BEGIN:VCALENDAR BEGIN:VEVENT SUMMARY:Welcome DTSTART;TZID=Europe/Berlin:20230629T093000 DTEND;TZID=Europe/Berlin:20230629T094500 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Digital transformation of organisations or processes through AI DTSTART;TZID=Europe/Berlin:20230629T110000 DTEND;TZID=Europe/Berlin:20230629T123000 DESCRIPTION:The contributions to the session will highlight opportunities for the digital transformation of organizations and processes through the use of AI. The possible applications are diverse and range from the optimi zation of workflows and efficiency increases to the development of innovat ive business models based on AI methods. Specific real-world examples will be presented and discussed on how AI-based solutions can be used in diffe rent industries. LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Opportunities of AI for society DTSTART;TZID=Europe/Berlin:20230629T133000 DTEND;TZID=Europe/Berlin:20230629T150000 DESCRIPTION:This session will highlight the positive impact and opportunit ies of AI on society. The presented papers show how AI-based technologies can contribute to solving societal challenges\, e.g. in law enforcement. E thical aspects will be discussed and questions about responsibility and tr ansparency in dealing with AI will be raised. The session will provide spa ce for a broad dialogue on the impact of AI on society and possible strate gies to make the most of the benefits of AI. LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Classification of static poses based on key point detection for ap plication of incriminated image files DTSTART;TZID=Europe/Berlin:20230629T133000 DTEND;TZID=Europe/Berlin:20230629T140000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Human Centered Implementation Process of AI in SMEs – Conditions for Success DTSTART;TZID=Europe/Berlin:20230629T140000 DTEND;TZID=Europe/Berlin:20230629T143000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Jusbrasil - AI challenges for the legal domain DTSTART;TZID=Europe/Berlin:20230629T143000 DTEND;TZID=Europe/Berlin:20230629T150000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Presentation of the program slots DTSTART;TZID=Europe/Berlin:20230629T093000 DTEND;TZID=Europe/Berlin:20230629T094500 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Shaping the future with artificial intelligence? On the role of AI in resilience and sustainability. DTSTART;TZID=Europe/Berlin:20230629T094500 DTEND;TZID=Europe/Berlin:20230629T103000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Developing a human-centred AI-based system to assist sorting laund ry DTSTART;TZID=Europe/Berlin:20230629T110000 DTEND;TZID=Europe/Berlin:20230629T113000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:AI-Powered Knowledge and Expertise Mining in Healthcare from a Fie ld Experiment DTSTART;TZID=Europe/Berlin:20230629T113000 DTEND;TZID=Europe/Berlin:20230629T120000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Iterative Development of a Process-Oriented Approach for the Selec tion of Platform-Based Digital Services DTSTART;TZID=Europe/Berlin:20230629T120000 DTEND;TZID=Europe/Berlin:20230629T123000 LOCATION:Ratsplenarsaal END:VEVENT BEGIN:VEVENT SUMMARY:Admission DTSTART;TZID=Europe/Berlin:20230629T083000 DTEND;TZID=Europe/Berlin:20230629T093000 LOCATION:Wandelhalle END:VEVENT BEGIN:VEVENT SUMMARY:Coffee break DTSTART;TZID=Europe/Berlin:20230629T103000 DTEND;TZID=Europe/Berlin:20230629T110000 DESCRIPTION:Posters and Stands LOCATION:Wandelhalle END:VEVENT BEGIN:VEVENT SUMMARY:Lunch break DTSTART;TZID=Europe/Berlin:20230629T123000 DTEND;TZID=Europe/Berlin:20230629T133000 LOCATION:Wandelhalle END:VEVENT BEGIN:VEVENT SUMMARY:Coffee break DTSTART;TZID=Europe/Berlin:20230629T150000 DTEND;TZID=Europe/Berlin:20230629T153000 LOCATION:Wandelhalle END:VEVENT BEGIN:VEVENT SUMMARY:Alexander Thamm Training DTSTART;TZID=Europe/Berlin:20230629T153000 DTEND;TZID=Europe/Berlin:20230629T180000 DESCRIPTION:In the training\, the creation of a synthetic dataset suitable for the use case will be explained and presented. Using the already prepa red images\, the participants will then implement and train Convolutional Neural Networks (CNNs) that recognize the extent of graffiti in relation t o the image area. The learning goal is to develop a sense of the strengths and weaknesses of neural networks in image analysis\, in addition to "han ds-on" testing of CNNs including tuning of hyperparameters. This will be d one within the classic data science life cycle of data pre-processing\, mo del implementation\, model evaluation\, and error analysis. Prerequisite f or participation is rudimentary Python knowledge - or experience with othe r programming languages. LOCATION:Room 259 END:VEVENT BEGIN:VEVENT SUMMARY:ScaDS.AI Training on the theoretical foundations of AI/ML DTSTART;TZID=Europe/Berlin:20230629T090000 DTEND;TZID=Europe/Berlin:20230629T103000 DESCRIPTION:The training is aimed at those who want to take their first st eps in Artificial Intelligence (AI) / Machine Learning (ML) and get to kno w the corresponding application possibilities. During the training\, basic principles of AI/ML as well as various methods of supervised and unsuperv ised ML are taught\, as well as the procedure for training and evaluating ML models. Finally\, what has been learned will be applied to an example u sing the Python programming language and corresponding libraries (includin g Pandas\, Scikit-learn).\nNo prerequisites necessary. LOCATION:Room 259 END:VEVENT BEGIN:VEVENT SUMMARY:ScaDS.AI Training on the theoretical foundations of AI/ML DTSTART;TZID=Europe/Berlin:20230629T110000 DTEND;TZID=Europe/Berlin:20230629T123000 DESCRIPTION:The training is aimed at those who want to take their first st eps in Artificial Intelligence (AI) / Machine Learning (ML) and get to kno w the corresponding application possibilities. During the training\, basic principles of AI/ML as well as various methods of supervised and unsuperv ised ML are taught\, as well as the procedure for training and evaluating ML models. Finally\, what has been learned will be applied to an example u sing the Python programming language and corresponding libraries (includin g Pandas\, Scikit-learn).\nNo prerequisites necessary. LOCATION:Room 259 END:VEVENT BEGIN:VEVENT SUMMARY:Alexander Thamm Training DTSTART;TZID=Europe/Berlin:20230629T133000 DTEND;TZID=Europe/Berlin:20230629T150000 DESCRIPTION:In the training\, the creation of a synthetic dataset suitable for the use case will be explained and presented. Using the already prepa red images\, the participants will then implement and train Convolutional Neural Networks (CNNs) that recognize the extent of graffiti in relation t o the image area. The learning goal is to develop a sense of the strengths and weaknesses of neural networks in image analysis\, in addition to "han ds-on" testing of CNNs including tuning of hyperparameters. This will be d one within the classic data science life cycle of data pre-processing\, mo del implementation\, model evaluation\, and error analysis. Prerequisite f or participation is rudimentary Python knowledge - or experience with othe r programming languages. LOCATION:Room 259 END:VEVENT END:VCALENDAR