Contact

Jake Petersen, opencampus.sh

Description

Together we want to build, code and race a Self-Driving Car equipped with an NVIDIA Jetson Nano.

Dataset

We generate the training data by driving the car via a manual remote control.

How you can contribute

We are looking forward to participants having very limited coding experience to having already experience in programming machine learning models to complete the track in the shortest amount of time.

Contact

Vladimir Alexeev, Digital Artist and OpenAI Ambassador Michael Haas, Freelance Artist

Description

Instead of increasing efficiency through machine learning and doing predictive analytics, we let AI, for example, write poems in the style of Kurt Schwitters and perform them by AI-generated jazz musicians. We can use AI to create previously non-existent people, art movements, and dream sequences, or short films made entirely by AI (script, video, music, and actors).

Dataset

Your memories and fantasies. :-)

How you can contribute

We look forward to participants who are interested in combining artificial intelligence and creativity.

Contact

Christian Mayer, University of Mannheim

Description

In learning contexts it is crucial to provide fast feedback to the learner. Therefore, as a first step, we will categorize open text responses provided on complex problem solving tasks according to the quality and correctness of the arguments used. Later we want to use these to automatically provide appropriate feedback.

Dataset

About 1,800 open text responses from students.

How you can contribute

We are happy for anyone to join our project. You should have a strong interest in how to automate the scoring or classification of open-ended text responses. Basic programming knowledge would be good. You are also welcome to use our data for a fine-tuning approach to the classification.

Contact

Yiqing Wang, University of Kiel Dustin Hanke, University of Kiel

Description

Transposable elements (TE) are frequent genomic sequences that can change their position ineffectively in the genome by copy & paste mechanisms. During this process they are often dragging essential or beneficial genes like antibiotic resistance genes to another location. Sometimes characteristics of TEs are missing or TEs are defragmented, which makes the identification challenging. In this project we are approaching to use the gene frequency landscape of genomic sequences to detect putative TE regions utilizing methods like LSTMs or transformer models.

Dataset

We provide a protein family frequency dataset of 9226 genomic sequences originating from Klebsiella, Escherichia, and Salmonella.

How you can contribute

We are happy about anyone to join, who is interested in understanding the fundamentals of transposable elements and wants to push our approach. Especially, we would be glad about participants having a strong technical background in applying LSTMs and/or Transformer models. Experience in adjusting, improving and building complex ML models would be beneficial.

From June 7 to 10, 2022, and with support of the State Chancellery of Schleswig-Holstein, the Waterkant Festival and Kiel.AI will bring together machine learning and AI enthusiasts to push their machine learning projects to the next level or to get expertise by joining others working on their projects - and all this in the unique atmosphere of the Waterkant Festival!

Register for the event HERE! Under the same link you also find more information on the participation.

The following projects are already participating in the event:

• (Jake Petersen, opencampus.sh)

• (Vladimir Alexeev, OpenAI Embassador; Michael Haas, Freelance Digital Artist)

• (Yiqing Wang and Dustin Hanke, University of Kiel)

• (Christian Mayer, University of Mannheim)

• (Simon Van der Wulp and Juan Veliz, north.io)

• (Jonas Mielck, University of Kiel and stackOcean GmbH)

• (Thorben Jansen and Nils-Jonathan Schaller, IPN Kiel)

• (Fabian Zehner and Nico Andersen, DIPF Frankfurt)

• (Niklas Koser and Claus Glüer, University of Kiel)

• (Lennart Büttner, Sourceboat GmbH & Co. KG)

• (Lena Krüger, Muthesius University of Fine Arts and Design)

Contact

Simon Van der Wulp, north.io Juan Veliz, north.io

Description

German (and international) coastal waters are scattered with munition, from the two world wars. To deal with these munitions and prevent toxic chemical from entering the water marine ecosystem, we need to find, identify and remediate.

The first step deals with the past, mining information available from historical documents to learn where, what and how many munitions could be found, to make the search as effective as possible. This included documents describing munition dumping, sea-battles or mine laying activities that occurred during and in post-war periods.

Using scans of historical text documents, we want to retrieve the positions of ships loaded with munitions that were dumped after World War II. Currently a Neural Net is, in a first step, used to improve the image quality for the OCR scans and, in a second step, a further net is used to identify and extract relevant text information.

