machine learning
Events
Public Lecture Series: Taming the Machines. Misinformation
For better or worse, the revolutionary potential of AI has reached public consciousness, with a growing recognition of the ways that AI might change how we live and work together. Indeed, the fabric of society is already changing in front of our very eyes, with powerful profiteers of AI rallying behind its supposed inevitability. The AI revolution is afoot and it seems as if there is nothing that we can do about it. However, Donald Trump’s emerging alliance with Silicon Valley’s “Magnificent Seven” provides a potent reason for pause and for sustained reflection on the path we are collectively treading.
To discuss how AI, like any other technology, is part of a societal process of struggle, negotiation, and cooperation, this lecture series brings together experts from philosophy, law, and cognitive science. How are technologies like AI grounded in social processes of knowledge production, design, and innovation? What is the environmental impact of AI systems and what ecological responsibilities fall to providers, politicians, and users? What is the human rights impact of AI technologies deployed in military and security contexts? And what, to speak with Nietzsche, renders AI ‘all too human’ after all?
Join us at our “Taming the Machine” lecture series this summer term to explore with our distinguished guests these and other related questions. To get the latest updates and details how to attend the lectures, please visit http://uhh.de/inf-eit.
Institutions
- UHH
Public Lecture Series: Taming the Machines. Repairing AI for Environmental Justice
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).This lecture series brings together perspectives from ethics, politics, law, geography, and media studies to assess the potential for preserving and developing human values in the design, dissemination, and application of AI technologies. How does AI challenge our most fundamental social, political, and economic institutions? How can we bolster (or even improve) them in times of technological disruption? What regulations are needed to render AI environments fairer and more transparent? What needs to be done to make them more sustainable? In what sense could (and even should) we hold AI accountable?
To explore these and other related questions, this public lecture series invites distinguished international researchers to present and discuss their work. To get the latest updates and details how to attend the lectures, please visit http://uhh.de/inf-eit.
Prof. Dr. Aimee van Wynsberghe, Rheinische Friedrich-Wilhelms-Universität Bonn, D
Institutions
- UHH
Public Lecture Series: Taming the Machines. Repairing AI for Environmental Justice
Aimee van Wynsberghe is the Alexander von Humboldt Professor for Applied Ethics of Artificial Intelligence at the University of Bonn in Germany. Aimee is director of the Institute for Science and Ethics and the Bonn Sustainable AI lab. She is co-director of the Foundation for Responsible Robotics and a member of the European Commission's High-Level Expert Group on AI. In each of her roles, Aimee works to uncover the ethical risks associated with emerging robotics and AI. Aimee’s current research, funded by the Alexander von Humboldt Foundation, brings attention to the sustainability of AI by studying the hidden environmental costs of developing and using AI.
Institutions
- UHH
Public Lecture Series: Taming the Machines. Risk Ethics and Big Tech Business
Prof. Dr. Sven Ove Hansson (Uppsala University, SE)
About the lecture
tbd
Institutions- UHH
Public Lecture Series: Taming the Machines. Talking to Myself With AI: From Self-Knowledge to Solitude
Dr. Lucy Osler (University of Exeter, UK)
AI in its many forms is often presented as a driver of “progress”: improving lives, accelerating solutions, and expanding human possibilities. This talk offers a critical framework for assessing such claims. Drawing on a pragmatist understanding of progress, it proposes that genuine progress consists in removing entrenched obstacles to human flourishing – especially where deprivation, exclusion, and domination persist.
Against this standard, I examine how and why AI’s most celebrated promises often misfire. First, the political economy of AI entails massive opportunity costs: While severe deprivation remains cheaply preventable, extraordinary resources are channelled into ever more powerful IT systems. Second, “sustainable AI” narratives often function as a reputational alibi rather than meeting defensible threshold standards of sustainability. Third, some of the most ambitious AI imaginaries carry troubling assumptions about authority and hierarchy, about who decides and who counts.
The critical conclusion is not anti-technology, but firmly pro-justice. It is imperative to resist any potential hypes, to ask critical questions, and to accept responsibility for just regulation and reform as a shared political task. Furthermore, genuine progress needs to begin by taking seriously those at the margins.
- UHH
Public Lecture Series: Taming the Machines. The Future of Prediction. Algorithmic Forecast in Science and Society
This lecture series invites viewpoints from different relevant disciplines to explore how we can preserve and advance human values through the development and use of AI technologies. Key questions include: How does AI impact our fundamental social, political, and economic structures? What does it mean to lead a meaningful life in the AI age? What design and regulatory decisions should we make to ensure digital transformations are fair and sustainable?
Speaker: Prof. Dr. Elena Esposito, Universität Bielefeld, DE
Institutions
- UHH
Public Lecture Series: Taming the Machines. The Influence of AI on Democracy
Prof. Dr. Tilo Wesche, Carl von Ossietzky Universität Oldenburg, DE
Artificial intelligence is rapidly becoming a structuring force in contemporary life. From scientific research and public administration to everyday communication and self-understanding, AI systems shape how we act, decide, and relate to one another. Yet their rapid diffusion raises urgent philosophical and political questions: What kind of progress does AI promise and for whom? How do algorithmic systems transform responsibility, agency, and justice? Who is likely to suffer from the watchful eye of AI systems? Can democratic societies meaningfully govern technologies that increasingly govern them?
