  Hardly any other area of research has recently attracted as much attention as
machine learning (ML) through the rapid advances in artificial intelligence
(AI). This publication provides a short introduction to practical concepts and
methods of machine learning, problems and emerging research questions, as well
as an overview of the participants, an overview of the application areas and
the socio-economic framework conditions of the research.
  In expert circles, ML is used as a key technology for modern artificial
intelligence techniques, which is why AI and ML are often used interchangeably,
especially in an economic context. Machine learning and, in particular, deep
learning (DL) opens up entirely new possibilities in automatic language
processing, image analysis, medical diagnostics, process management and
customer management. One of the important aspects in this article is
chipization. Due to the rapid development of digitalization, the number of
applications will continue to grow as digital technologies advance. In the
future, machines will more and more provide results that are important for
decision making. To this end, it is important to ensure the safety, reliability
and sufficient traceability of automated decision-making processes from the
technological side. At the same time, it is necessary to ensure that ML
applications are compatible with legal issues such as responsibility and
liability for algorithmic decisions, as well as technically feasible. Its
formulation and regulatory implementation is an important and complex issue
that requires an interdisciplinary approach. Last but not least, public
acceptance is critical to the continued diffusion of machine learning processes
in applications. This requires widespread public discussion and the involvement
of various social groups.
