machine learning

An diesem Abend werden sich die Fanny Mendelssohn Artists mit den Möglichkeiten der KI in der Musik auseinandersetzen unter der thematischen Überschrift Vier Jahreszeiten. Mit seinem Werk 4 Seasons for Hasty People stellt Mathias Rehfeldt die klassische Programmmusik auf den Kopf – und die Frage nach Kreativität in Zeiten künstlicher Intelligenz ins Zentrum.

Während Vivaldis berühmter Zyklus der Jahreszeiten rund 45 Minuten dauert, durchläuft Rehfeldt Frühling, Sommer, Herbst und Winter in nur wenigen Minuten – verdichtet, zugespitzt und mit einem Augenzwinkern an eine Gegenwart, in der alles immer schneller gehen muss.

Das Besondere: Das Werk steht im direkten Vergleich zu einer Komposition, die nicht von einem Menschen stammt, sondern von einer künstlichen Intelligenz. Beide Werke werden anonym vom selben Ensemble präsentiert – das Publikum hört und entscheidet: Wer ist Mensch? Wer ist Maschine?

Rehfeldt nutzt dieses Setting nicht nur für ein musikalisches Experiment, sondern für eine Auseinandersetzung mit der künstlerischen Realität der allerneuesten Gegenwart: Was ist Originalität? Was ist Emotion? Und wie verändert sich unser Bild von Kreativität, wenn Maschinen beginnen, Kunst zu produzieren?

Bereits seit 2015 zeichnet der Fanny Mendelssohn Förderpreis junge Klassik Künstler:innen aus – dabei bewertet der international renommierte Preis nicht nur die technische Virtuosität der Bewerber:innen, sondern legt besonderen Wert auf innovative Konzepte und visionäres Gestalten. Den jungen Talenten wird bei ihren musikalischen Projekten absoluter Freiraum gelassen. So zeigt auch das »Festival der Preisträger«, das nun bereits zum siebten Mal im Kleinen Saal der Elbphilharmonie stattfindet, die künstlerische Kreativität und das außerordentliche Talent der Fanny Mendelssohn Artists.

Institutions

• Elbphilharmonie
• Oscar und Vera-Ritter Stiftung
• Fanny Mendelssohn Förderpreis e.V.

Recent advances in Generative AI have given rise to strong emotions among the general public, including excitement, fear, wonder, and disbelief. To be sure, the emergence of Large Language Models (LLMs) marks a significant milestone in the history of AI. But are systems like ChatGPT and Gemini actually intelligent, and are we at the threshold of so-called Artificial General Intelligence (AGI)? This session provides an overview of how LLM-based systems work, where they excel, and where they fall short, with a special emphasis on opportunities for the complementary strengths of humans and machines.

Session Objectives:

By the end of the session, participants will be able to:

• Explain the probabilistic nature of Large Language Models (LLMs) to demystify how they generate responses.
• Differentiate between the capabilities of current Generative AI and the “magical thinking” often associated with Artificial General Intelligence (AGI).
• Identify the operational limitations of AI systems to determine where human intervention and oversight are essential.
• Map complementary strengths of humans and machines to optimize collaboration in their own workflows.

Institutions

• AI for Good

Get to know other researchers working in Image computation and AI within UKE and at institutions around Hamburg.

Our workshop will consist of a mixture of elevator pitches and discussions with interesting formats to create dynamic participation. Please indicate if you are willing to give a short pitch (max 3 mins, 1 slide) of your research topic/interests.

We will have drinks and snacks throughout the afternoon and will end the workshop with pizza and drinks.

Registration: Please write to a.reinicke-vogt@uke.de by May 23^rd with your name, email, research topic, and if you are willing to give a short (3 mins, 1 slide) elevator pitch of your research.

The School of Electrical Engineering, Computer Science and Mathematics at the Hamburg University of Technology (TUHH) is pleased to share another inaugural lecture and research talk from the field of Data Science with the entire TUHH and with the public as part of its colloquium.