This semester of Taming the Machines explores these questions from interdisciplinary perspectives in philosophy, political theory, and science and technology studies. We invite you to reflect with us on AI as a site of power and normativity, and examine its role in economic and political ordering, surveillance and security, knowledge production, and the formation of subjectivity. And also to considers more intimate dimensions, such has how interactions with such systems might reshape self-knowledge, dialogue, creativity, and even solitude.
Institutions- UHH
Public Lecture Series: Taming the Machines. Uneven Datafication: Political Economy of Digital Colonial Capitalism
Prof. Dr. Azadeh Akbari, Goethe-Universität Frankfurt, DE
This paper develops the concept of uneven datafication, drawing on literature on coloniality, uneven development, and dependency theory. Uneven datafication refers to uneven development in the contemporary political economy of data, showing how global cycles of differentiation and totalisation perpetuate inequality to sustain capitalist structures. Datafication is neither homogeneous nor universal, but marked by colonial continuities, spatial differentiation, and temporal unevenness. Uneven datafication operates through three interrelated dynamics. First, territorialisation, deterritorialisation, and reterritorialisation produce uneven geographies of digital colonial capitalism, from datafied bodies to platform infrastructures and space-based data centres. Second, dispossession enacts spatial, temporal, and dehumanising violence, ranking populations as more or less valuable and enforcing biopower ‘within’ and necropower ‘beyond’. Third, unequal exchange sustains asymmetrical valuation and circulation of data and data labour, enabling Big Tech and core economies to extract surplus value from peripheral regions.
Uneven datafication thus sustains colonial capitalist accumulation through differentiated dispossession and dependency across populations, spaces, and classes.
- UHH
Public Lecture Series: Taming the Machines. What, if anything, are convivial technologies?
Prof. Dr. Darian Meacham (Maastricht University, NL)
This talk will focus on how this distinction between convivial and monopolistic (or non-convivial) technologies can support a critical phenomenology of technology. I will argue that Gorz attempts to do just this, but that his development of the “convivial – un-convivial” distinction in terms of a broader account of “autonomy” vs “heteronomy” would benefit from a more phenomenologically grounded account of autonomy. I will pose (and try to address) the question of whether a more embodied account of autonomy, such as developed within the context of enactive approaches to cognition would serve such an aim.
A third step will be to ask if and how an enactively enriched notion of autonomy, when situated within the critique of technology and economic rationality, can contribute to the development of programmes for “concrete utopias”.
Institutions
- UHH
Remote sensing and machine learning for environmental monitoring: Opportunities and challenges
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:
- Introduction to PDFM Embeddings: Delve into the model architecture of PDFM and discover how aggregated data (such as search trends, busyness levels, and weather conditions) generates location-specific embeddings.
- Data Preparation: Learn to integrate ground truth data, including health statistics and socioeconomic indicators, with PDFM Embeddings at the postal code or county level.
- Hands-On Exercises: Engage with interactive Colab notebooks to explore real-world applications, such as predicting housing prices using Zillow data and nighttime light predictions with Google Earth Engine data.
- Visualization and Interpretation: Analyze and visualize geospatial predictions and PDFM features in 3D, enhancing your ability to interpret complex datasets.
By the end of this workshop, participants will have a strong foundation in utilizing PDFM Embeddings to address real-world geospatial challenges.
Institution
- AI for Good
Risks and solutions when using non-stationary training data in earth system science Online Discovery - AI for Earth and Sustainability Science
Earth system science increasingly relies on machine learning to analyze complex, multivariate, and spatiotemporal data. However, the validity of these models critically depends on the assumption that training and deployment data share similar statistical properties – a condition often violated in real-world environmental applications. This presentation addresses the risks associated with non-stationary training data distributions, arising from climate change, evolving land use, or sensor shifts over time. We show how such distribution shifts can lead to degraded model performance, biased predictions, and misleading scientific conclusions. Through different examples, we illustrate the mechanisms and consequences of non-stationarity. We then discuss methodological solutions, including domain adaptation, continual learning, and uncertainty quantification techniques, that help mitigate these effects and improve model robustness. By combining insights from machine learning and earth system science, this talk aims to foster awareness of distributional risks and promote the development of adaptive, interpretable, and trustworthy models for understanding and predicting Earth’s dynamic systems.
Institutions
- AI for Good
Seminar über kausales maschinelles Lernen
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
- Fakultät für Betriebswirtschaft, Lehrstuhl für Statistik
Series Computation & Data
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.
Special Physics/AI Seminar: Distribution mapping with Schrödinger Bridges
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
- UHH, Institut für Experimentalphysik
Systems Biology of Cancer - BNITM seminar series "AI in Biology and 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.
Angela Relógio, Medical School Hamburg MSH
For further details and hybrid links, please go to the webpage AI in Biology & Medicine
Technical foundations of Large Language Models, their applications and limitations - BNITM seminar series "AI in Biology and 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
Train Your Engineering - Data-driven methods for the Maxey-Riley equations
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
Train Your Engineering - Development of a Conversational Interface Based on Institution-Specific Documentation Through LLM Finetuning
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
People
Timo Gerkmann
Institutions
The Helmut Schmidt University (HSU)
The Helmut Schmidt University/University of the Federal Armed Forces Hamburg is a place of science.
Universität Hamburg
Adeline Scharfenberg