Machine learning has its origins in cybernetics and computer
science, is a sub-area of artificial intelligence (AI) and one of the most
important technology building blocks in data science. Data science deals with
techniques and theories of how hidden structures and implicit knowledge can be
extracted from large amounts of data.
When it comes to machine learning, the Federal Ministry of
Education and Research (BMBF) has focused on the constant promotion of software
innovations. In many projects modern procedures were used and existing
procedures improved. Funding from the BMBF over the past 20 years has proven to
be a significant contribution to the fact that Germany is one of the leading
scientific nations in the field of machine learning today. The scientific and
technical progress in hardware and software has created steadily improved
foundations for new and further developed methods of machine learning in recent
years. New areas of application have developed on this basis. bacobolts
New applications such as complex voice control, automated
translations or autonomous driving show the technological and economic
potential of modern machine learning processes. On the way to further, even
more complex and highly relevant applications in international competition,
however, the limits in the current state of science and thus open research
fields of machine learning become apparent.
In order to shape top-level research in the field of machine
learning in Germany for the future and to set new priorities, the BMBF,
together with experts from science and business, identified relevant research
fields in which concrete challenges for research are revealed. So there is z.
For example, the challenge with autonomous systems is that the machine
learning, which is important for environment recognition, has to be monitored
by deterministic control and regulation systems that are understandable and
explainable for liability cases. Further challenges are the lack of
qualitatively acceptable data for machine learning in many practical areas as
well as the rapid change in everyday environments, which prevents the
collection of the necessary minimum amount of sample data.The evaluation of the
data quality when compiling training data is another field of research. Even
this small excerpt shows the need for research work on machine learning for
everyday use in new fields of application.
The solution to the problem areas mentioned is of great
strategic importance for the German economy, especially in the automotive,
logistics, finance and medicine sectors. The BMBF will specifically support
researchers and developers in subject areas such as deterministic behavior,
transferability of knowledge, explainability of the solution, estimation of the
error probability of a prediction, robustness of ML processes and increasing
the efficiency of machine learning through various funding measures.Small and
medium-sized enterprises (SMEs) in particular will benefit from the funding
measures of the BMBF, since the training and further education of skilled
workers as well as application-oriented, pre-competitive development are to be
promoted.
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