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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
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  Competence Center ML2R starts Blog on Machine Learning and Artificial Intelligence

The Competence Center Machine Learning Rhine-Ruhr (ML2R) has launched its new blog: https://machinelearning-blog.de. In the categories Application, Research and Foundations, researchers of the Competence Center and renowned guest authors provide exciting insights into scientific results, interdisciplinary projects and industry-related findings surrounding Machine Learning (ML) and Artificial Intelligence (AI). The Competence Center ML2R brings forward-looking technologies and research results to companies and society.

Seven articles already await readers: a four-part series on ML-Basics as well as one article each within the sections Application, Research and Foundations. The authors illustrate why AI must be explainable, how obscured satellite images can be recovered using Machine Learning and show methods for the automated assignment of keywords for short texts.

SFB 876 researcher Sebastian Buschjäger will be featured in the ML2R-Blog with a post on February 3. In his article, Buschjäger details an ML approach, which has been developed in the context of the Collaborative Research Center and allows for the real-time analysis of cosmic gamma rays. 

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New Techreport Online: On Probabilistic Rationalism - Wolfgang Rhode

How does one arrive at scientifically proven knowledge? This question has accompanied research from the very beginning. Depending on the scientific context, claims to the degree of truth, and on scientific methodology, different answers to this question have been given throughout the ages. More recently, a new scientific methodology has emerged that can best be characterized as "probabilistic rationalism." In collaboration between computer science and physics, methods have been developed in recent decades and years that allow large amounts of data collected in modern experiments to be analyzed in terms of their probabilistic properties. Artificial intelligence or machine learning are the methods of the moment.

This is the subject of a new Techreport entitled "On Probabilistic Rationalism" by Prof. Dr. Dr. Wolfgang Rhode. It does not deal with individual aspects of statistical analysis, but rather with the entire evolutionary process of knowledge expansion. Interdisciplinary aspects of epistemology from the perspectives of physics, computer science and philosophy are brought together to form an up-to-date and consistent model of knowledge acquisition. This model can be used to overcome some well-known problems of existing epistemological approaches. In particular, interesting parallels have been identified between the functioning of machine learning and biological-neural learning processes.

The report can be found here.


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