Podcasts about tu berlin

Am 26. Mai 2025 öffnet die Ausstellung "#FakeImages – Gefahren von Stereotypen erkennen" an der TU Berlin". Ausgestellt werden visuelle Antisemitika aus der Sammlung von Arthur Langerman. Mehr lassen wir uns von Prof. Dr. Uffa Jensen erklären. Er ist unter anderem wissenschaftlicher Leiter des "Arthur Langerman Archivs für die Erforschung des visuellen Antisemitismus" an der TU Berlin. Außerdem gehen wir bei den Profis der Frage nach, wie sich das menschliche Gehirn entwickelt hat. Der 22. Mai ist seit 2001 der Internationale Tag der biologischen Vielfalt. Er soll an den 22. Mai 1992 erinnern, den Tag, an dem der Text des Übereinkommens über die biologische Vielfalt offiziell angenommen wurde. Eine neue Studie zeigt jetzt: Wir sollten dabei auch auf die genetische Vielfalt achten. Warum weiß Prof. Dr. Thomas Hickler. Im Gespräch mit Prof. Dr. med. Thomas Eigentler, Leiter des Hauttumorcentrums Charité, klären wir, wie es zum starken Anstieg an Hautkrebs-Fällen kommt und welche Präventionsmaßnahmen am sinnvollsten Ein Forscherteam hat einen physischen Nachweis für Kämpfe zwischen Gladiatoren und Löwen geliefert. Über den Fund und dessen Bedeutung sprechen wir mit Malin Holst.

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

Katharina Lottner ist gerne dort, wo viele Menschen – aber auch wo keine Menschen sind. Als Creative Director fällt es ihr schwer, nicht inspiriert zu sein. Katharinas Arbeiten wurden unter anderem mit dem Red Dot Award und dem Designpreis der Bundesrepublik Deutschland ausgezeichnet. Mit ihr spreche ich über den Reiz des Unperfekten, warum man sich mit anderen Menschen auf die Yoga-Matte setzen sollte und wie wir das gemeinsame Erleben gestalten können. Wie können wir psychologische Aspekte besser in Konzepte integrieren, um tiefe Verbindungen zum Publikum herzustellen? Welche Schritte müssen wir als Gestaltende unternehmen, damit die Inszenierung bereits vor dem Event beginnt? Wie können wir lernen, Unperfektes zuzulassen und dadurch authentische Erlebnisse schaffen? Herzlichen Dank für diese Begegnung, Katharina Lottner! 01:30 Der kleinste gemeinsame Nenner 05:54 Spacial Storytelling: innerer und äußerer Raum 18:59 Inszenierung beginnt vor der Bühnenkante 22:44 Erwartung, Intention, Tonalität 29:05 Der Reiz des Unperfekten 37:23 Publikums-Interaktion: Nahbarkeit 40:49 A crack in everything & What would Rosa do 47:39 Tiefe Verbindung generieren Inszenierungen von Katharina Lottner: Instagram: www.instagram.com/katharina.lottner/?hl=de LinkedIn: www.linkedin.com/in/katharina-lottner/ Weitere Folge zur Szenografie: www.ablaufregisseur.de/whats-next-szenografie/ Über Katharina Lottner – geboren 1970 in Nürnberg, lebt und arbeitet in Berlin. Nach ihrem Architekturstudium an der FH Frankfurt/Main und dem Studium für Bühnen- und Kostümbild an der TU Berlin widmet sie sich seit 1999 hauptsächlich temporären Bauten wie Ausstellungspavillons und Bühnenbildern. Mit ihren Thesen stellt sie traditionelle Ansätze der Inszenierung infrage und betont die Bedeutung von Psychologie, den Beginn der Inszenierung weit vor der Bühne und den Charme des Unperfekten. Begegnet mir! LinkedIn: https://bit.ly/3olKIHK Newsletter: https://bit.ly/ablaufregisseur Mein Buch: https://bit.ly/Inszenieren Chris Cuhls ist als Regisseur, Konzepter und Berater mit diesem Podcast auf der Suche nach den Prinzipien der Wirkung – für Momente, die haften bleiben und Erlebnisse, die Wandel bewirken. Viel Spaß beim Hören des Podcasts und bei deiner nächsten Begegnung!

In dieser Episode tauchen wir tief ab – wortwörtlich! Gemeinsam mit Tim Nitzsche von der TU Berlin klären wir die Frage: Was ist eigentlich ein Düker? Spoiler: Es hat nichts mit Tauchen zu tun, obwohl man bei der Inspektion manchmal ganz schön nass wird. Von der Definition über Bauformen bis hin zu Problemen im Betrieb – diese Folge ist ein Muss für alle, die sich für urbane Infrastruktur und spannende Abwassertechnik begeistern.

In this eye-opening episode of the KuppingerCole Analyst Chat, Dr. Kashyap Thimmaraju, postdoc researcher at TU Berlin and founder of FlowGuard Institute, joins Matthias Reinwarth to discuss his groundbreaking research into burnout, well-being, and flow state in Security Operations Centers (SOCs).

In this eye-opening episode of the KuppingerCole Analyst Chat, Dr. Kashyap Thimmaraju, postdoc researcher at TU Berlin and founder of FlowGuard Institute, joins Matthias Reinwarth to discuss his groundbreaking research into burnout, well-being, and flow state in Security Operations Centers (SOCs).

Der Winter war laut Deutschem Wetterdienst in Brandenburg so trocken wie in keinem anderen Bundesland. Die Wintersonne schien gute 30 Stunden länger als gewöhnlich. Was hat das für Folgen für die Umwelt und für uns Menschen? Und was können wir tun, um dem zu begegnen? In unserem radio3 Klimagespräch sprechen wir mit Prof. Irina Engelhardt, die das Fachgebiet Hydrogeologie an der TU Berlin leitet.