Program:

• Welcome
  Prof. Dr.-Ing. Irina Smirnova, Vizepräsidentin Forschung, TUHH
  Prof. Dr.-Ing. Gerhard Bauch, Studiendekan EIM, TUHH
• Inaugural Lecture “From obeying AIs to collaborative agents: a human-centric view“
  Dr. Pierre-Alexandre Murena, Human-Centric Machine Learning Institute, TUHH
• Guest talk “Towards machines that understand people”
  Dr. Andrew Howes, University of Exeter
• Get together
  Foyer, Building II, TUHH Campus

Online participation via Zoom is also possible. You will get the zoom link after registration

Once again, the five platforms Helmholtz.AI, Helmholtz Imaging, HIFIS, HIDA and HMC have teamed up to createa second edition of the Incubator Summer Academy on 18-29 September 2023! We have designed a joint program with a variety course packages covering state of the art Data Science methods and skills, as well as networking opportunities in our Summer Academy Gathertown space!

Ranging from fundamental course packages as for instance “Python”, or “Introduction to Scienctific Metadata” to advanced topics as “Machine Learning Based Image analysis”, the program offers participants to select course packages that best suit their experience levels and interests.

The Incubator Summer Academy is open to all doctoral and postdoctoral researchers in the Helmholtz Association. Additionally, a small number of seats in our workshops are reserved for Master students, doctoral and postdoctoral students from other research institutions and universities.

More detailed information and registration: https://events.hifis.net

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:

Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:
Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is open to students, researchers, and clinicians keen to learn the essentials of machine learning and implementing it via Python. The aim of the course is to provide a comprehensive map of machine learning (and deep learning) methods with no specific background requirements. A little background in python can be helpful, though. We will focus on fundamentals of machine learning, validation methods, linear and nonlinear models, and feature reduction. The students will also get familiarized with the Python packages of Sci-kit Learn and Pytorch. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics
Types of machine learning: supervised and unsupervised
Validation  metrics and cross validation
Introduction to linear and nonlinear models include: Linear regression, Random forest, support vector machines, deep neural networks.
Feature reduction.
Regularization.

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Lecturer: Dr. Fatemeh Hadäghi, Institute of Computational Neuroscience, UKE

Prerequisites A basic understanding of neural networks and machine learning concepts is expected as well as a familiarity with Python and basic programming skills.

Description This workshop is open to students, researchers, and clinicians wanting to learn about recurrent neural networks (RNNs) and their applications in biomedical signal processing. RNNs are vital tools in the field of neural networks, especially known for their capability to manage sequential data. This workshop will provide an accessible introduction to RNNs, concentrating on their core concepts and various applications. We will explore how RNNs excel at capturing temporal dependencies through their unique recurrent connections, making them highly effective for a variety of tasks. Participants can expect to achieve a solid understanding of the basic principles and architecture of RNNs as well as the ability to identify suitable applications for RNNs and implement basic RNN models. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics

• Overview of RNN fundamentals and how they differ from other neural networks
• Key applications of RNNs in biomedical signal processing
• Reservoir computing (RC)
• Hands-on exercises and examples to illustrate RNN implementation and usage

Satellite remote sensing enables a wide range of downstream applications, including habitat mapping, carbon accounting, and strategies for conservation and sustainable land use. However, satellite time series are voluminous and often corrupted, making them challenging to use: the scientific community’s ability to extract actionable insights is often constrained by the scarcity of labelled training datasets and the computational burden of processing temporal data.

The presentation will introduce TESSERA (Time-series Embeddings of Surface Spectra for Earth Representation and Analysis), an open foundation model that preserves spectral-temporal signals in 128-dimensional latent representations at 10-meter resolution globally. The model uses self-supervised learning to summarise petabytes of Earth observation data. TESSERA is shown to be label-efficient and closely matches or outperforms state-of-the-art alternatives. By preserving temporal phenological signals that are typically lost in conventional approaches, TESSERA enables new insights into ecosystem dynamics, agricultural food systems, and environmental change detection. Moreover, the open-source implementation supports reproducibility and extensibility, while the privacy-preserving design allows researchers to maintain data sovereignty. To current knowledge, TESSERA is unprecedented in its ease of use, scale, and accuracy: no other foundation model provides analysis-ready outputs, is open, and delivers global, annual coverage at 10m resolution using only spectral-temporal features at pixel level.

This session is part of a two-session series, providing the theoretical introduction to TESSERA. The second session, a hands-on workshop, will be held on February 2nd, 2026.

Institutions

• AI for Good

Die Veranstaltungsreihe beschäftigt sich in diesem Herbst unter dem Titel KI und Wir mit den rasanten und grundlegenden Veränderungen unserer Gesellschaft durch künstliche Intelligenz.

