Machine learning (ML) and artificial intelligence (AI) have permeated the sciences and large parts of working life. Today many people use machine learning techniques without being a proven expert. Consequently, many questions and problems arise while using these techniques. BIFOLD accommodates distinguished machine learning experts from different areas and offers a weekly consultation on machine learning for students, but also for companies and institutions.
Event date: December 06, 2021
Norbert Ritter (University of Hamburg), Felix Gessert (Baqend), and Wolfram Wingerath (Baqend) will talk about their scalable and fast cloud data management research at University of Hamburg and Software-as-a-Service company Baqend.
Science & Startups is the association of the four startup services of Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin and Charité – Universitätsmedizin Berlin. Now they officially launched their new focus programme: K.I.E.Z. (Künstliche Intelligenz Entrepreneurship Zentrum). K.I.E.Z. will be carried out in close cooperation with the Berlin Institute for the Foundations of Learning and Data (BIFOLD).
BIFOLD Researchers, together with colleagues from Deutsche Commercial Internet Exchange (DE-CIX) and Brandenburg University of Technology, show that the exchange of information about ongoing cyberattacks has the potential to detect and mitigate substantially more attacks and protect critical parts of the Internet infrastructure.
To reduce the carbon footprint of cloud computing, researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) investigated the potential of shifting delay-tolerant compute workloads, such as batch processing and machine learning jobs, to times where energy can be expected to be green. Their publication “Let’s Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud,” was now accepted at Middleware’21.
Congratulations to BIFOLD Fellow Prof. Dr. Manfred Hauswirth and BIFOLD Junior Fellow Dr. Danh Le-Phuoc: At the International Semantic Web Conference 2021 (ISWC-2021), their paper “A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data” achieved 2nd place for the “SWSA Ten-Year Award” with an honourable mention.
On October 13, 2021, at an event celebrating the 200th birthday of the famous pathologist, physician and socialist politician Rudolf Virchow, the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) and the Charité – Universitätsmedizin Berlin, together with the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and several other Berlin research institutions declared the founding of the Berlin Cell Hospital. The new Cell Hospital wants to shape and develop the cell-based medicine of the future.
BIFOLD Fellow Prof. Dr. Konrad Rieck, head of the Institute of System Security at TU Braunschweig, and his colleagues provide the first comprehensive analysis of image-scaling attacks on machine learning, including a root-cause analysis and effective defenses. Konrad Rieck and his team could show that attacks on scaling algorithms like those used in pre-processing for machine learning (ML) can manipulate images unnoticeably, change their content after downscaling and create unexpected and arbitrary image outputs. The work was presented at the USENIX Security Symposium 2020.
Artificial intelligence (AI) has found its way into many work routines – be it the development of hiring procedures, the granting of loans, or even law enforcement. However, the machine learning (ML) systems behind these procedures repeatedly attract attention by distorting results or even discriminating against people on the basis of gender or race. “Accuracy is one essential factor of machine learning models, but fairness and robustness are at least as important,” knows Felix Neutatz, a BIFOLD doctoral student in the group of Prof. Dr. Ziawasch Abedjan, BIFOLD researcher and former professor at TU Berlin who recently moved to Leibniz Universität Hannover. Together with Ricardo Salazar Diaz they published “Automated Feature Engineering for Algorithmic Fairness“, a paper on fairness of machine learning models in Proceedings of the VLDB Endowment.