The 2023 joint scientific retreat of the EAI Center & Computational Systems Genomics Group took place in Burghausen, Germany.
In November 2022, an EAI scientific retreat took place in Gent, Belgium with Students and researchers from all associated projects. Hereby, new ideas and cooperations were brought out during project presentations by means of posters. Special thanks go to the guest speakers Dr. Pietro Verzelli, Dr. Nicolo Alagna and Max Sprang who sparked the scientific discourse with interesting presentations on the topic of machine learning. And of course, besides scientific exchange and interesting talks, the enjoyment of the picturesque city was not neglected. We'd like to thank the Carl Zeiss foundation for funding this special event.
The Emergent AI Annual Meeting 2022 took place from September 27-28, 2022 at the Johannes Gutenberg-University of Mainz. Students and researchers from all associated projects presented their work. Numerous exciting discussions once again demonstrated the diversity and necessity of understanding emergent artificial intelligence and fostered novel ideas.
We'd like to thank the Carl Zeiss foundation for funding, and especially acknowledge Mr. Findeisen for taking the time to attend this event. Furthermore, we are grateful to the Institute of Molecular Biology for the accommodation.
The 2021 joint Summer School of the BINARY and EAI Project will take place from Monday, September 06 – Thursday, September 09 2021 as a pure virtual, online event (access codes will be provided upon registration).
Full information can be found here.
Neuromorphic computing is a modern approach to computation that has attracted lots of interest from the research community. It uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Researchers from the Emergent AI Center, in collaboration with groups in France, USA, and Japan have published a review on the subject, exploring spintronics-based implementations of neuromorphic computing tasks as well as discussing the challenges that exist in scaling up these systems.
Read the original Publication here: Neuromorphic spintronics
New algorithm solves complex problems more easily and more accurately on a personal computer while requiring less processing power than a supercomputer
The exponential growth in computer processing power seen over the past 60 years may soon come to a halt. Complex systems such as those used in weather forecast, for example, require high computing capacities, but the costs for running supercomputers to process large quantities of data can become a limiting factor. Researchers at Johannes Gutenberg University Mainz (JGU) and Università della Svizzera italiana (USI) in Lugano in Switzerland have recently unveiled an algorithm that can solve complex problems with remarkable facility – even on a personal computer.
The Emergent AI Center welcomes the following new projects:
- Neural network-based approaches for multiscale modelling of topological defects
Prof. Dr. Friederike Schmid, Dr. Karin Everschor-Sitte
- Machine learning prediction of Odorant Receptor (OR)-ligand interactions: Chemical communication as the basis of swarm intelligence
Prof. Dr. Miguel Andrade, Prof. Dr. Susanne Foitzik
- Computational performance of autistic-like networks
Prof. Dr. Martin Heine, Dr. Arthur F Bikbaev, Jun-Prof. Dr. Susanne Gerber
- How emergent is the Brain?
Prof. Dr. Heiko Luhmann, Jun-Prof. Dr. Maik Stüttgen, Prof. Dr. Illia Horenko
- Application of machine learning algorithms to search for dark-matter signals in spectroscopic data
Prof. Dr. Dmitry Budker, Prof. Dr. Stefan Kramer
- Deep Learning of natural selection in population genomic data
Dr. Yoan Diekmann, Prof. Dr. Joachim Burger
- Repetitive DNA and the ‘C-value enigma’ of genomics
Prof. Dr. Thomas Hankeln, Prof. Dr. Miguel Andrade
- Learned Structures for Big Data in Bioinformatics
Prof. Dr. Bertil Schmidt, Prof. Dr. Thomas Hankeln, Prof. Dr. Andreas Hildebrandt
For more information on the individual projects, please take a look at the research part of the website
On December 9th, 2019 Philipp Slusallek, Scientific Director at the German Research Center for Artificial Intelligence (DFKI), presented a seminar at the Johannes Gutenberg-University Mainz. Continue reading "Seminar on Training and Validating AI Systems by Prof. Dr.-Ing. Philipp Slusallek"
On June 4th of 2019 Prof. Dr. Herbert Jaeger, pioneer in the field of Reservoir Computing, presented a seminar at the Johannes Gutenberg University Mainz (JGU). Reservoir Computing is an alternative machine learning approach for Recurrent Neural Networks which is in many ways complementary to deep learning. The talk gave an introduction to the basic principles and variants of Reservoir Computing. Some illustrative examples were presented.