research data

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

Um Methoden aus dem Bereich der Künstlichen Intelligenz adäquat in der Forschung anwenden zu können, ist es erforderlich, sie in ihren Grundzügen zu verstehen und idealerweise aktiv (spielerisch) zu erproben.

• Welche Verfahren gibt es überhaupt und wie funktionieren sie technisch?
• Wo kommen sie im Akademienprogramm bereits zum Einsatz, wie können Forschende voneinander lernen und Synergien nutzen?
• Profitieren die Geisteswissenschaften nur von den neueren Entwicklungen im Bereich Generativer KI oder leisten sie auch einen Beitrag zur KI-Entwicklung?

Die Themenkonferenz, die von der Akademie der Wissenschaften in Hamburg und der Niedersächsische Akademie der Wissenschaften zu Göttingen ausgerichtet wird, möchte sich diesem Spannungsfeld widmen. Teilnehmende haben die Möglichkeit, die Potenziale neuer Methoden für die Analyse und Aufbereitung digitaler Ressourcen, speziell im Akademienprogramm, gemeinsam zu erörtern und zu erproben.

Um einen Überblick zur (auch geplanten) Verwendung von KI-Methoden zu geben, laden wir Angehörige der Unionsakademien und Langzeitvorhaben im Akademienprogramm herzlich ein, einen Impuls-/Kurzvortrag oder einen Vorschlag für eine Hands-On-Session oder Systemdemonstration beizusteuern (s. vorläufiges Programm).

Vorschläge (i.d.R. nicht mehr als zwei pro Akademie) können bis zum 30. Juni 2024 formlos an ki@akademie-der-wissenschaften-in-hamburg.de gesendet werden. Es ist vorgesehen, die Foliensätze der Beiträge auf Zenodo zu veröffentlichen.

Vorläufiges Programm hier

On September, 25th to 27th, 2023 the third MLE Days on machine learning in engineering will take place on the campus of the Hamburg University of Technology (TUHH). This year, it combines a summer school with a one-day startup-challenge. Participation is free of charge.

The Summer School teaches insights into the world of machine learning with a focus on engineering. It provides sessions about fundamentals of machine learning, concrete application examples, as well as hands-on sessions to try out and consolidate lessons learned. Keynote talks complete the program. Three parallel tracks are provided to allow participants to choose contributions adapted to their interest and machine learning experience. The use cases range from sensor and image processing, to electrical engineering and materials science, to aviation and maritime logistics. A poster session and an elevator pitch event allow participants to present their own work on machine learning topics. The best posters and pitches will be selected by a jury and awarded prizes. A networking event allows attendees to establish contacts with selected corporate partners and sponsors from start-ups and medium-sized businesses to large corporations. In the startup-challenge attendees learn how to turn machine learning ideas into a business.

The MLE Days are organized by the Machine Learning in Engineering research initiative of the TUHH (MLE@TUHH) in collaboration with the Helmholtz Center Hereon, DASHH, and the Career Center of TUHH, the AI.Startup.Hub, and AI.HAMBURG. The MLE initiative joins the competencies in the field of machine learning at the Hamburg University of Technology with the goal of transferring knowledge towards business and industry. Students, PhD students, postdocs, and professors from all disciplines of the TUHH are engaged together with colleagues from the Helmholtz Center Hereon to make methods and applications of machine learning known, to network, and to foster scientific exchange.

Join us to discover the new applications of machine learning in engineering practice!

Institutions

• Hamburg University of Technology (TUHH) 

The Machine Learning in Engineering (MLE) initiative integrates competencies and activities in the field of machine learning at Hamburg University of Technology (TUHH) and partner organizations from the Hamburg metropolitan area. Within the Machine Learning in Engineering (MLE) initiative, students, doctoral candidates, postdocs, researchers, and professors from all departments at the TUHH and from the partners are working interdisciplinary together. The aim is to conduct fundamental research particularly relevant for the development of new technology, thereby contributing to the digital transformation of the engineering sciences. In addition to basic research, the initiative aims at transferring knowledge to business and industry. Among a number of instruments for this kind of transfer, the annually organised MLE days provide a natural opportunity for education and knowledge exchange.

Registration: If you want to attend, please, send an email to Ulrike Schneider (in cc) with your name and affiliation. The registration is open until 01.03.2025. The number of participants is limited by the size of the lecture hall. For guaranteeing your participation and to make planning (particularly of the coffee breaks) easier, please register as soon as possible. Your data will only be used for organizing the MLE Days.

Program (preliminary):
09:00-15:00: Presentations by MLE-members and partners following the updated MLE structure here   
15:00-17:00: Poster & Networking session

Catering: There will be a coffee break in the morning and afternoon with hot drinks and snacks. Lunch in the canteen is to be paid for by the participants themselves.

Poster in English until 14.02.2025.

The organizing committee reserves the right to reject registrations and poster contributions. If you have any questions, please do not hesitate to contact the organizing team via mle@tuhh.de .

Institutions

• Hamburg University of Technology (TUHH) 

Professor Bast completed her Ph.D. and Habilitation at the MPI for Informatics in Saarbrücken. She is a professor for computer science at the University of Freiburg since 2009. Her research focus is on all kinds of intelligent search in large data sets, with and without AI. The search technology developed in her group is used worldwide on a daily basis. During an extended research stay at Google, she developed a new public transport route planner for Google Maps. She has served as Dean of the Faculty of Engineering, and is still serving as Dean of Studies and Senator of the university. She was a member of the German Bundestag's Enquete Commission on Artificial Intelligence. She is a member of the committee that is responsible for the evaluation of the German Leibniz Institutes. She has received various research and teaching awards.

She is going to talk about QLever, which is a new SPARQL engine and triple store developed by her group, with several unique features. Notably, QLever is very fast, can handle hundreds of billions of triples on a standard PC, supports the incremental construction of SPARQL queries via context-sensitive autocompletion, supports SPARQL+Text queries on text linked to a knowledge graph, and supports very fast spatial joins on very large numbers of geometric objects as well as the visualization of very many such objects on a map. She will show many examples and demos, some performance comparisons with other engines, and will also take a look at what's under the hood. If time permits, she might also talk about the automatic translation of natural language questions to SPARQL queries via large language models, and about entity linking . Also a word or two about the rewards and challenges of writing and maintaining software that actually works in academia.

Institutions

• Leuphana Universität Lüneburg

In the digital transformation of science, research data is an essential basis of the research process and as diverse as the subject cultures in which they are created and used. In data-based or data-driven research, computational methods can be used to scale and transform the research process.
As a scientist, you should store your research data in a secure technical environment using established standards and compliance with data protection regulations. When using computer-aided methods - from statistics to artificial intelligence - and in interdisciplinary research and use of innovative digital methods, you need advice from an expert.
What does this mean in concrete terms for scientific practice? The experts give concrete tips and answer your questions. In addition, the participants have the opportunity to exchange perspectives, challenges and best practice examples and to be inspired.
All interested parties are invited to find out more about the services of the Center for Sustainable Research Data Management (CRDM) and - this time also present - the House of Computing and Data Science (HCDS) at Universität Hamburg from 12 p.m.
The information event is open to the university. Registration is free. The event will take place in German.