Die Berliner Feuerwehr und Forschende der TU Berlin planen eine Weltneuheit: Sie wollen ein wasserstoffbetriebenes Feuerlöschboot für Berlin bauen. Von Maren Schibilsky

We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems.The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster's supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind's Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore.SPONSOR MESSAGES:***CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting!https://centml.ai/pricing/Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/**** DiscoPOP - A framework where language models discover their own optimization algorithms* EvoLLM - Using language models as evolution strategies for optimizationThe AI Scientist - A fully automated system that conducts scientific research end-to-end* Neural Attention Memory Models (NAMMs) - Evolved memory systems that make transformers both faster and more accurateTRANSCRIPT + REFS:https://www.dropbox.com/scl/fi/gflcyvnujp8cl7zlv3v9d/Sakana.pdf?rlkey=woaoo82943170jd4yyi2he71c&dl=0Robert Tjarko Langehttps://roberttlange.com/Chris Luhttps://chrislu.page/Cong Luhttps://www.conglu.co.uk/Sakanahttps://sakana.ai/blog/TOC:1. LLMs for Algorithm Generation and Optimization [00:00:00] 1.1 LLMs generating algorithms for training other LLMs [00:04:00] 1.2 Evolutionary black-box optim using neural network loss parameterization [00:11:50] 1.3 DiscoPOP: Non-convex loss function for noisy data [00:20:45] 1.4 External entropy Injection for preventing Model collapse [00:26:25] 1.5 LLMs for black-box optimization using abstract numerical sequences2. Model Learning and Generalization [00:31:05] 2.1 Fine-tuning on teacher algorithm trajectories [00:31:30] 2.2 Transformers learning gradient descent [00:33:00] 2.3 LLM tokenization biases towards specific numbers [00:34:50] 2.4 LLMs as evolution strategies for black box optimization [00:38:05] 2.5 DiscoPOP: LLMs discovering novel optimization algorithms3. AI Agents and System Architectures [00:51:30] 3.1 ARC challenge: Induction vs. transformer approaches [00:54:35] 3.2 LangChain / modular agent components [00:57:50] 3.3 Debate improves LLM truthfulness [01:00:55] 3.4 Time limits controlling AI agent systems [01:03:00] 3.5 Gemini: Million-token context enables flatter hierarchies [01:04:05] 3.6 Agents follow own interest gradients [01:09:50] 3.7 Go-Explore algorithm: archive-based exploration [01:11:05] 3.8 Foundation models for interesting state discovery [01:13:00] 3.9 LLMs leverage prior game knowledge4. AI for Scientific Discovery and Human Alignment [01:17:45] 4.1 Encoding Alignment & Aesthetics via Reward Functions [01:20:00] 4.2 AI Scientist: Automated Open-Ended Scientific Discovery [01:24:15] 4.3 DiscoPOP: LLM for Preference Optimization Algorithms [01:28:30] 4.4 Balancing AI Knowledge with Human Understanding [01:33:55] 4.5 AI-Driven Conferences and Paper Review

Wie lassen sich Tiefseebergbau und Umweltschutz vereinbaren? Das Projekt "DeepSea Protection" unter der Leitung von Matthias Golz der TU Berlin sucht Lösungen. Von Anna Corves.

In dieser Pumpkin Progress-Story spricht unser Co-Founder David Döbele mit unserem Coaching Member Alejandro über seine Erfahrungen mit dem pumpkin Coaching und darüber, wie ihm die Zusammenarbeit mit uns bei seinem Weg von der TU Berlin in die Beratung geholfen hat.

Millionen Tonnen Fisch aus den Ozeanen werden selbst zu Fischfutter. Nun wird an der TU versucht, Fischmehl und -öl als Futter zu ersetzen - durch Mikroalgen. Von Maren Schibilsky

In dieser Folge spricht Jacqueline Klusik-Eckert mit Meike Hopp über die aktuellen Entwicklungen und Herausforderungen der Provenienzforschung. Im Fokus stehen dabei digitale Hilfsmittel wie Datenbanken, die es ermöglichen, komplexe Objekt- und Personenbiographien besser sichtbar zu machen und Wissenssilos aufzubrechen.Während Datenbanken wie das Art Loss Register und die Lost Art Datenbank seit Jahren zur Verfügung stehen, haben sich die Methoden und Werkzeuge zur Erforschung der Herkunft von Kunstwerken und Kulturgütern rasant weiterentwickelt. Die zunehmende Öffnung von Sammlungsinstitutionen hilft dabei. Dennoch gibt es erhebliche Herausforderungen bei der Standardisierung, dem Zugang zu Daten und der internationalen Zusammenarbeit. Und dabei ist das Öffnen der Silos nur ein Aspekt des ganzen. Provenienzforschung ist nämlich viel mehr als nur genug Quellen zusammenzutragen. Datenauswertung im großen Stil verlangt Kompetenzen, die noch lange nicht zum Ausbildungskanon der Kunstgeschichte gehören.Ein besonderer Fokus liegt daneben auf der Notwendigkeit, Forschungsdaten und Quellen so aufzubereiten und zu präsentieren, dass sie nicht nur für Forschende, sondern auch für die breite Öffentlichkeit zugänglich sind. Dabei betont Meike Hopp die Bedeutung der Provenienzforschung, die über die reine Restitution von Kunstwerken hinausgeht. Es geht vermehrt um Teilhabe und Ermächtigung. Betroffenen Familien und Gemeinschaften erhalten erst durch optimal aufbereitete Daten – Stichwort Mehrsprachigkeit – und Interfaces die Möglichkeit, ihre eigene Geschichte aufzuarbeiten.Trotz signifikanter Fortschritte in der Provenienzforschung bangt der Forschungsbereich noch immer um eine nachhaltige Etablierung in der kunsthistorischen Ausbildungslandschaft.Prof. Dr. Meike Hopp, Juniorprofessorin für Digitale Provenienzforschung an der TU Berlin sowie Vorsitzendes des Arbeitskreis Provenienzforschung.Begleitmaterial zu den Folgen findest du auf der Homepage unter https://www.arthistoricum.net/themen/podcasts/arthistocast.Alle Folgen des Podcasts werden bei heidICON mit Metadaten und persistentem Identifier gespeichert. Die Folgen haben die Creative-Commons-Lizenz CC BY 4.0 und können heruntergeladen werden. Du findest sie unter https://doi.org/10.11588/heidicon/1738702.Bei Fragen, Anregungen, Kritik und gerne auch Lob kannst du uns gerne per Mail kontaktieren unter podcast@digitale-kunstgeschichte.de.

