Berlin’s AI competence center the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin (TUB) has now made the transition from project funding to permanent joint funding provided by the federal government and the State of Berlin. This sees the establishment of a national AI competence center in Berlin that will make an important contribution to the development and applications of artificial intelligence. Through a partnership with Charité – Universitätsmedizin Berlin, BIFOLD is set to become a cross-university central institute in the near future.
Jean-Pierre Seifert is professor of security in telecommunications at TU Berlin as well as a researcher at the Berlin Institute for the Foundations of Learning and Data. His research focuses on topics such as hardware security, cryptography technology, and quantum computers. He is also an established specialist for computer and communication security. Along with other experts he has been warning for some time now of the dangers of cyberattacks for private businesses and critical state infrastructures. Even before the start of the war in Ukraine his research demonstrated that almost all hardware solutions used in the commercial sector and even those intended to protect state high security areas do not function adequately.
The project Sphere: Knowledge System Evolution and the Shared Scientific Identity of Europe is one of the leading Digital Humanities projects, exploring a large corpus of more than 350 book editions about geocentric cosmology and astronomy from the early days of printing between the 15th and the 17th centuries (Sphaera Corpus) for about 76.000 pages of material. The relatively large size of this humanities dataset presents a challenge to traditional historical approaches, but provides a great opportunity to computationally explore such a large collection of books. In this regard, the Sphere project is an incubator of multiple Digital Humanities (DH) approaches aimed at answering various questions about the corpus, with the ultimate objective to understand the evolution and transmission of knowledge in the early modern period.
To reduce their carbon footprint, more and more computing systems are connected to microgrids to gain direct access to renewable energy sources. However, the local availability of solar and wind energy is highly variable and requires consumers to timely adapt their consumption to the current supply. Researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) have developed a new admission control approach that accepts flexible workloads such as machine learning training jobs only if they can be computed relying solely on renewable excess energy.
To tap the full potential of artificial intelligence, not only do we need to understand the decisions it makes, these insights must also be made applicable. This is the aim of the new book “xxAI – Beyond Explainable AI”, edited by Wojciech Samek, head of the Artificial Intelligence department at the Fraunhofer Heinrich Hertz Institute (HHI) and BIFOLD researcher and Klaus-Robert Mueller, professor of machine learning at the Technical University of Berlin (TUB) and co-director at BIFOLD.
A demo paper co-authored by Robin van de Water, Francesco Ventura, Zoi Kaoudi, Jorge-Arnulfo Quiane-Ruiz, and Volker Markl on “Farming Your ML-based Query Optimizer’s Food” presented at the virtual conference ICDE 2022 has won the best paper award.
NebulaStream, the novel, general-purpose, end-to-end data management system for the IoT and the Cloud, recently announced the release of NebulaStream 0.2.0., the closed-beta release. The System is developed and explored by a team of BIFOLD researchers led by Prof. Dr. Volker Markl. It addresses the unique challenges of the “Internet of Things” (IoT).
BIFOLD researcher Dr. Wojciech Samek has been appointed Professor of Machine Learning and Communications at TU Berlin with effect from 1 May 2022. Professor Samek heads the Department of Artificial Intelligence at the Fraunhofer Heinrich-Hertz-Institute. His goal is to further develop three areas: explainability and trustworthiness of artificial intelligence, the compression of neural networks, and so-called federated leaning. He aims to focus on the practical, methodological, and theoretical aspects of machine learning at the interface to other areas of application.