July 2022 marked the start of our 10th Network Science & AI Lab at the…
In July 2021 our 9th Network Science & AI lab took place which originally started in 2013.
Due to the Corona situation we had to limit participation to 4 students this year focusing on three major building blocks:
- Network Science
- Narrow Artificial Intelligence
- Ecosystem Intelligence
After a thorough introduction to the Network Science/Analysis space we focused on the main areas of narrow AI, particularly:
- Machine Learning (supervised, unsupervised, reinforcement, and semi-supervised learning)
- Natural Language Processing (machine search, machine translation, text geneeration, information extraction, sentiment analysis and others)
- Robotics (including all kinds of sensor technologies to sense the world, the processing part of information, and state of the art innovation)
We hereby particularly looked at the role of graph neural networks, deep neural networks, and convolutional neural networks. As in previous years the Lab team members not only got a solid overview but also the opportunity to apply their knowledge in building an Ecosystem Intelligence approach from modeling the network of an industry, the global agricultural machinery industry among others, to using the technology required for indexation and search, including all the relevant steps which need to be considered as part of the human-machine collaboration. We also focused considerably on the data related issues which need to be considered to come up with highly relevant, and significant results.
Two of our students are going back to Frankfurt International School, www.fis.edu, to resume their studies in 11 and 12th grade while the other two join The University of Edinburgh and Stanford University. As in previous years it was a very rewarding program, with high profile students, and we look forward to engaging with our lab members in the future again, including the ones which had joined us initially in 2013. The Network Science & AI Lab was established in 2013 with the vision to educate young people about network science and its crucial role nowadays in narrow AI, and also identify future colleagues.