Unser heutiger Gast ist Prof. em. Dr. Norbert Bolz. Herr Bolz ist Medienwissenschaftler und lehrte bis 2018 an der TU Berlin. Wir sprechen mit ihm über die aktuelle Medienlandschaft, vor dem Hintergrund des Wahlsiegs von Donald Trump. Norbert Bolz auf X/Twitter: https://x.com/NorbertBolz Kapitel: 0:00 Einleitung 0:50 Realitätsverblendung der Medien anhand der US-Wahl 5:08 Ist Selbstreflektion in den Medien zu beobachten? 8:25 Aktivismus als Selbstverständnis von Journalisten 13:44 Wie unabhängig sind Medien vom Staat? 16:52 Sind alternative Medien wirklich eine Alternative? 19:47 Die Rolle von Joe Rogan im Wahlkampf 23:13 Vertrauenswürdige Leitfiguren in den Medien 28:38 Glaubwürdigkeit durch Mut und Konfrontation 35:44 Können wir die Echokammern aufbrechen? 40:40 Abschluss

Zu Gast ist Stefan Merath. Stefan ist ein renommierter deutscher Unternehmercoach, Bestsellerautor und Gründer der Unternehmercoach GmbH. Seine berufliche Laufbahn ist geprägt von einer Mischung aus akademischer Bildung, unternehmerischer Erfahrung und der Leidenschaft, andere Unternehmer zu unterstützen. Merath begann sein Studium 1983 mit Informatik an der Universität Stuttgart, wechselte dann aber zur Freien Universität Berlin, wo er sich auf Philosophie, Publizistik und Psychologie konzentriert. 1991 schloss er sein Studium als Diplom-Soziologe ab. Nach einer kurzen Zeit als wissenschaftlicher Mitarbeiter an der TU Berlin gründete Merath 1997 die blue orange Internet GmbH, die jedoch 2003 Insolvenz anmelden musste. Diese Erfahrung prägte ihn nachhaltig und führte schließlich 2007 zur Gründung der Unternehmercoach GmbH in Eschbach bei Freiburg. Als Unternehmercoach hat sich Merath darauf spezialisiert, Unternehmer im KMU-Sektor zu unterstützen. Seine Philosophie basiert auf der Überzeugung, dass der Erfolg eines Unternehmens eng mit der persönlichen Entwicklung des Unternehmers verknüpft ist. Er betont die Bedeutung von Selbstbestimmung, Freiheit und innerer Ruhe für erfolgreiches Unternehmertum. Merath ist Autor mehrerer erfolgreicher Bücher über Unternehmertum und Führung. Sein Werk "Der Weg zum erfolgreichen Unternehmer" (2008) gilt als Standardwerk in der deutschsprachigen Unternehmerszene. Weitere bedeutende Publikationen umfassen "Die Kunst, seine Kunden zu lieben" (2011) und "Dein Wille geschehe" (2017). Sein neuestes Buch "Die Schwarzgurt-Unternehmer" (2024) wird als Pflichtlektüre für Unternehmer bezeichnet und behandelt das Konzept des "Schwarzgurt-Unternehmers", das Merath entwickelt hat. Merath hat das Konzept des "Schwarzgurt-Unternehmers" eingeführt, das auf der Idee basiert, dass wahre unternehmerische Meisterschaft durch kontinuierliche persönliche Entwicklung erreicht wird. Dieses Konzept verbindet Prinzipien des fernöstlichen Thai Ki San mit modernen Unternehmensführungsmethoden. Stefan Merath's Ansatz zur Unternehmensführung und persönlichen Entwicklung hat viele Unternehmer inspiriert und beeinflusst. Seine Arbeit zielt darauf ab, Unternehmern zu helfen, nicht nur finanziellen Erfolg zu erreichen, sondern auch ein erfülltes und ausgewogenes Leben zu führen.

SHOWNOTES:ein Vortrag des Mathematikers Thorsten KochModeration: Katrin Ohlendorf *******KI ist in aller Munde. Die einen knüpfen überbordende Hoffnung an sie, andere warnen vor ihren großen Gefahren. Was stimmt nun? Um die Technologie wirklich richtig einschätzen und eine sachliche Diskussion führen zu können, müssen wir sie verstehen. Dabei hilft der Mathematiker Thorsten Koch mit seinem Vortrag. Thorsten Koch ist Professor für Software und Algorithmen der diskreten Optimierung an der TU-Berlin und Leiter der Abteilungen Angewandte Methoden der Algorithmischen Intelligenz und Digitale Daten und Informationen für Gesellschaft, Wissenschaft und Kultur am Zuse-Institut Berlin (ZIB). Seinen Vortrag "Algorithmische Intelligenz und was so auf uns zukommt" hat er am 10. September 2024 im Rahmen der Berliner Sommer-Uni gehalten, die von der HU Berlin und der Berliner Akademie für weiterbildende Studien veranstaltet wurde und unter dem Titel stand "Künstliche Intelligenz und wie sich unsere Gesellschaft verändert". ******* Ihr wollt den Hörsaal live erleben? Wir machen einen Live-Podcast in Halle! Am 1. November 2024. Infos gibt's hier: https://www.deutschlandfunknova.de/beitrag/hoersaal-live-podcast-laesst-der-mensch-die-erde-beben ******* Schlagworte: +++ Künstliche Intelligenz +++ Algorithmische Intelligenz +++ KI +++ AI +++ Large Language Models +++ LLM +++ Algorithmen +++ Suchmaschinen +++ Gesellschaft +++ Konflikte +++ Prompt +++ Prompten +++ Prompting +++ ChatGPT +++ Gemini +++ Zukunft +++ Fortschritt +++ Computer +++ Mathematik +++ Software +++ Alan Turing +++ Turing-Test +++ Science Fiction +++**********Quellen aus der Folge:Folien zum VortragStephen Wolfram: What is ChatGPT Doing… and Why Does It Work?“The thinking machine”, Video zu Künstlicher Intelligenz von 1961Alan M. Turing: Computing Machinery and Intelligence. In: Mind. Band LIX, Nr. 236, 1950, ISSN 0026-4423, S. 433–460Rezension von “Profiles of the Future” (Profile der Zukunft) von Arthur C. Clarke (Engl.)**********Den Artikel zum Stück findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok auf&ab , TikTok wie_geht und Instagram .

