Artificial intelligence (AI) is being applied in a wide variety of areas, for example in business, industry or healthcare. It can be used to solve complex tasks that were previously unsolved. For example, AI-based systems are able to recognize patterns and correlations or make predictions for the future. AI-based systems have great potential to relieve or supplement human resources. In particular, the combination of humans and machines can increase efficiency and effectiveness.
Machine Learning and Deep Learning
Machine learning (ML) is a subfield of AI that enables machines to learn from data. ML models are able, for example, to recognize patterns and correlations or to make predictions for the future based on data.
Deep learning (DL), in turn, is a subfield of machine learning in which deep neural networks are used to learn complex relationships. Deep neural networks are characterized by a high number of learnable parameters. These parameters can solve difficult tasks, but require a large amount of training data and special algorithms.
How can you benefit from AI?
You also benefit from artificial intelligence: AI systems enable the automation of complex (decision-making) processes, in some cases with an accuracy superior to human expertise. By drawing on the latest findings from AI research, RISC Software GmbH can achieve robust solutions for your individual use cases in the shortest possible time.
The foundation for successful AI models is appropriate data collection. As an AI enabler, RISC Software GmbH’s strength is to prepare this data appropriately and to make AI systems usable for the end user in daily applications. In doing so, RISC Software GmbH draws on its many years of experience in a wide variety of application domains (logistics, industry, medicine). As a research institution, it has a very good overview of the state of the art and is able to transfer the latest methods and findings into applicable systems.
RISC Software GmbH supports you with expertise and experience in the implementation of your individual AI use cases. Great emphasis is placed on quality and sustainability. The development and implementation of the AI systems are based on ethics, transparency and fairness, whereby the systems not only work efficiently and effectively, but are also responsible and reliable.