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Artificial intelligence is an umbrella term and combines various services. Artificial intelligence (AI) essentially describes applications in which machines or algorithms perform processing tasks on data sets, for example. This enables human intelligence to take over other tasks. Through the progress in the development of artificial intelligence, further categories are emerging: strong AI on the one hand and weak AI on the other. The former is capable of independently recognising tasks, acquiring relevant knowledge and developing suitable solutions to problems. In contrast, weak AI works reactively and is often used for clearly defined tasks.

IoT or the Internet of Things makes it possible to connect physical devices with the possibilities of virtual worlds and artificial intelligence. This opens up completely new possibilities in the networking of cities, in healthcare, transport or production.

Especially when it comes to optimisation in industrial environments, the industrial IoT and the numerous possibilities of networking and integrating AI is proving to be a crucial technology.

Automation of processes or centralised monitoring processes are only the beginnings of new revenue or business models.

So-called SaaS IoT applications (Software-as-a-Service) are a rapidly growing area. A SaaS service combines data processing with the help of AI applications. The range of possible applications is broad.

Subfields of AI include machine learning and deep learning. Machine learning describes the generation of knowledge, insights and predictions from existing data sets. This requires the programming of an algorithm, which must be set up by humans in the initial phases. Over a longer period of time, the AI then forms a network of connections and processes the data sets independently.

Deep learning, in turn, forms a subgrouping of machine learning, in which complex information processing processes can be designed with the help of artificial neural networks. Deep learning processes are used where large amounts of data have to be analysed and sorted according to patterns and trends.

In terms of content, a neural network is based on the human brain. Through a KNN, it is possible to implement complex problems in a specific setting. Neural networks are increasingly used in the areas of autonomous driving, early warning systems, image recognition, fraud detection, medical analyses or weather forecasts. If a neural network is particularly complex and profound, it is referred to as deep learning.

Data mining: This term refers to the systematic application of AI applications to data sets to uncover hidden patterns, anomalies, trends or structures. This is done with the aim of deriving new insights, cross-connections or trends for optimisation measures.

Customer-specific marketing automations can thus be implemented in a much more promising way. Text mining is also possible, in which certain information can be extracted from texts, processed and used. Text mining thus opens the door to the semantic web.

Data lake: This is a mostly cloud-based storage of data, in which the data is obtained from a wide variety of sources. The data from the data lake is still unprocessed and thus in a raw format. In contrast, in the data warehouse the data is processed with the help of artificial intelligence and transferred into structures for analysis.