(Conversation recorded on October 3rd, 2024)    While humans, like all animals, are subject to certain fundamental realities, we also possess the unique ability to shape the world around us through physical infrastructure, laws and institutions, and our economic and social systems. And yet, it's important to remember that, as today's guest would say, what we design designs us back. In short, the systems and structures we build influence our cultures, values, and identities. Today, Nate is joined by architect and professor of planetary civics, Indy Johar, to explore the relationship between system design and human behavior - and what might be possible for transformational change. Along the way, they discuss the impact of sunk costs on our ability to change, the importance of new language to describe and respond to our human predicament, and envision future governance and economies that could enable the full spectrum of what it means to be human.  What sorts of unconventional ideas, like self-owning land and technology, could lead to economies that are capable of sustaining humans as well as foster a healthy planet? How do our current societies prevent us from embodying and living into our greatest gifts as human beings? Is it possible to intentionally redesign our systems at the physical, structural, and psychological levels in service of all the entangled life inhabiting the Earth?    About Indy Johar: Indy Johar is co-founder of Dark Matter Labs, as well as the RIBA award winning architecture and urban practice Architecture00. He is also a founding director of Open Systems Lab, seeded WikiHouse (open source housing), and Open Desk (open source furniture company). Indy is also a non-executive international Director of the BloxHub, which is the Nordic Hub for sustainable urbanization. He has taught & lectured at various institutions from the University of Bath, TU-Berlin; University College London, Princeton, Harvard, MIT and New School. He is currently a professor at RMIT University.   Show Notes and More Watch this video episode on YouTube   ---   Support The Institute for the Study of Energy and Our Future Join our Substack newsletter Join our Discord channel and connect with other listeners

Quantum Nurse https://graceasagra.com/  presents Freedom International Livestream On Tuesday, Oct 22, 2024 @ 12:00 PM EST SOURCE CONSCIOUSNESS SERIES Featured Guest:  Dieter Broers, BioPhysicist Topic: Solar Revolution: Exploring the Quantum Physics Aspects of an Interconnected Conscious Cosmos. www.dieterbroers.com   Guest Bio/Info: Dieter Broers, a distinguished German biophysicist born in 1951, has made remarkable contributions to the fields of frequency and regulation therapy since the 1980s. With an impressive portfolio of 113 international patents primarily focused on medical therapy and research, Broers has specialized in exploring the effects of weak (non-thermal) electromagnetic fields on biological systems. His pivotal role as project leader for a BMFT initiative in 1987, part of the "Applied Biology and Biotechnology" program, showcased his ability to coordinate an interdisciplinary team involving eleven renowned university departments, including the TU Berlin, FU Berlin, and Humboldt University Berlin. This collaborative effort yielded innovative therapeutic methods utilizing 150 MHz radio waves, ultimately leading to the approval of a specialized frequency generator as a medical device, compliant with contemporary European Medical Device Regulations.   Conversation of Dieter Broers and Dr. Jere Rivera-Dugenio, PhD https://www.youtube.com/watch?v=YpXJTPPcsMw   Creator Host   Grace Asagra, RN MA, QMPPhD Podcast:  Quantum Nurse: Out of the Rabbit Hole from Stress to Bliss TIP/DONATE LINK for Grace Asagra @ Quantum Nurse Podcast https://www.paypal.com/donate/?hosted_button_id=FHUXTQVAVJDPU Venmo - @Grace-Asagra 609-203-5854 https://patron.podbean.com/QuantumNurse https://graceasagra.com/   WELLNESS RESOURCES Optimal Health and Wellness with Grace Virtual Dispensary Link (Designs for Health)               2https://www.designsforhealth.com/u/optimalhealthwellness Quantum Nurse Eternal Health (Face Skin Care, Protein Powder and Elderberry) https://www.quantumnurseeternalhealth.com/   Co-host: Dr. Alfredo, QMPPhD Email: DrAlfredoQMPPhD@ gmail.com   Co-host: George Chirco, EE, BS, MQMP-www.GraceAsagra.com-QRA Vastu Master Practitioner –IN-PERSON Consultation -New Jersey, New York City, Philadelphia and surrounding areas or VIRTUAL

Verträge werden nicht verlängert, Promotionen erschwert: An deutschen Hochschulen werde besonders gegenüber jungen Wissenschaftlern hoher Druck ausgeübt, sagt die Präsidentin der TU Berlin, Geraldine Rauch. Drang zur Veränderung gebe es nur wenig. Rauch, Geraldine www.deutschlandfunkkultur.de, Studio 9

Ich spreche mit Dr. Friederike Rohde über den Energiebedarf von Künstlicher Intelligenz. Sie arbeitet beim Institut für Ökologische Wirtschaftsforschung und beim Berlin Ethics Lab der TU Berlin und hat gemeinsam mit AlgorithmWatch und dem Distributed Artificial Intelligence Labor der TU Berlin im Rahmen des Leutturmprojekts SustAIn untersucht, wie KI nachhaltiger werden kann. https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.heiseplus.de/podcast https://www.ct.de/ki https://www.ioew.de/news/article/ki-verbraucht-immer-mehr-ressourcen-jetzt-nachhaltigkeit-messen https://www.ioew.de/publikation/taking_policy_action_to_enhance_the_sustainability_of_ai_systems

Zurücktreten wegen eines Likes auf Social Media: Diese Diskussion wurde etwa im Fall der Präsidentin der TU Berlin, Geraldine Rauch, geführt. Aber meint ein Daumen nach oben immer Zustimmung? Und wer entscheidet, was ein Like oder Emoji bedeutet? Opitz, Till www.deutschlandfunk.de, Systemfragen

Timo Berthold is a Director at FICO, leading the MIP research and development team of the FICO Xpress Solver. In additon, he is a lecturer at the Mathematical Optimization Department of TU Berlin, working of the intersection of academia and industry. Before joining FICO, Timo was a main developer of the open-source MIP and MINLP solver SCIP at Zuse Institue Berlin. Timo is an expert on all aspects of computational mixed-integer linear and nonlinear optimization, heuristic methods, and recently, the integration of ML methods into optimization solvers. He has published over 50 papers in this field, supervised many talented students and won multiple prestigious awards for his research. As a fun-fact, his PhD thesis won the 2014 GOR dissertation award and made the finals of the EURO dissertation award, while at the same time, an accompanying article on his PhD work received a science-communication prize for being the best "Math research explained to the general public" article of the year.

Zurücktreten wegen eines Likes auf Social Media: Diese Diskussion wurde etwa im Fall der Präsidentin der TU Berlin, Geraldine Rauch, geführt. Aber meint ein Daumen nach oben immer Zustimmung? Und wer entscheidet, was ein Like oder Emoji bedeutet? Opitz, Till www.deutschlandfunk.de, Systemfragen

Die Angst vor dem Klimawandel, die Krise der Demokratie, die Brutalität des Krieges – es ist das pessimistische Grundgefühl unserer Zeit, dass sich die Welt nicht zum Besseren entwickelt, im Gegenteil: Wo einst Aufbruch war, herrscht heute gefühlter Verlust. In der allgemeinen Rede von Transformation wird allein die Sicherung des Status Quo zum Erfolg. Anpassung statt Steigerung lautet die Maxime der Gegenwart. Geht uns der Glaube an den Fortschritt verloren? Michael Risel diskutiert mit Prof. em. Dr. Norbert Bolz - Philosoph, TU Berlin, Prof. Dr. Heiner Hastedt - Philosoph, Universität Rostock, Petra Pinzler - Journalistin, „DIE ZEIT“

Host Chris Adams is joined by special guest Philipp Wiesner, a research associate and PhD student at TU Berlin, to discuss how computing systems can better align energy consumption with clean energy availability. Contributing to Project Vessim, Philipp explains how researchers are now able to model different energy consumption scenarios, from solar and wind power integration to the complexities of modern grids despite the scarcity of available testing environments. They discuss federated learning and its role in carbon-aware designs, along with challenges in tracking real energy savings. Tune in to learn about the future of carbon-aware computing and the tools being developed to help software become more sustainable.

