Taming the Machines — Horizons of Artificial Intelligence. The Ethics in Information Technology Public Lecture Series
This summer‘s „Taming the Machine“ lecture series sheds light on the ethical, political, legal, and societal dimensions of Artificial Intelligence (AI).Prof. Dr. Andra Siibak, University of Tartu, Tartu, Estland
Present day children’s futures are decided by algorithms predicting their probability of success at school, their suitability for a job position, their likely recidivism or mental health problems. Advances in predictive analytics, artificial intelligence (AI) systems, behavioral-, and biometrics technologies, have started to be aggressively used for monitoring, aggregating, and analyzing children’s data. Such dataveillance happening both in homes, schools, and peer networks has a profound impact not only on children’s preferences, social relations, life chances, rights and privacy but also the "future of human agency - and ultimately, of society and culture" (Mascheroni & Siibak 2021: 169).
Building upon the findings of my different empirical case studies, I will showcase how the popular digital parenting practices and the growing datafication happening in the education sector, could create not only hypothetical data scares but also lead to real data scars in the lives of the young.
Institutions
Taming the Machines — Horizons of Artificial Intelligence. The Ethics in Information Technology Public Lecture Series
This summer‘s „Taming the Machine“ lecture series sheds light on the ethical, political, legal, and societal dimensions of Artificial Intelligence (AI).Vincent C. Müller is AvH Professor for Philosophy and Ethics of AI and Director of the Centre for Philosophy and AI Research (PAIR) at FAU Erlangen-Nuremberg
It is now frequently observed that there is no proper scope and no proper method in the discipline of AI-ethics. This has become an issue in the development towards maturity of the discipline, e.g. canonical problems, positions, arguments … secure steps forward. We propose a minimal, yet universal view of the field (again Müller 2020). Given this proposal, we will know the scope and the method, and we can appreciate the wide set of contributions.
Institutions
Prof. Dr. José van Dijck (Utrecht University, NL)
The growing dominance of two global platform ecosystems has left European countries to rely on American and Chinese digitale infrastructures. This dependency is not just affecting markets and labor relations, but is also transforming social practices, and affecting democracies. While two large ecosystems fight for information control in the global online world, the European perspective on digital infrastructures is focused on regulation rather than on building alternatives. With emerging technologies such as generative AI (ChatGPT, Bard) and geopolitical changes, the infrastructural perspective becomes more poignant. How can Europe achieve sovereignty in the digital world?
This lecture takes up two questions. First, what public values are fundamental to Europe’s platform societies? Values such as privacy, security, transparency, equality, public trust, and (institutional, professional) autonomy are important principles upon which the design of platform architectures should be based. Second, what are the responsibilities of companies, governments, and citizens in building an alternative, sustainable platform ecosystem based on those public values?
Institutions
Institutions
Institutions
Taming the Machines — Horizons of Artificial Intelligence. The Ethics in Information Technology Public Lecture Series
This summer‘s „Taming the Machine“ lecture series sheds light on the ethical, political, legal, and societal dimensions of Artificial Intelligence (AI).Prof. Dr. Aimee van Wynsberghe, Rheinische Friedrich-Wilhelms-Universität Bonn, D
Institutions
Institutions
Prof. Dr. Sven Ove Hansson (Uppsala University, SE)
tbd
InstitutionsSpeaker: Prof. Dr. Elena Esposito, Universität Bielefeld, DE
Institutions
Prof. Dr. Darian Meacham (Maastricht University, NL)
Institutions
Explore the transformative potential of the Population Dynamics Foundation Model (PDFM), a cutting-edge AI model designed to capture complex, multidimensional interactions among human behaviors, environmental factors, and local contexts. This workshop provides an in-depth introduction to PDFM Embeddings and their applications in geospatial analysis, public health, and socioeconomic modeling.
Participants will gain hands-on experience with PDFM Embeddings to perform advanced geospatial predictions and analyses while ensuring privacy through the use of aggregated data. Key components of the workshop include:
By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges.
Institution
Gastvortrag von Lin Jia, Senior Data Scientists bei Booking.com. Sie wird an unserem Seminar über kausales maschinelles Lernen am 8. Juli 2024 teilnehmen und über vergangene und aktuelle Projekte zu kausaler Inferenz und kausalem maschinellem Lernen bei Booking.com referieren.
Booking.com ist eine der weltweit führenden digitalen Reise-Plattformen und verfügt über ein starkes Team von Datenwissenschaftlern, die Experten sind in der Anwendung und Entwicklung von Methoden für kausale Analysen und maschinelles Lernen in der Industrie.