Vom 9. Oktober bis zum 14. November diskutieren Expert:innen aus Wissenschaft, Politik und Praxis mit dem Hamburger Publikum über das Potenzial von ChatGPT und Co. Wie können die neuen Technologien Medizin und Klimaschutz voranbringen oder auch in der Kunst genutzt werden? Wo kann KI beim Lernen in Schule, Hochschule und darüber hinaus unterstützen? Welche Regulierungen oder Transparenzvorschriften brauchen wir dabei? Um diese und andere Fragen geht es in vielfältigen Formaten von Podiumsdiskussionen über Ausstellungen bis hin zu interaktiven Workshops, die zum

Mitdiskutieren, Dazulernen und Ausprobieren einladen. Die Veranstaltungen finden u.a. an der Uni Hamburg statt. Viele UHH-Mitglieder tragen aktiv zum Programm bei.

Institutions

• UHH
• HIAS
• Körber Stiftung

In the century between the 1870s and the 1970s, hundreds of Malay-language periodicals circulated around the Malay-speaking world. These periodicals chronicle a fascinating era and have been the focus of intense study by scholars such as William Roff and Ian Proudfoot. Many of these periodicals have been digitized, and comprehensive collections are at the National Library of Singapore, as well as in other libraries and archives.
Given the availability and size of the collections, the opportunity is ripe for systematic digital analysis. Projects elsewhere in the world have demonstrated the power of analysing historical newspapers at scale using computational methods. Examples include "Living With Machines" (a partnership between the British Library and several universities in the UK) and "Oceanic Exchanges" (a partnership between Finland, Germany, Mexico, the Netherlands, the United Kingdom, and the United States).

What is holding back a similar study of Malay-language newspapers?
  – The main obstacle is the script. The majority of these periodicals were published in Jawi, an adaptation of the Perso-Arabic script for the Malay language, which poses significant challenges for digital processing. For one thing, typical Optical Character Recognition pipelines (OCR) don't work well for Jawi.
  – Another challenge is that most contemporary Malay readers, including many historians who would be interested in these collections, are less familiar with Jawi than with Rumi (the Romanized version of Malay most commonly used today). The automatic transliteration from Jawi to Rumi is also a complex task, as vowels are often not marked down in Jawi. 
  – In addition to this, spelling conventions have changed, and there are many different approaches for transliterating the same word.

To address these challenges, the "Computational Heritage" research group at the National University of Singapore has developed specialized AI models for both Jawi OCR and Jawi-to-Rumi transliteration. In this talk, I will describe our progress so far, the challenges we still face and the future directions of our work.

Institutions

• Department for Languages and Cultures of Southeast Asia, Asia-Africa-Institute (AAI), UHH

Learning to Optimize: How Machine Learning Drives Decisions at Companies like Uber

Have you ever wondered why and where taxi cars wait, or how smart Uber drivers make their decisions? This talk will delve into these intriguing questions. We will explore the concept of sequential decision-making and policy classes through a hands-on approach. Using taxi services as an example, we learn how service providers can optimize their decision-making. And guess what? There´s not as much magic behind it as you might think...so prepare to be surprised!

Distinguished Lecture Series in Business Analytics & Data Science

The Distinguished Lecture Series in Business Analytics & Data Science at Kühne Logistics University (KLU) presents public lectures on selected topics in analytics, data science, and their applications across various industries, including logistics and supply chain management. Targeted at students, alumni, industry professionals, and other interested individuals, the series offers insights into the latest advancements, trends, and challenges in these fields. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

This talk explores analytic methods that prioritize theory over pure data accumulation. It deep dives into game theory by dissecting the principal-agent model to reveal hidden strategic dynamics. Ultimately, the presentation underscores that thoughtful analysis provides robust decision-making tools where data is scarce. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.

Event free of charge, register here

Distinguished Lecture Series in Business Analytics & Data Science

The Distinguished Lecture Series in Business Analytics & Data Science at Kühne Logistics University (KLU) presents public lectures on selected topics in analytics, data science, and their applications across various industries, including logistics and supply chain management. Targeted at students, alumni, industry professionals, and other interested individuals, the series offers insights into the latest advancements, trends, and challenges in these fields. Attendees gain valuable knowledge on how data-driven decision-making can transform business operations and enhance efficiency in today's digital economy.