Widerstand gegen den eigenen Konsum ist möglich, sagt Psychologin Marlene Münch. Ein Konsumpass der TU Berlin soll dabei helfen, sinnvoll auszumisten und mit Kaufanreizen richtig umzugehen. Ein großer Teil unserer Dinge wird im Alltag nicht genutzt. Münsch, Marlene www.deutschlandfunkkultur.de, Studio 9

The Distributed AI Research Institute, or DAIR—which seeks to conduct community-rooted AI research that is independent from the technology industry—has launched a new project called the Data Workers' Inquiry to invite data workers to create their own research and recount their experiences. The project is supported by DAIR, the Weizenbaum Institute, and TU Berlin. For this episode, journalist and audio producer Rebecca Rand parsed some of the ideas and experiences discussed at a virtual launch event for the inquiry that took place earlier this month.

Der antisemitische Nährboden im Internet wird durch Künstliche Intelligenz noch einmal beschleunigt, sagt der Linguist Marcus Schreiber von der TU Berlin. Durch Umwegkommunikation werde die Aufspürung von antisemitischen Posts zudem erschwert. Schreiber, Marcus www.deutschlandfunkkultur.de, Fazit

Über das Ergebnis der Europa-Wahl 2024 mit Fokus auf Deutschland, Frankreich und Polen spricht Gerd Buurmann mit der Politologin und Publizistin Aleksandra Rybińska und dem Juristen und Professor für Finanzwirtschaft und Wirtschaftspolitik an der TU Berlin, Markus C. Kerber.

Die Präsidentin der TU Berlin, Geraldine Rauch, hat auf X mehrfach Sympathien mit antisemitischen Inhalten bekundet. Ihre Reue kommt zu spät und ist unglaubwürdig - einer politischen Amtsträgerin kann man solche Fehler nicht verzeihen. Ein Kommentar von Sebastian Engelbrecht

van Laak, Claudia www.deutschlandfunkkultur.de, Studio 9

Das Einkommen der Deutschen ist im EU-Vergleich ziemlich gut. Der Streit um Lauterbachs Klinikatlas eskaliert. Und die Präsidentin der TU Berlin beantragt ein Disziplinarverfahren gegen sich selbst. Das ist die Lage am Mittwochabend.  Die Artikel zum Nachlesen: So gut ist Ihr Einkommen im Europavergleich Schleswig-Holstein fordert Warnung bei Klinikatlas – oder seine Abschaltung TU-Präsidentin beantragt Disziplinarverfahren gegen sich selbst+++ Alle Infos zu unseren Werbepartnern finden Sie hier. Die SPIEGEL-Gruppe ist nicht für den Inhalt dieser Seite verantwortlich. +++ Den SPIEGEL-WhatsApp-Kanal finden Sie hier. Alle SPIEGEL Podcasts finden Sie hier. Mehr Hintergründe zum Thema erhalten Sie bei SPIEGEL+. Jetzt für nur € 1,- für die ersten vier Wochen testen unter spiegel.de/abonnieren Informationen zu unserer Datenschutzerklärung.

Geraldine Rauch, Präsidentin der TU Berlin, likte einen antisemitischen Post und entschuldigte sich dafür. Der Akademische Senat gab ihr eine Frist, selbst über ihren Verbleib zu entscheiden. Politologin Barbara Zehnpfennig meint: Rauch muss gehen. Zehnpfennig, Barbara www.deutschlandfunkkultur.de, Fazit

van Laak, Claudia www.deutschlandfunkkultur.de, Studio 9

Ameln, Leonard www.deutschlandfunk.de, Kommentare und Themen der Woche

ein Vortrag des Wirtschafts- und Sozialwissenschaftlers Tilman Santarius Moderation: Nina Bust-Bartels ********** Wer ist schuld am Klimawandel? Wenn man sich sie CO2-Emissionen der verschiedenen Einkommensgruppen anschaut, wird klar: Klimawandel ist ein Reichtumsproblem. Ein Vortrag des Klimaforschers Tilman Santarius. Tilman Santarius ist Professor für sozial-ökologische Transformation an der Technischen Universität Berlin. Den Vortrag "Klimawandel, Klimapolitik und soziale Gerechtigkeit" hat er am 15. Januar 2024 im Rahmen der "TU Berlin for Future - Ringvorlesung zum Klimaschutz" gehalten. Die Präsentation zum Vortrag findet ihr auf Youtube. Und wenn ihr noch mehr Klimawissenschaft hören wollt, dann hört mal in den Podcast Update Erde rein. Da erzählen euch jede Woche zwei Hosts aus den Deutschlandfunk Nova-Wissensnachrichten, was sich in der Woche Wichtiges in Sachen Klimawandel und Biodiversität getan hat. Neue Folgen gibt es immer freitags: https://www.deutschlandfunknova.de/podcasts/landingpage/update-erde ********** Schlagworte: +++ Klimawandel +++ Klimakrise +++ Klimagerechtigkeit +++Klimaforschung +++ Klima +++ Gerechtigkeit +++ Reichtum +++ Ungleichheit +++ Einkommensverteilung +++ Vermögensverteilung +++ Vorlesung +++ Wissenschaft +++ Hörsaal +++ Universität**********Mehr zum Thema bei Deutschlandfunk Nova:CO2-Preis: Bester Anreiz für klimafreundliches VerhaltenUltrareichtum in Deutschland - Martyna Linartas: "Das ist eine Spaltung"Generationengerechtigkeit: Ältere leben auf Kosten der Jungen**********Den Artikel zum Stück findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: Tiktok und Instagram.

