small, but also involve minimal energy consumption.

If a company wants to avoid data storage in external clouds, we can help it get set up with local computing infrastructure using private cloud or edge solutions. In addition, 5G technologies can be an effective approach for applications that use smart connectivity or require the rapid transport of large data volumes. 5G’s ultra-short latency times combined with extremely low energy consumption allow applications such as real-time sensor systems or new forms of process control and configuration.