Dataset

We have a training data set consisting of ~1100 scans; it includes the original scans, a corresponding set of scans with manually improved image quality and a corresponding set of labelled text element for each scan.

How you can contribute

We are looking forward to participants who want to support us in improving our models and applying new models that have been published in recent times.

Presentation Slides:

Link to the Hugging Face course:

Contact

Lennart Büttner, Sourceboat GmbH & Co. KG

Description

We are currently developing a database interaction & creation tool for users without a programming background. In this context we want to evaluate the possibilites to use NLP to generate queries from natural languages, especially German and English.

Ideas reach from a chatbot that simply answers questions about your data, e.g. “How many paying customers do we have” over the generation of endpoints in ensemble with a functions as a service provider up to schema creation.

At this very first phase of experimentation and ideation we want to estimate scope and requirements of this project. Primary challenges to overcome are the dynamic generation of data based on a database schema as well as the tokenization and handling of Out-of-Vocabluary words.

Dataset

We started using databases in different languages from to generate data based on table and column names of a database schema.

How you can contribute

I am looking forward to discuss techniques and exchange ideas with everyone who is interested.

Contact

Jonas Mielck, University of Kiel / stackOcean GmbH

Here the Link to the Streamlit Docker Example:

If you have any questions, feel free to contact Matthias under mail@matthiasnannt.com

Contact

Lena Krüger, Muthesius University of Fine Arts and Design

Description

Language and linguistic contexts are to be visualized multidimensionally on the basis of geometries generated by language algorithms. This project’s aim is to explore how the algorithm can be used creatively and how its output can be visualized.

Dataset

Your creativity and fantasy. :-)

How you can contribute

I look forward to participants who are interested in combining artificial intelligence and creativity.

Contact

Niklas Koser, University of Kiel Claus Glüer, University of Kiel

Description

The Intelligent Imaging Lab (i2Lab) of the Section Biomedical Imaging, Department of Radiology and Neuroradiology, UKSH, and CAU Kiel is developing innovative artificial intelligence methods for radiological imaging. Here we address super-resolution imaging. This AI approach may allow to improve the image quality (without increasing the radiation dose) or reduce the radiation dose (at the same image quality).

In the challenge, participating teams will use a pretrained Artificial Neural Network to segment the bronchial tree and try to program a super-resolution approach for improving image quality to improve the segmentation result.

Dataset

60 lung CT images and segmentation mask from public repositories will be provided by us (40 for training/validation and 20 for testing)

How you can contribute

Single competitors or teams with some experience in building ML models are invited to participate. Experience in PyTorch or TensorFlow would be beneficial.

Contact

Thorben Jansen, IPN Kiel Nils-Jonathan Schaller, IPN Kiel

Contact

Fabian Zehner, DIPF Frankfurt Nico Andersem, DIPF Frankfurt

Description

When test persons respond to test questions with natural language, the responses typically need to be scored by humans. We use natural language processing techniques and embeddings to represent word semantics for grouping (i.e., clustering) and classifying responses. This pipeline is available via the R-based Shiny app of (shinyReCoR) through a graphical user interface to people without knowledge of how to use NLP or ML coding frameworks. Therefore, our app focuses on making the classification process transparent and offers many interactive ways to diagnose resulting models/classifiers, among others through visualizing the semantic space.

At the Coding.Waterkant, we plan to develop two new diagnostic and visualization features.

Literature Andersen, N., & Zehner, F. (2021). shinyReCoR: A Shiny Application for Automatically Coding Text Responses Using R. Psych, 3(3), 422–446. doi: 10.3390/psych3030030 Zehner, F., Sälzer, C., & Goldhammer, F. (2016). Automatic Coding of Short Text Responses via Clustering in Educational Assessment. Educational and Psychological Measurement, 76(2), 280–303. doi: 10.1177/0013164415590022

Dataset

We use a publically released demo data set from ReCo with about 4,000 text responses to a demo item for demonstration purposes. For internal purposes, we work with confidential text responses from the PISA assessment.

How you can contribute

We are happy to exchange ideas on text classification and how to provide easy access to current NLP and ML methods.