In this final episode of our short series, host Matt Prewitt speaks with Indy Johar, architect and co-founder of Dark Matter Labs. Together they discuss the topic of ownership through the lens of theories of governance. Indy advocates for decentralized protocols in property governance, emphasizing complex contributions and contextual responsiveness – moving away from control-oriented systems towards ennobling frameworks that empower individuals and foster deeper engagement.RadicalxChange has been working with Indy Johar and Dark Matter Labs, together with Margaret Levi and her team at Stanford, on exploring and reimagining the institutions of ownership.This episode is part of a short series exploring the theme of What and How We Own: Building a Politics of Change.Read more in our newsletter What & How We Own: The Politics of Change | Part III.Links & References: References:The Code of Capital | Princeton University Press by Katharina PistorDaniel Schmachtenberger: Steering Civilization Away from Self-Destruction | Lex Fridman Podcast #191Partial Common Ownership | RxC Wiki[The Bellagio Model: an evidence-informed, international framework for population-oriented primary care. First experiences]Hayekian economic policy - ScienceDirectJames Lovelock - WikipediaThe Economics of Care | Elizabeth Hill Bios:Indy Johar (he/him) is an architect, co-founder of 00 (project00.cc), and most recently Dark Matter Labs.Indy, on behalf of 00, has co-founded multiple social ventures from Impact Hub Westminster to Impact Hub Birmingham. He has also co-led research projects such as The Compendium for the Civic Economy, whilst supporting several 00 explorations/experiments including the wikihouse.cc, opendesk.cc. Indy is a non-executive director of WikiHouse Foundation & Bloxhub. Indy was a Good Growth Commissioner for the RSA, RIBA Trustee, and Advisor to Mayor of London on Good Growth, The Liverpool City Region Land Commissioner, The State of New Jersey - The Future of Work Task Force - among others.Most recently he has founded Dark Matter - a field laboratory focused on building the institutional infrastructures for radicle civic societies, cities, regions, and towns.Dark Matter works with institutions around the world, from UNDP (Global), Climate Kic, McConnell (Canada), to the Scottish Gove to Bloxhub (Copenhagen)He has taught and lectured at various institutions including the University of Bath, TU-Berlin; Architectural Association, University College London, Princeton, Harvard, MIT, and New School.He writes often on the https://provocations.darkmatterlabs.orgIndy's Social Links:Indy Johar (@indy_johar) / XIndy Johar - London, United Kingdom, Project00.cc | about.meIndy Johar – MediumMatt Prewitt (he/him) is a lawyer, technologist, and writer. He is the President of the RadicalxChange Foundation.Matt's Social Links:ᴍᴀᴛᴛ ᴘʀᴇᴡɪᴛᴛ (@m_t_prewitt) / XAdditional Credits:This episode was recorded and produced by Matt Prewitt.This is a RadicalxChange Production. Connect with RadicalxChange Foundation:RadicalxChange Website@RadxChange | TwitterRxC | YouTubeRxC | InstagramRxC | LinkedInJoin the conversation on Discord.Credits:Produced by G. Angela Corpus.Co-Produced, Edited, Narrated, and Audio Engineered by Aaron Benavides.Executive Produced by G. Angela Corpus and Matt Prewitt.Intro/Outro music by MagnusMoone, “Wind in the Willows,” is licensed under an Attribution-NonCommercial-ShareAlike 3.0 International License (CC BY-NC-SA 3.0)

Martin Grötschel, born in 1948, studied mathematics at U Bochum (1969-1973), received his PhD in economics (1977) and his habilitation in Operations Research (1981) at U Bonn. He was professor of applied mathematics at U Augsburg 1982-1991, professor of information technology at TU Berlin and vice president/president of the Zuse Institute for Information Technology Berlin (ZIB) 1991-2015. Grötschel was the President of the German Mathematical Society (DMV) 1993-1994, Secretary General of the International Mathematical Union (IMU) 2007-2014, President of the Berlin Brandenburg Academy of Sciences and Humanities (BBAW) 2015-2020 and chaired the DFG Research Center MATHEON “Mathematics for Key Technologies” 2002-2008. He has held numerous further science administration and advisory positions. Grötschel's main areas of research are discrete mathematics, optimization, and operations research. He has made, e.g., contributions to polyhedral combinatorics and to the development of methods proving the polynomial time solvability of optimization problems. He has also focused on the design of practically efficient algorithms for hard combinatorial optimization problems appearing in practice, such as the travelling salesman, the max-cut, the linear ordering, and various connectivity problems. Cutting plane algorithms for integer programming are among his favorites. The application areas include telecommunications, chip design, energy, production planning and control, logistics, and public transport. He has been an open access and open science activist and is currently involved in fostering digital humanities. Grötschel's scientific achievements were honored with several distinctions including the Cantor Medal, the Leibniz, the Beckurts, the Dantzig, the Fulkerson, and the John von Neumann Theory Prize. He holds four honorary degrees and is a member of seven scientific academies, including the US National Academy of Engineering. For more details, see http://www.zib.de groetschel/ Martin Grötschel and his wife Iris enjoy travelling, understanding and appreciating varied cultures, and exploring their history and archaeology.