Über die Referentin: Lin Jia ist eine leitende Datenwissenschaftlerin bei Booking.com. Sie ist spezialisiert auf die Verwendung von kausalen Beobachtungsansätzen zur Bewertung der Auswirkungen von Produktänderungen und leitet die Initiative zur Durchführung robuster und transparenter Kausalanalysen bei Booking.com. Sie wird ihre Erfahrungen aus verschiedenen Projekten zur kausalen Inferenz bei Booking.com teilen.
Der Vortrag ist offen für alle interessierten Forscher:innen und Studierenden, die etwas über die Aktivitäten der Industrie in der kausalen Datenwissenschaft erfahren möchten.
Institutions
Gerhard Wellein: Application Knowledge Required: Performance Modeling for Fund and Profit & Axel Klawonn: What can machine learning be used for in domain decomposition methods?
Gerhard Wellein is a Professor for High Performance Computing at the Department for Computer Science of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and holds a PhD in theoretical physics from the University of Bayreuth. He is a member of the board of directors of the German NHR-Alliance which coordinates the national HPC Tier-2 infrastructures at German universities. As a member of the scientific steering committees of the Leibniz Supercomputing Centre (LRZ) and the Gauss-Centre for Supercomputing (GCS) he is organizing and surveying the compute time application process for national HPC resources. Gerhard Wellein has more than twenty years of experience in teaching HPC techniques to students and scientists from computational science and engineering, is an external trainer in the Partnership for Advanced Computing in Europe (PRACE) and received the “2011 Informatics Europe Curriculum Best Practices Award” (together with Jan Treibig and Georg Hager) for outstanding teaching contributions. His research interests focus on performance modelling and performance engineering, architecture-specific code optimization, novel parallelization approaches and hardware-efficient building blocks for sparse linear algebra and stencil solvers.
Prof. Dr. Axel Klawonn heads the research group on numerical mathematics and scientific computing at the Universität zu Köln. The group works on the development of efficient numerical methods for the simulation of problems from computational science and engineering. This comprises the development of efficient algorithms, their theoretical analysis, and the implementation on large parallel computers with up to several hundreds of thousands of cores. A special focus in the applications is currently on problems from biomechanics/medicine, structural mechanics, and material science. The research is in the field of numerical methods for partial differential equations and high performance parallel scientific computing, including machine learning.
A multitude of ML tasks in particle physics, from unfolding detector effects to refining simulation and extrapolating background estimations, require mapping one arbitrary distribution to another. Several indirect methods have been developed to achieve this, such as classifier-based reweighting on a distribution level, or conditional generative models. However, training an ML model to perform a direct, deterministic mapping has long been a challenging prospect.
In this talk, I introduce the concept of Schrödinger Bridges, ML architecture closely related to Diffusion Models, which enables direct mapping of arbitrary distribution to arbitrary distribution. I demonstrate two implementation approaches with differing upsides and present state-of-the-art results applying Schrödinger Bridges to unfolding and refinement tasks.
Institutions
bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.
Angela Relógio, Medical School Hamburg MSH
For further details and hybrid links, please go to the webpage AI in Biology & Medicine
bAIome Center for biomedical AI (UKE) and Bernhard Nocht Institute for Tropical Medicine (BNITM) will host the seminar series entitled “AI in biology and Medicine”. This series aims to capture a broad audience and promote cross institutional collaboration. Our expert speakers will give an overview and insight into particular AI/data science methods being developed in key areas of biology and medicine. We will have drinks and snacks following each seminar to facilitate exchange.
Christopher Gundler, Institute for Applied Medical Informatics, UKE
For further details and hybrid links, please go to the webpage AI in Biology & Medicine
The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.
Niklas Dieckow - Data-driven methods for the Maxey-Riley equations
The Maxey-Riley equations (MRE) describe the motion of a small inertial particle suspended in a fluid flow. They are a system of implicit integro-differential equations with a singular kernel. Exact solution methods require the evaluation of an integral over the entire particle history in each time step, causing the computation time to grow quadratically in the number of steps. In this talk, data-driven methods such as SINDy (Sparse Identification of Nonlinear Dynamics) are discussed and employed to obtain approximations of the MRE that do not contain an integral term and are therefore easier to solve.
Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link
The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.
Philip Suskin - Development of a Conversational Interface Based on Institution-Specific Documentation Through LLM Finetuning
All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469
The Helmut Schmidt University/University of the Federal Armed Forces Hamburg is a place of science.
Universität Hamburg
Adeline Scharfenberg
Universität Hamburg
Adeline Scharfenberg
Universität Hamburg
Adeline Scharfenberg