The United Launch Alliance's Delta IV rocket carries an NRO satellite to orbit for its final flight. NASA unveils their Space Sustainability Strategy. Astrobotic has announced that it is working on a project to bring 3D printing to the Moon, and more. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our weekly intelligence roundup, Signals and Space, and you'll never miss a beat. And be sure to follow T-Minus on LinkedIn and Instagram. T-Minus Guest Our guests are Kathy O'Donnell, Senior Manager, AWS Space Specialist Solutions Architecture, and Derek McCoy, Head of Channel, Enterprise, & Public Sector at Rescale. You can learn more about AWS Aerospace and Satellite on their website.  Selected Reading Ending an era, final Delta 4 Heavy boosts classified spy satellite into orbit - CBS News  NASA's Space Sustainability Strategy Next Step Toward the Moon: LZH and TU Berlin partner with Astrobotic Vast's Haven-1 to be World's First Commercial Space Station Connected by SpaceX Starlink Firefly Aerospace Announces Agreement with Klepsydra Technologies to Demonstrate Edge Computing in Space Aegis Aerospace Closes Strategic Acquisition Russia aborts planned test launch of new heavy-lift space rocket T-Minus Crew Survey We want to hear from you! Please complete our 4 question survey. It'll help us get better and deliver you the most mission-critical space intel every day. Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at space@n2k.com to request more info. Want to join us for an interview? Please send your pitch to space-editor@n2k.com and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Vortrag des Entwicklungspsychologen Daniel HaunModeration: Katrin Ohlendorf**********Wie hängen Sprache und Denken zusammen? Wie unterscheiden sich Denken und Wahrnehmung bei Tieren und Menschen? Und welche Unterschiede gibt es dabei möglicherweise auch zwischen einzelnen Individuen und menschlichen Kulturkreisen? Diesen spannenden Fragen geht in diesem Vortrag Daniel Haun vom Max-Planck-Institut für evolutionäre Anthropologie in Leipzig nach. Anhand vergleichender Studien und Experimente mit nichtmenschlichen Primaten und in unterschiedlichen Sprachregionen deckt er spannende Zusammenhänge auf und plädiert damit gleichzeitig dafür, dass wir Menschen uns von unserem Egozentrismus verabschieden müssen, damit wir unsere eigene Art überhaupt richtig erforschen und verstehen können.********* Daniel Haun leitet die Abteilung für Vergleichende Kulturpsychologie am Max-Planck-Institut für evolutionäre Anthropologie in Leipzig und lehrt als Professor an der Erziehungswissenschaftlichen Fakultät der Leipziger Uni. Er interessiert sich unter anderem für die kulturelle Vielfalt des Menschen und was Kognition damit zu tun hat. Dafür forscht er nicht nur in unterschiedlichen Kulturen sondern auch mit Menschenaffen. Seinen Vortrag „Denken mit und ohne Sprache. Lehren aus der Kognitionsforschung mit Menschen und anderen Affen “ hat er am 21. November 2023 gehalten und zwar im Rahmen der jährlichen Walter-Höllerer-Vorlesung. Mit ihr erinnert die Gesellschaft von Freunden der TU Berlin an den Literaturwissenschaftler und Schriftsteller Walter Höllerer.**********Schlagworte: +++ Kognitionswissenschaften +++ Anthropologie +++ Denken +++ Sprache +++ Primaten +++ Menschenaffen +++ Kognition +++**********Den Artikel zum Stück findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: Tiktok und Instagram.

Viele der historischen Parkanlagen in Deutschland leiden einer Studie zufolge unter Klimastress und massiven Schädigungen. Grund seien die extremen Wetterphänomene der Jahre 2017, 2018 und 2019, heißt es in einer Studie der Technischen Universität (TU) Berlin. In der Folge sei es zu Astbrüchen, Entwurzelungen und auch zum Absterben ganzer Baumbestände gekommen.

The Boring Revolution. The matter of this Better Worlds episode is far from mundane. As advocated by Indy Johar, co-founder of Dark Matter Labs, who visited with Green Planet Blue Planet Host Julian Guderley for this podcast episode, a boring revolution is a fundamental shift in how we as humans perceive ourselves, our relationships, and the institutional frameworks that reinforce those perceptions. The old world view created over time, positions humans as dominion over everything instead of recognizing the agency and aliveness of everything, including objects. This episode explores multiple facets of this paradigm shift challenges us to fundamentally rethink what it means to be human and how we relate to each other and the planet. Indy suggests our current worldview and societal structures are extractive, guided by externalities, and they put humanity at risk of self-termination. In other words, we have constructed a language of humans being in dominion over the world, in control of the world through theories constructed in various ways, including by religions. Next, Indy says, we constructed perspective, which put distance between us and put control into bureaucracy, governance, kings etc. Humans then separated themselves from the world, turned things into objects rather than perceiving them as entanglements in relationship with humans. That led to classifications and language shifts from verb - action oriented terms - to nouns, and finally moved into a thesis of property as a universal means of organizing. The worldview became one of control over, and property - ownership - became an enslavement of things. To hear more about these fascinating and complex theories, tune in now, let us know what you think, like it and share, and then visit us at betterworlds.com for more shows and podcast subjects.  About Indy Johar Indy Johar is focused on the strategic design of new super scale civic assets for transition - specifically at the intersection of financing, contracting and governance for deeply democratic futures. Indy is co-founder of darkmatterlabs.org and of the RIBA award winning architecture and urban practice Architecture00 - https://www.architecture00.net, a founding director of open systems lab - https://www.opensystemslab.io (digitising planning), seeded WikiHouse (open source housing) - https://www.wikihouse.cc  and Open Desk (open source furniture company) https://www.opendesk.cc. Indy is a non-executive international Director of the BloxHub https://bloxhub.org (Denmark Copenhagen) - the Nordic Hub for sustainable urbanization and was 2016-17 Graham Willis Visiting Professorship at Sheffield University.  He was also Studio Master at the Architectural Association - 2019-2020, UNDP Innovation Facility Advisory Board Member  2016-20 and RIBA Trustee 2017-20. He has taught & lectured at various institutions from the University of Bath, TU-Berlin; University College London, Princeton, Harvard, MIT and New School.  Most recently, he was awarded the London Design Medal for Innovation in 2022. About Dark Matter Labs  Dark Matter Labs is not-for-profit designing and building the underlying infrastructure to support this new civic economy, exploring how ownership, legal systems, governance, accountancy and insurance might begin to change.  The boring revolution︎ is designed propel wider societal transition. The team is establishing toolkits and blueprints, pilots, and case studies, supporting communities and institutions with applications, digital products and civic technologies that challenge established thought and demonstrate that an alternative is possible. --- Support this podcast: https://podcasters.spotify.com/pod/show/julian-guderley/support

Zu Gast im Studio: Verkehrssoziologe Andreas Knie, Sozialwissenschaftler am Wissenschaftszentrum Berlin für Sozialforschung gGmbH (WZB) und Professor für Soziologie an der TU Berlin. Ein Gespräch auf die Autoideologie der Deutschen, die Geschichte des Automobils, des Fahrrads sowie des Bahn-, Schiffs- und Flugverkehrs, über den Unterschied von Mobilität und Verkehr, den Einfluss der NS-Zeit auf die Verkehrsgeschichte, Tankstellen- und Ladeinfrastruktur, Ampeln und Parken im öffentlichen Raum, Verknappung vom Fliegen, Inlandlandsflugverbot, Verbot von Kreuzfahrten, Andreas' Kindheit, Jugend und Werdegang, seine Promotionsarbeit über den Diesel, die Macht der Autoindustrie, der Autolobbyismus eines jeden Deutschen, kollektive vorauseilende Angst, das Bundesverkehrsministerium als Autoindustrieministerium, Elektroautos, ÖPNV, seine Vorstellung von einer Verkehrswende, Mobilität auf dem Land, autonomes Fahren in der Stadt und Städte frei von privatbesessenen PKWs, CO2-Emissionen des Verkehrssektors und neue Autobahnprojekte der Ampelregierung uvm. + eure Fragen via Hans Bitte unterstützt unsere Arbeit finanziell: Konto: Jung & Naiv IBAN: DE854 3060 967 104 779 2900 GLS Gemeinschaftsbank PayPal ► http://www.paypal.me/JungNaiv

ein Vortrag des Historikers Uffa Jensen Moderation: Katrin Ohlendorf ********** 19. Dezember 1980: Der jüdische Verleger Shlomo Lewin und seine Freundin Frida Poeschke werden in ihrem Haus erschossen. Die Polizei tappt lange im Dunkeln, übersieht naheliegende Hinweise auf rechtsextreme Kreise. Der Historiker Uffa Jensen erinnert an den ersten antisemitischen Mord in Deutschland nach 1945 und erklärt, was wir daraus Wichtiges für heute lernen sollten. ********** Uffa Jensen lehrt Geschichte an der TU Berlin und forscht am dortigen Zentrum für Antisemitismusforschung, dessen stellvertretender Direktor er auch ist. Seinen Vortrag „Die vergessene Geschichte des Rechtsterrorismus in der Bundesrepublik. Der antisemitische Doppelmord an Shlomo Lewin und Frida Poeschke“ hat er am 23. November 2023 gehalten. Der Vortrag war Teil der Vortragreihe „Mehr als eine Randnotiz. Die extreme Rechte in der deutschen Gesellschaft nach 1945“. Veranstaltet hat die das Zentrum für Weiterbildung der Universität Hamburg und die Forschungsstelle für Zeitgeschichte in Hamburg in Kooperation mit der Hamburger Landeszentrale für politische Bildung und der Stiftung Hamburger Gedenkstätten und Lernorte zur Erinnerung an die Opfer der NS-Verbrechen. ********** Schlagworte: +++ Antisemitismus +++ Struktureller Antisemitismus +++ Rechtsterrorismus +++ Rechte Gewalt +++ Neonazis +++ Polizei +++ Justiz +++ Geschichte +++ Mordfall +++ Erlanger Doppelmord +++ Shlomo Lewin +++ Frida Poeschke +++**********Empfehlungen aus der Folge:Uffa Jensen: Ein antisemitischer Doppelmord. Die vergessene Geschichte des Rechtsterrorismus in der Bundesrepublik, Suhrkamp 2022 **********Mehr zum Thema bei Deutschlandfunk Nova:Rechtsextremismus: Warum die Polizei in den 90ern versagteBürgerwehren: Rechtsextremismus getarnt als NachbarschaftshilfeRechtsextremismus: Wie stark er in der Polizei verbreitet ist**********Den Artikel zum Stück findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: Tiktok und Instagram.

In today's show, I'm receiving Art Lapinsch, the creator of the Climate Tech newsletter called Delphi Zero, and a super inspiring mind.We talk about:-Why Climate Tech has a HUGE Story Problem-how we can learn from other mega entertainment successes like-

Endlich hat Christian einen Grund mal seinen allerbesten Freund David in den Podcast einzuladen, denn Netflix hat den Kult-Manga/Anime "One Piece" verfilmt. Da Christian sich damit nur grob auskennt, lässt er sich von den Megafans David Seibt (Soziologe, TU Berlin) und Louis Derfert (angehender Filmemacher) erklären, was One Piece ausmacht, und ob Netflix das verstanden hat.

This week's guest is Elias Grünewald, Privacy Engineering Research Associate at Technical University, Berlin, where he focuses on cloud-native privacy engineering, transparency, accountability, distributed systems, & privacy regulation. In this conversation, we discuss the challenge of designing privacy into modern cloud architectures; how shifting left into DevPrivOps can embed privacy within agile development methods; how to blend privacy engineering & cloud engineering; the Hawk DevOps Framework; and what the Shared Responsibilities Model for cloud lacks. Topics Covered:Elias's courses at TU Berlin: "Programming Practical Privacy: Web-based Application Engineering & Data Management" & "Advanced Distributed Systems Prototyping: Cloud-native Privacy Engineering"Elias' 2022 paper, "Cloud Native Privacy Engineering through DevPrivOps" - his approach, findings, and frameworkThe Shared Responsibilities Model for cloud and how to improve it to account for privacy goalsDefining DevPrivOps & how it works with agile developmentHow DevPrivOps can enable formal privacy-by-design (PbD) & default strategiesElias' June 2023 paper, "Hawk: DevOps-Driven Transparency & Accountability in Cloud Native Systems," which helps data controllers align cloud-native DevOps with regulatory requirements for transparency & accountabilityEngineering challenges when trying to determine the details of personal data processing when responding to access & deletion requestsA deep-dive into the Hawk 3-phase approach for implementing privacy into each DevOps phase: Hawk Release; Hawk Operate; & Hawk MonitorHow open sourced project, TOUCAN, is documenting conceptual best practices for corresponding phases in the SDLC, and a call for collaborationHow privacy engineers can convince their management to adopt a DevPrivOps approachRead Elias' papers, talks, & projects:Cloud Native Privacy Engineering through DevPrivOpsHawk: DevOps-driven Transparency and Accountability in Cloud Native Systems CPDP Talk: Privacy Engineering for Transparency & Accountability TILT: A GDPR-Aligned Transparency Information Language & Toolkit for Practical Privacy EngineeringTOUCAN Guest Info:Connect with Elias on LinkedInContact Elias at TU Berlin Privado.ai Privacy assurance at the speed of product development. Get instant visibility w/ privacy code scans.Shifting Privacy Left Media Where privacy engineers gather, share, & learnDisclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Copyright © 2022 - 2024 Principled LLC. All rights reserved.

Photo: No known restrictions on publication. @Batchelorshow 1/2:  #Climate:  #EU:  Future climate discipline on transportation , housing and cuisine.  Felix Creutzig,TU Berlin. https://www.nature.com/articles/d41586-022-01616-z Professor of Sustainability Economics of Human Settlements at TU Berlin Scientific Coordinator Climate Change Center Coordinating Lead Author of the IPCC's 6th Assessment Report

Photo: No known restrictions on publication. @Batchelorshow 2/2:  #Climate: #EU: Future climate discipline on transportation , housing and cuisine.  Felix Creutzig, TU Berlin https://www.nature.com/articles/d41586-022-01616-z Professor of Sustainability Economics of Human Settlements at TU Berlin Scientific Coordinator Climate Change Center Coordinating Lead Author of the IPCC's 6th Assessment Report